├── createPeruCSV.R
├── createHaitiCSV.R
├── createItalyCSV.R
├── createJapanCSV.R
├── createBelgiumCSV.R
├── createColombiaCSV.R
├── createSwitzerlandCSV.R
├── getItaly.R
├── createBrazilCSV.R
├── createAfghanistanCSV.R
├── getHaiti.R
├── getJapan.R
├── img
    ├── peru-flag-xs.png
    ├── belgium-flag-xs.png
    ├── brazil-flag-xs.png
    ├── canada-flag-xs.png
    ├── china-flag-xs.png
    ├── germany-flag-xs.png
    ├── haiti-flag-xs.png
    ├── india-flag-xs.png
    ├── italy-flag-xs.png
    ├── japan-flag-xs.png
    ├── australia-flag-xs.png
    ├── colombia-flag-xs.png
    ├── afghanistan-flag-xs.png
    ├── switzerland-flag-xs.png
    └── united-states-of-america-flag-xs.png
├── getBelgium.R
├── getBrazil.R
├── getColombia.R
├── getAfghanistan.R
├── getSwitzerland.R
├── investigate recoveries.xlsx
├── getPeru.R
├── runDeconvolutionAfghanistan.R
├── runDeconvolutionPeru.R
├── dat
    └── Continents
    │   ├── continentData.RData
    │   └── getContinents.R
├── runDeconvolutionBrazil.R
├── runDeconvolutionHaiti.R
├── runDeconvolutionIndia.R
├── runDeconvolutionItaly.R
├── runDeconvolutionJapan.R
├── getDataLocal.R
├── runDeconvolutionBelgium.R
├── runDeconvolutionColombia.R
├── runDeconvolutionGermany.R
├── runDeconvolutionSwitzerland.R
├── translations
    ├── translation_Turkish_with_notes.xlsx
    └── translations.json
├── mostRecentDateForEachCountry.R
├── getGermany.R
├── getIndia.R
├── runDeconvolutionJHU.R
├── .gitignore
├── anythingWithStates.R
├── LICENSE
├── getDataJHU.R
├── README.md
├── detection
    ├── estGlobalV2.R
    ├── deconvEst.R
    ├── deconvFunctions.R
    └── estFunctionsV3.R
├── createCSV.R
├── getDataGeneral.R
├── functions.R
├── doc
    └── about.Rmd
├── app.R
└── base.html


/createPeruCSV.R:
--------------------------------------------------------------------------------
1 | source('createCSV.R')
2 | createCSV('Peru')
3 | 


--------------------------------------------------------------------------------
/createHaitiCSV.R:
--------------------------------------------------------------------------------
1 | source('createCSV.R')
2 | createCSV('Haiti')
3 | 


--------------------------------------------------------------------------------
/createItalyCSV.R:
--------------------------------------------------------------------------------
1 | source('createCSV.R')
2 | createCSV('Italy')
3 | 


--------------------------------------------------------------------------------
/createJapanCSV.R:
--------------------------------------------------------------------------------
1 | source('createCSV.R')
2 | createCSV('Japan')
3 | 


--------------------------------------------------------------------------------
/createBelgiumCSV.R:
--------------------------------------------------------------------------------
1 | source('createCSV.R')
2 | createCSV('Belgium')
3 | 


--------------------------------------------------------------------------------
/createColombiaCSV.R:
--------------------------------------------------------------------------------
1 | source('createCSV.R')
2 | createCSV('Colombia')
3 | 


--------------------------------------------------------------------------------
/createSwitzerlandCSV.R:
--------------------------------------------------------------------------------
1 | source('createCSV.R')
2 | createCSV('Switzerland')
3 | 


--------------------------------------------------------------------------------
/getItaly.R:
--------------------------------------------------------------------------------
1 | source('getDataGeneral.R')
2 | 
3 | getDataCovid19datahub('Italy')
4 | 


--------------------------------------------------------------------------------
/createBrazilCSV.R:
--------------------------------------------------------------------------------
1 | source('createCSV.R')
2 | createCSV('Brazil','covidregionaldata')
3 | 


--------------------------------------------------------------------------------
/createAfghanistanCSV.R:
--------------------------------------------------------------------------------
1 | source('createCSV.R')
2 | createCSV('Afghanistan','covidregionaldata')
3 | 


--------------------------------------------------------------------------------
/getHaiti.R:
--------------------------------------------------------------------------------
1 | source('getDataGeneral.R')
2 | 
3 | getDataCovid19datahubWithoutRecovered('Haiti')
4 | 


--------------------------------------------------------------------------------
/getJapan.R:
--------------------------------------------------------------------------------
1 | source('getDataGeneral.R')
2 | 
3 | getDataCovid19datahubWithoutRecovered('Japan')
4 | 


--------------------------------------------------------------------------------
/img/peru-flag-xs.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/benflips/nCovForecast/HEAD/img/peru-flag-xs.png


--------------------------------------------------------------------------------
/getBelgium.R:
--------------------------------------------------------------------------------
1 | source('getDataGeneral.R')
2 | 
3 | getDataCovid19datahubWithoutRecovered('Belgium')
4 | 


--------------------------------------------------------------------------------
/getBrazil.R:
--------------------------------------------------------------------------------
1 | source('getDataGeneral.R')
2 | 
3 | getDataCovid19datahubWithoutRecovered('Brazil')
4 | 


--------------------------------------------------------------------------------
/getColombia.R:
--------------------------------------------------------------------------------
1 | source('getDataGeneral.R')
2 | 
3 | getDataCovid19datahubWithoutRecovered('Colombia')
4 | 


--------------------------------------------------------------------------------
/img/belgium-flag-xs.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/benflips/nCovForecast/HEAD/img/belgium-flag-xs.png


--------------------------------------------------------------------------------
/img/brazil-flag-xs.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/benflips/nCovForecast/HEAD/img/brazil-flag-xs.png


--------------------------------------------------------------------------------
/img/canada-flag-xs.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/benflips/nCovForecast/HEAD/img/canada-flag-xs.png


--------------------------------------------------------------------------------
/img/china-flag-xs.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/benflips/nCovForecast/HEAD/img/china-flag-xs.png


--------------------------------------------------------------------------------
/img/germany-flag-xs.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/benflips/nCovForecast/HEAD/img/germany-flag-xs.png


--------------------------------------------------------------------------------
/img/haiti-flag-xs.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/benflips/nCovForecast/HEAD/img/haiti-flag-xs.png


--------------------------------------------------------------------------------
/img/india-flag-xs.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/benflips/nCovForecast/HEAD/img/india-flag-xs.png


--------------------------------------------------------------------------------
/img/italy-flag-xs.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/benflips/nCovForecast/HEAD/img/italy-flag-xs.png


--------------------------------------------------------------------------------
/img/japan-flag-xs.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/benflips/nCovForecast/HEAD/img/japan-flag-xs.png


--------------------------------------------------------------------------------
/img/australia-flag-xs.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/benflips/nCovForecast/HEAD/img/australia-flag-xs.png


--------------------------------------------------------------------------------
/img/colombia-flag-xs.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/benflips/nCovForecast/HEAD/img/colombia-flag-xs.png


--------------------------------------------------------------------------------
/getAfghanistan.R:
--------------------------------------------------------------------------------
1 | source('getDataGeneral.R')
2 | 
3 | getDataCovid19datahubWithoutRecovered('Afghanistan')
4 | 


--------------------------------------------------------------------------------
/getSwitzerland.R:
--------------------------------------------------------------------------------
1 | source('getDataGeneral.R')
2 | 
3 | getDataCovid19datahubWithoutRecovered('Switzerland')
4 | 


--------------------------------------------------------------------------------
/img/afghanistan-flag-xs.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/benflips/nCovForecast/HEAD/img/afghanistan-flag-xs.png


--------------------------------------------------------------------------------
/img/switzerland-flag-xs.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/benflips/nCovForecast/HEAD/img/switzerland-flag-xs.png


--------------------------------------------------------------------------------
/investigate recoveries.xlsx:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/benflips/nCovForecast/HEAD/investigate recoveries.xlsx


--------------------------------------------------------------------------------
/getPeru.R:
--------------------------------------------------------------------------------
1 | source('getDataGeneral.R')
2 | 
3 | getDataCovid19datahubWithoutRecovered('Peru', ignore.deaths=TRUE)
4 | 


--------------------------------------------------------------------------------
/runDeconvolutionAfghanistan.R:
--------------------------------------------------------------------------------
1 | source('getDataGeneral.R')
2 | 
3 | runDeconvolution('Afghanistan', deconvProcess = 1)
4 | 


--------------------------------------------------------------------------------
/runDeconvolutionPeru.R:
--------------------------------------------------------------------------------
1 | source('getDataGeneral.R')
2 | 
3 | runDeconvolution('Peru', deconvProcess = 2, runShort = TRUE)
4 | 


--------------------------------------------------------------------------------
/dat/Continents/continentData.RData:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/benflips/nCovForecast/HEAD/dat/Continents/continentData.RData


--------------------------------------------------------------------------------
/runDeconvolutionBrazil.R:
--------------------------------------------------------------------------------
1 | source('getDataGeneral.R')
2 | 
3 | runDeconvolution('Brazil', deconvProcess = 2, runShort = TRUE)
4 | 


--------------------------------------------------------------------------------
/runDeconvolutionHaiti.R:
--------------------------------------------------------------------------------
1 | source('getDataGeneral.R')
2 | 
3 | runDeconvolution('Haiti', deconvProcess = 2, runShort = TRUE)
4 | 


--------------------------------------------------------------------------------
/runDeconvolutionIndia.R:
--------------------------------------------------------------------------------
1 | source('getDataGeneral.R')
2 | 
3 | runDeconvolution('India', deconvProcess = 2, runShort = TRUE)
4 | 


--------------------------------------------------------------------------------
/runDeconvolutionItaly.R:
--------------------------------------------------------------------------------
1 | source('getDataGeneral.R')
2 | 
3 | runDeconvolution('Italy', deconvProcess = 2, runShort = TRUE)
4 | 


--------------------------------------------------------------------------------
/runDeconvolutionJapan.R:
--------------------------------------------------------------------------------
1 | source('getDataGeneral.R')
2 | 
3 | runDeconvolution('Japan', deconvProcess = 2, runShort = TRUE)
4 | 


--------------------------------------------------------------------------------
/getDataLocal.R:
--------------------------------------------------------------------------------
1 | load("dat/dataList.RData") # main data source
2 | load("dat/Global/menuData.RData") # data to fire up the ui
3 | 
4 | 


--------------------------------------------------------------------------------
/runDeconvolutionBelgium.R:
--------------------------------------------------------------------------------
1 | source('getDataGeneral.R')
2 | 
3 | runDeconvolution('Belgium', deconvProcess = 2, runShort = TRUE)
4 | 


--------------------------------------------------------------------------------
/runDeconvolutionColombia.R:
--------------------------------------------------------------------------------
1 | source('getDataGeneral.R')
2 | 
3 | runDeconvolution('Colombia', deconvProcess = 2, runShort = TRUE)
4 | 


--------------------------------------------------------------------------------
/runDeconvolutionGermany.R:
--------------------------------------------------------------------------------
1 | source('getDataGeneral.R')
2 | 
3 | runDeconvolution('Germany',    deconvProcess = 2, runShort = TRUE)
4 | 


--------------------------------------------------------------------------------
/img/united-states-of-america-flag-xs.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/benflips/nCovForecast/HEAD/img/united-states-of-america-flag-xs.png


--------------------------------------------------------------------------------
/runDeconvolutionSwitzerland.R:
--------------------------------------------------------------------------------
1 | source('getDataGeneral.R')
2 | 
3 | runDeconvolution('Switzerland', deconvProcess = 2, runShort = TRUE)
4 | 


--------------------------------------------------------------------------------
/translations/translation_Turkish_with_notes.xlsx:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/benflips/nCovForecast/HEAD/translations/translation_Turkish_with_notes.xlsx


--------------------------------------------------------------------------------
/mostRecentDateForEachCountry.R:
--------------------------------------------------------------------------------
1 | load('dat/dataList.RData')
2 | 
3 | namesFromDatalist <- names(dataList)
4 | 
5 | for (name in namesFromDatalist) {
6 |    message(paste(tail(names(dataList[[name]]$timeSeriesInfections),n=1), name))
7 | }
8 | 
9 | 


--------------------------------------------------------------------------------
/getGermany.R:
--------------------------------------------------------------------------------
1 | source('getDataGeneral.R')
2 | 
3 | getDataGeneral('Germany',
4 |                '/srv/shiny-server/covid19_rki_data/confirmed.csv',
5 |                '/srv/shiny-server/covid19_rki_data/deaths.csv',
6 |                '',
7 |                TRUE)
8 | 


--------------------------------------------------------------------------------
/getIndia.R:
--------------------------------------------------------------------------------
1 | source('getDataGeneral.R')
2 | 
3 | getDataGeneral('India',
4 |                'https://raw.githubusercontent.com/vipinbhatnagar/covid19/master/confirmed.csv',
5 |                'https://raw.githubusercontent.com/vipinbhatnagar/covid19/master/deaths.csv',
6 |                '',
7 |                TRUE)
8 | 


--------------------------------------------------------------------------------
/runDeconvolutionJHU.R:
--------------------------------------------------------------------------------
1 | source('getDataGeneral.R')
2 | 
3 | runDeconvolution('Global',    deconvProcess = 2, runShort = TRUE)
4 | runDeconvolution('China',     deconvProcess = 2, runShort = TRUE)
5 | runDeconvolution('US',        deconvProcess = 2, runShort = TRUE)
6 | runDeconvolution('Canada',    deconvProcess = 2, runShort = TRUE)
7 | runDeconvolution('Australia', deconvProcess = 2, runShort = TRUE)
8 | 


--------------------------------------------------------------------------------
/.gitignore:
--------------------------------------------------------------------------------
 1 | .Rproj.user
 2 | .Rhistory
 3 | .RData
 4 | .Ruserdata
 5 | /rsconnect
 6 | *.Rproj
 7 | .DS_Store
 8 | dat/~$*
 9 | 
10 | dat/cacheData.RData
11 | dat/estGlobal.RData
12 | dat/menuData.RData
13 | dat/dataList.RData
14 | 
15 | csvData/
16 | 
17 | dat/Global/*.*
18 | 
19 | dat/Australia/*.*
20 | dat/Canada/*.*
21 | dat/China/*.*
22 | dat/US/*.*
23 | dat/India/*.*
24 | dat/Germany/*.*
25 | dat/Italy/*.*
26 | dat/Colombia/*.*
27 | dat/Japan/*.*
28 | dat/Peru/*.*
29 | dat/Switzerland/*.*
30 | dat/Belgium/*.*
31 | dat/Haiti/*.*
32 | dat/Brazil/*.*
33 | dat/Afghanistan/*.*
34 | 


--------------------------------------------------------------------------------
/anythingWithStates.R:
--------------------------------------------------------------------------------
 1 | library(COVID19)
 2 | 
 3 | countries_with_state_data <- unique(covid19(level = 2)$administrative_area_level_1)
 4 | 
 5 | # [1] "United Kingdom" "Colombia"       "Latvia"         "Canada"         "Russia"        
 6 | # [6] "Czech Republic" "Japan"          "South Africa"   "France"         "Italy"         
 7 | #[11] "China"          "Haiti"          "Switzerland"    "Netherlands"    "Sweden"        
 8 | #[16] "India"          "United States"  "Australia"      "Belgium"        "Denmark" 
 9 | 
10 | for (country in countries_with_state_data) {
11 |   message(paste(toString(length(unique(covid19(c(country), level=2, verbose=FALSE)$deaths))), ',', country))
12 | }
13 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2020 benflips
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


--------------------------------------------------------------------------------
/getDataJHU.R:
--------------------------------------------------------------------------------
 1 | source('getDataGeneral.R')
 2 | 
 3 | getDataGeneral('Global',
 4 |                'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv',
 5 |                'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv',
 6 |                verbose = TRUE)
 7 | 
 8 | getDataGeneral('China',
 9 |                'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv',
10 |                'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv','',
11 |                TRUE)
12 | 
13 | getDataGeneral('US',
14 |                'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_US.csv',
15 |                'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_US.csv','',
16 |                TRUE)
17 | 
18 | getDataGeneral('Canada',
19 |                'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv',
20 |                'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv','',
21 |                TRUE)
22 | 
23 | getDataGeneral('Australia',
24 |                'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv',
25 |                'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv','',
26 |                verbose = TRUE)
27 | 


--------------------------------------------------------------------------------
/dat/Continents/getContinents.R:
--------------------------------------------------------------------------------
 1 | ## ---------------------------
 2 | ##
 3 | ## Script name: getContinents
 4 | ##
 5 | ## Purpose of script: Get continent, country names for alignment with JHU data
 6 | ##
 7 | ## Author: Ben Phillips
 8 | ##
 9 | ## Date Created: 2020-04-18
10 | ##
11 | ## Email: phillipsb@unimelb.edu.au
12 | ##
13 | ## ---------------------------
14 | ##
15 | ## Notes:
16 | ##   
17 | ##
18 | ## --------------------------
19 | ## load up the packages we will need 
20 | 
21 | ## ---------------------------
22 | 
23 | ## load up our functions into memory
24 | 
25 | ## ---------------------------  
26 | 
27 | d <- read.csv(file = "https://raw.githubusercontent.com/dbouquin/IS_608/master/NanosatDB_munging/Countries-Continents.csv",
28 |               stringsAsFactors = FALSE)
29 | 
30 | misMatchData <- d$Country[! d$Country %in% timeSeriesInfections$Region]
31 | misMatchJHU <- timeSeriesInfections$Region[! timeSeriesInfections$Region %in% d$Country]
32 | 
33 | d$Country[d$Country=="Burkina"] <- "Burkina Faso"
34 | d$Country[d$Country=="Congo, Democratic Republic of"] <- "Congo (Kinshasa)"
35 | d$Country[d$Country=="Congo"] <- "Congo (Brazzaville)"
36 | d$Country[d$Country=="Burma (Myanmar)"] <- "Myanmar"
37 | d$Country[d$Country=="Cape Verde"] <- "Cabo Verde"
38 | d$Country[d$Country=="Ivory Coast"] <- "Cote d'Ivoire"
39 | d$Country[d$Country=="CZ"] <- "Czechia"
40 | d$Country[d$Country=="Swaziland"] <- "Eswatini"
41 | d$Country[d$Country=="Macedonia"] <- "North Macedonia"
42 | d$Country[d$Country=="Russian Federation"] <- "Russia"
43 | d$Country[d$Country=="East Timor"] <- "Timor-Leste"
44 | 
45 | d <- rbind(d, cbind(Continent = "Europe", Country = "Kosovo"))
46 | d <- rbind(d, cbind(Continent = "Europe", Country = "Holy See"))
47 | d <- rbind(d, cbind(Continent = "Asia", Country = "Taiwan*"))
48 | d <- rbind(d, cbind(Continent = "Asia", Country = "West Bank and Gaza"))
49 | d <- rbind(d, cbind(Continent = "Africa", Country = "Western Sahara"))
50 | 
51 | continentData <- d
52 | 
53 | save(continentData, file = "dat/Continents/continentData.RData")
54 | 
55 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # nCovForecast
 2 | Code for nCov forecasting tool (Shiny app)
 3 | 
 4 | Takes data from several sources (Johns Hopkins University, Indian Government), breaks it down by country/region and:
 5 | 
 6 | 1. gives simple projections of active confirmed cases for next ten days;
 7 | 2. estimates detection probability over time in each country and reports detection-corrected values
 8 | 3. shows growth rates in each country over last twenty days (tracking success at suppression)
 9 | 
10 | The app is under continual development.  Contributions and suggestions are welcome.  If you have data that you would like to see represented here, please get in contact. 
11 | 
12 | ## To run in development mode
13 | 
14 | 1. Install R on your machine.  This app needs at least version 3.6.0.  Check your R version with `R --version`
15 | 2. Get the application files: `git clone https://github.com/benflips/nCovForecast.git`
16 | 3. cd to your new folder `nCovForecast`
17 | 4. Run it by typing "R" in a terminal
18 | 5. Install some packages:
19 | ```
20 | install.packages('shiny')
21 | install.packages('readr')
22 | install.packages('markdown')
23 | install.packages('curl')
24 | install.packages('addreg')
25 | install.packages('plotly')
26 | install.packages('shiny.i18n')
27 | install.packages('quantreg')
28 | install.packages('gam')
29 | install.packages('COVID19')
30 | install.packages('broom')
31 | install.packages('lubridate')
32 | install.packages('rvest')
33 | install.packages('covidregionaldata')
34 | ```
35 | 6. Generate cached data (see below)
36 | 7. `library(shiny)`
37 | 8. `runApp('.')`  This should automatically open your browser and display the app.
38 | 
39 | After you have installed and run this for the first time, only steps 3, 4, and 7-8 will be required.  Data can be updated each day by running step 6.
40 | 
41 | ## To generate cached data (step 6)
42 | 
43 | If you are on the main server, simply run:
44 | 
45 | `Rscript getData.R`
46 | 
47 | Otherwise, go into getData.R, set `server <- FALSE`.  This will then obtain data directly from raw.githubusercontent.com rather than from a saved path (which only applies if you are on the main server).  Then run:
48 | 
49 | `Rscript getData.R`
50 | 
51 | Which will take about 10 minutes to run.  This script will generate a directory structure inside /dat (if that structure doesn't already exist) and populate it with data required by the app.
52 | 
53 | In subsequent days, to update data after this initial run you will need to run both `getData.R` and `getIndia.R` separately.
54 | 


--------------------------------------------------------------------------------
/detection/estGlobalV2.R:
--------------------------------------------------------------------------------
  1 | ## ---------------------------
  2 | ##
  3 | ## Script name: estGlobal.R
  4 | ##
  5 | ## Purpose of script: estimation of cumulative infections and projections of new cases based on daily case counts
  6 | ##
  7 | ## Author: Ian Marschner
  8 | ##
  9 | ## Date Created: 2020-03-30
 10 | ##
 11 | ## Email: ian.marschner@ctc.usyd.edu.au
 12 | ##
 13 | ## ---------------------------
 14 | ## ---------------------------
 15 | ##
 16 | ## Notes:
 17 | ##   
 18 | ## Objects produced by this code:
 19 | ## 1. cumulative.infections = estimated cumulative number of infections at each day (both diagnosed and undiagnosed) 
 20 | ## 2. cumulative.projections = forecast cumulative number of new cases for future days, appended to past observed cases 
 21 | ##
 22 | ## Currently the forecasting of new cases is disabled in this script 
 23 | ## --------------------------
 24 | 
 25 | #packages for reading data and implementing non-negative linear Poisson regression
 26 | library("addreg", quietly = TRUE)
 27 | suppressPackageStartupMessages(library("turboEM", quietly = TRUE, warn.conflicts = FALSE))
 28 | suppressPackageStartupMessages(library("SparseM", quietly = TRUE, warn.conflicts = FALSE))
 29 | 
 30 | #functions for estimation and projection
 31 | source("detection/estFunctionsV3.R")
 32 | source("functions.R")
 33 | 
 34 | orgLevel <- commandArgs()[6] # get relevant command line argument
 35 | 
 36 | cat("  Running deconvolutions for", orgLevel, "using old method...\n")
 37 | 
 38 | load(paste0("dat/",orgLevel,"/cacheData.RData"))
 39 | cases.all <- t(timeSeriesInfections)[-1,]
 40 | 
 41 |                             
 42 | # organise case data by country
 43 | 
 44 | T<-dim(cases.all)[1]
 45 | 
 46 | #incubation distribution: discretised version of a logNormal(mean=5.2 days, 95% percentile=12.5 days)
 47 | #Li et al. (2020) NEJM DOI:10.1056/NEJMoa2001316
 48 | #maximum incubation period is assumed to be 25 days
 49 | 
 50 | inc.dist<-cbind(c(0:25),
 51 |                 c(0.0167216581, 0.1209939502, 0.1764151755, 0.1645457302, 0.1319862874, 0.0998480498,
 52 |                   0.0738430769, 0.0542463417, 0.0398885964, 0.0294712744, 0.0219198648, 0.0164263561,
 53 |                   0.0124063530, 0.0094438368, 0.0072440676, 0.0055980247, 0.0043568666, 0.0034139852,
 54 |                   0.0026925052, 0.0021365915, 0.0017053915, 0.0013687946, 0.0011044472, 0.0008956368,
 55 |                   0.0007297832, 0.0005973546))
 56 | 
 57 | colnames(inc.dist)=c("days","probability")
 58 | 
 59 | #Design matrix for linear Poisson regression
 60 | designF<-design(T,inc.dist)
 61 | 
 62 | #produce cumulative infection estimates for each country/region
 63 | infect.total<-apply(cases.all,2,infect.est,inc.dist,designF)
 64 | cumulative.infections<-data.frame(timeSeriesInfections[,1],t(infect.total))
 65 | colnames(cumulative.infections)<-colnames(timeSeriesInfections)
 66 | 
 67 | #produce undiagnosed cases
 68 | infMat <- as.matrix(timeSeriesInfections[, -1])
 69 | cumMat <- as.matrix(cumulative.infections[, -1])
 70 | undiag <- cumMat - infMat
 71 | undiag[undiag<0] <- 0
 72 | undiagnosed.infections <- data.frame(timeSeriesInfections[,1], undiag)
 73 | colnames(undiagnosed.infections)<-colnames(timeSeriesInfections)
 74 | 
 75 | #Produce projections of active cases  -- aggregate undiagnosed cases onto future times
 76 | projectTo <- 5 # days to project forward
 77 | projections<-apply(infect.total, 2, project, inc.dist, designF, proj.days = projectTo, inf.extrap = 10) #cumulative new cases over projection period
 78 | projections<-timeSeriesActive[,T]+t(projections) #add projected new cases on to active cases
 79 | 
 80 | # produce death projections and subtract from projection
 81 |   # assumes 17 days from diagnosis to death and 2.5% case fatality ratio in diagnosed cohort
 82 | newCasesMat <- infMat - cbind(rep(0, nrow(infMat)), infMat[,-ncol(infMat)])
 83 | deathProj <- round(newCasesMat[, (ncol(newCasesMat)-(17+projectTo-1)):(ncol(newCasesMat)-17)]*0.025, 0)
 84 | deathProj <- t(apply(deathProj, 1, cumsum)) # cumulative deaths
 85 | projections <- projections - deathProj # subtract deaths
 86 | 
 87 | # produce recovery projections and subtract from projection
 88 | # estimate best time to recovery, given recovery data, for each region
 89 | deathMat <- as.matrix(timeSeriesDeaths[, -1])
 90 | recTime <- rep(NA, nrow(timeSeriesInfections)) # vector to take best recovery time
 91 | pad <- matrix(0, nrow = length(recTime), ncol = projectTo) # data to pad matrix with
 92 | matI <- cbind(infMat-deathMat, pad) # padded infection matrix
 93 | recProj <- matrix(NA, nrow = nrow(matI), ncol = ncol(matI)) #matrix to take recoveries
 94 | for (rr in 1:length(recTime)){
 95 |   recTime[rr]<-recLagOptim(unlist(timeSeriesInfections[rr, -1]),
 96 |                            unlist(timeSeriesDeaths[rr, -1]),
 97 |                            unlist(timeSeriesRecoveries[rr, -1]))
 98 |   recProj[rr,] <- c(rep(0, recTime[rr]), matI[rr, -((ncol(matI)-(recTime[rr]-1)):ncol(matI))]) # lagged by recLag days
 99 | }
100 | recProj <- recProj - cbind(rep(0, nrow(recProj)), recProj[,-ncol(recProj)]) # recoveries on each day
101 | recProj <- recProj[,(ncol(recProj)-projectTo+1):ncol(recProj)] # at projected times
102 | recProj <- t(apply(recProj, 1, cumsum))
103 | recProj[recProj<0] <- 0
104 | projections <- projections - recProj # subtract recoveries
105 | projections[projections < 0] <- 0 #bound results
106 | 
107 | 
108 | active.projections<-data.frame(timeSeriesActive[,1], projections) # form dataframe
109 | colnames(active.projections) <- c("Region", format(dates[length(dates)]+1:5, "%m.%d.%y"))
110 | 
111 | 
112 | #save output 
113 | save(cumulative.infections, undiagnosed.infections, active.projections, file=paste0("dat/",orgLevel,"/estDeconv.RData"))
114 | cat("  Deconvolution complete.\n\n")
115 | 


--------------------------------------------------------------------------------
/createCSV.R:
--------------------------------------------------------------------------------
  1 | library(COVID19)
  2 | library(tidyverse)
  3 | 
  4 | createCSV <- function(countryName, libraryToSourceFrom = 'COVID19') {
  5 | 
  6 | 
  7 | updateCSV <- FALSE
  8 | 
  9 | filename = paste0('csvData/',countryName,'_confirmed.csv')
 10 | 
 11 | if (file.exists(filename)) {
 12 |   existingCSV <- read.csv(file = filename)
 13 | } else {
 14 |   message('File does not exist - create for first time')
 15 |   updateCSV <- TRUE
 16 | }
 17 | 
 18 | if (libraryToSourceFrom == 'COVID19') { # covid19datahub.io
 19 |   library(COVID19)
 20 |   originalData <- covid19(c(countryName), level = 2, verbose=FALSE)
 21 |   states <- unique(originalData$administrative_area_level_2)
 22 |   originalData <- originalData %>% rename(state           = administrative_area_level_2,
 23 |                                           cases_total     = confirmed,
 24 |                                           deaths_total    = deaths,
 25 |                                           recovered_total = recovered) # rename columns from COVID19 to have same format as covidregionaldata
 26 |   #Fill in missing/NA values
 27 |   if (sum(is.na(originalData$cases_total)) > 0) {
 28 |     originalData$cases_total <- popNA(originalData$cases_total,originalData$state)
 29 |   }
 30 |   if (sum(is.na(originalData$deaths_total)) > 0) {
 31 |     originalData$deaths_total <- popNA(originalData$deaths_total,originalData$state)
 32 |   }
 33 |   if (sum(is.na(originalData$recovered_total)) > 0) {
 34 |     originalData$recovered_total <- popNA(originalData$recovered_total,originalData$state)
 35 |   }
 36 | } else if (libraryToSourceFrom == 'covidregionaldata') { # https://cran.r-project.org/web/packages/covidregionaldata/
 37 |   library(covidregionaldata)
 38 |   originalData <- get_regional_data(countryName)
 39 |   if (countryName == 'Afghanistan') {
 40 |     originalData <- originalData %>% rename(state = province)
 41 |     # Afghanistan does a funny thing where there are sort of two rows for each Province - e.g. 'Baghlan' and 'Baghlan Province'.  The row without the word 'Province' appears to be more accurate.
 42 |     # also artificially excluding Jawzjan  (apparent repeat of Jowzjan, with much less data)
 43 |     # also artificially excluding Dykundi  (apparent repeat of Daykundi, with much less data)
 44 |     # also artificially excluding Hirat    (apparent repeat of Herat, with much less data)
 45 |     # also artificially excluding Nimroz   (apparent repeat of Nimruz, with much less data)    
 46 |     # also artificially excluding Paktya   (apparent repeat of Paktia, with much less data)
 47 |     # also artificially excluding Panjsher (apparent repeat of Panjshir, with much less data)
 48 |     originalData <- subset(originalData, originalData$state %in% c('Badakhshan','Badghis','Baghlan','Balkh','Bamyan','Daykundi','Farah','Faryab','Ghazni','Ghor','Helmand','Herat','Jowzjan','Kabul','Kandahar','Kapisa','Khost','Kunar','Kunduz','Laghman','Logar','Nangarhar','Nimruz','Nuristan','Paktia','Paktika','Panjshir','Parwan','Samangan','Sar-e-Pul','Takhar','Urozgan','Wardak','Zabul'))
 49 |     originalData$iso_3166_2 <- NULL
 50 |   }
 51 |   states <- unique(originalData$state)
 52 |   originalData[is.na(originalData)] <- 0 # replace NAs with 0
 53 | }
 54 | 
 55 | # some missing values can put the dates out of order.
 56 | dates <- sort(unique(originalData$date))
 57 | seq <- c(1:length(dates))
 58 | 
 59 | 
 60 | 
 61 | if (!updateCSV) {
 62 | 
 63 |   print('going to update!')
 64 | 
 65 |   mostRecentDateFromExistingCSV <- tail(names(existingCSV), n=1)
 66 |   mostRecentDateFromFreshData <- format(tail(dates, n=1), 'X%-m.%d.%y')
 67 | 
 68 |   if (mostRecentDateFromExistingCSV == mostRecentDateFromFreshData) {
 69 |     message('File exists but already up-to-date')
 70 |     updateCSV <- FALSE
 71 |   } else {
 72 |     message('File exists and data is out-of-date - updating...')
 73 |     updateCSV <- TRUE
 74 |   }
 75 | }
 76 | 
 77 | if (updateCSV) {
 78 | 
 79 |   dir.create("csvData", showWarnings = FALSE) # if the directory doesn't exist, create it.
 80 | 
 81 |   #confirmed
 82 |   currentConfirmed <- numeric(length(states))
 83 |   dataConfirmed <- originalData %>% select(state, date, cases_total)
 84 |   outputConfirmed <- data.frame('Province/State' = states, 'Country/Region' = replicate(length(states),countryName))
 85 |   for (value in seq) {
 86 |     src_set <- subset(dataConfirmed, dataConfirmed$date == dates[value])
 87 |     if (length(src_set$cases_total) == length(currentConfirmed)) {
 88 |       currentConfirmed <- src_set$cases_total
 89 |     } else {
 90 |       currentConfirmed[(outputConfirmed$state %in% src_set$state)] <- src_set$cases_total
 91 |     }
 92 |     outputConfirmed[[format(dates[value], '%-m/%d/%y')]] <- currentConfirmed
 93 |   }
 94 |   write.csv(outputConfirmed, paste0('csvData/',countryName, '_confirmed.csv'), quote=FALSE, row.names=FALSE)
 95 | 
 96 |   #deaths
 97 |   currentDeaths <- numeric(length(states))
 98 |   dataDeaths <- originalData %>% select(state, date, deaths_total)
 99 |   outputDeaths <- data.frame('Province/State' = states, 'Country/Region' = replicate(length(states),countryName))
100 |   for (value in seq) {
101 |     src_set <- subset(dataDeaths, dataDeaths$date == dates[value])
102 |     if (length(src_set$deaths_total) == length(currentDeaths)) {
103 |       currentDeaths <- src_set$deaths_total
104 |     } else {
105 |       currentDeaths[(outputDeaths$state %in% src_set$state)] <- src_set$deaths_total
106 |     }
107 |     outputDeaths[[format(dates[value], '%-m/%d/%y')]] <- currentDeaths
108 |   }
109 |   write.csv(outputDeaths, paste0('csvData/',countryName, '_deaths.csv'), quote=FALSE, row.names=FALSE)
110 | 
111 |   #recovered
112 |   currentRecovered <- numeric(length(states))
113 |   dataRecovered <- originalData %>% select(state, date, recovered_total)
114 |   outputRecovered <- data.frame('Province/State' = states, 'Country/Region' = replicate(length(states),countryName))
115 |   for (value in seq) {
116 |     src_set <- subset(dataRecovered, dataRecovered$date == dates[value])
117 |     if (length(src_set$recovered_total) == length(currentRecovered)) {
118 |       currentRecovered <- src_set$recovered_total
119 |     } else {
120 |       currentRecovered[(outputRecovered$state %in% src_set$state)] <- src_set$recovered_total
121 |     }
122 |     outputRecovered[[format(dates[value], '%-m/%d/%y')]] <- currentRecovered
123 |   }
124 |   write.csv(outputRecovered, paste0('csvData/',countryName, '_recovered.csv'), quote=FALSE, row.names=FALSE)
125 | 
126 | }
127 | 
128 | }
129 | 
130 | # Fill any NA values in src data with copy of value from previous day
131 | # NAs at start of timeline are filled with 0.
132 | popNA <- function(fullData, states) {
133 |   state_list <- unique(states)
134 |   for (s in state_list) {
135 |     timeSeries <- fullData[states == s]
136 |     if (is.na(timeSeries[1])) {
137 |       timeSeries[1] <- 0
138 |     }
139 |     for (i in 2:length(timeSeries)) {
140 |       if (is.na(timeSeries[i])) {
141 |         timeSeries[i] <- timeSeries[i-1]
142 |       }
143 |     }
144 |     fullData[states == s] <- timeSeries
145 |   }
146 |   fullData
147 | }
148 | 


--------------------------------------------------------------------------------
/detection/deconvEst.R:
--------------------------------------------------------------------------------
  1 | ## ---------------------------
  2 | ##
  3 | ## Script name: deconvEst.R
  4 | ##
  5 | ## Purpose of script: Run Ian Marschner's back-projection model on a JHU-style dataframe
  6 | ##
  7 | ## Author: Ben Phillips (following example script provided by Ian)
  8 | ##
  9 | ## Date Created: 2020-06-22
 10 | ##
 11 | ## Email: phillipsb@unimelb.edu.au
 12 | ##
 13 | ## ---------------------------
 14 | ##
 15 | ## Notes: Designed to be run from source as a stand alone script
 16 | ##   
 17 | ##
 18 | ## --------------------------
 19 | # Make a cluster for parallelising the fitting task
 20 | 
 21 | ## load up the packages we will need 
 22 | suppressPackageStartupMessages(library("readr"))
 23 | suppressPackageStartupMessages(library("addreg"))
 24 | suppressPackageStartupMessages(library("turboEM", quietly = TRUE, warn.conflicts = FALSE))
 25 | suppressPackageStartupMessages(library("SparseM", quietly = TRUE, warn.conflicts = FALSE))
 26 | suppressPackageStartupMessages(library("gam"))
 27 | #suppressPackageStartupMessages(library("parallel"))
 28 | ## ---------------------------
 29 | # # Calculate the number of cores
 30 | # nCores <- 2
 31 | # # Initiate cluster
 32 | # cl <- makeCluster(nCores)
 33 | 
 34 | ## load up our functions into memory
 35 | source("detection/deconvFunctions.R")
 36 | source("functions.R")
 37 | ## ---------------------------
 38 | 
 39 | orgLevel <- commandArgs()[6] # get relevant command line argument
 40 | short.arg <- commandArgs()[7] # get relevant command line argument
 41 | if( (!is.na(short.arg))&(tolower(short.arg) == "short") ) {
 42 |   run.short <- TRUE
 43 | } else{
 44 |   run.short <- FALSE
 45 | }
 46 | 
 47 | if( run.short ){
 48 |   cat("  Only running deconvolutions for last 4 months\n")
 49 |   short.len <- 31*4
 50 |   nTries <- 10
 51 |   nGrow <- 5
 52 | }
 53 | cat("  Running deconvolutions for", orgLevel, "using new method...\n")
 54 | 
 55 | # load relevant dataset
 56 | load(paste0("dat/",orgLevel,"/cacheData.RData"))
 57 | init.total <- rep(0,dim(timeSeriesInfections)[1])
 58 | if( run.short ) {
 59 |   if (file.exists(paste0("dat/",orgLevel,"/estDeconv.RData"))) {
 60 |     load(paste0("dat/",orgLevel,"/estDeconv.RData"))
 61 |     if (dim(timeSeriesInfections)[1] != dim(cumulative.infections)[1]) {
 62 |       #special case for re-calculating when No. of regions changes.
 63 |       T.start <- 2
 64 |       T.end <- T.start + short.len
 65 |     } else {
 66 |       last_date <- NA
 67 |       last_ind <- dim(cumulative.infections)[2]
 68 |       while( (is.na(last_date) & (last_ind>1)) ) {
 69 |         last_date <- colnames(cumulative.infections)[last_ind]
 70 |         last_ind <- last_ind - 1
 71 |       }
 72 |       T.start <- which( colnames(timeSeriesInfections) == last_date )[1]
 73 |       if ((T.start+short.len) > (dim(timeSeriesInfections)[2])) {
 74 |         T.end <- dim(timeSeriesInfections)[2]
 75 | 	last_ind <- last_ind - (T.start-(T.end-short.len))
 76 | 	T.start <- max( 2, T.end-short.len )
 77 |       } else {
 78 |         T.end <- T.start+short.len
 79 |       }
 80 |       if (T.start > 2) {
 81 |         init.total <- cumulative.infections[,last_ind]
 82 |       }
 83 |     }
 84 |   } else {
 85 |     T.start <- 2
 86 |     T.end <- T.start + short.len
 87 |   }
 88 | } else {
 89 |   T.start <- 2
 90 | }
 91 | 
 92 | cases.all <- t(timeSeriesInfections[,-1])
 93 | # Number of regions
 94 | R <- dim(cases.all)[2]
 95 | # need to add extra element to start of cases, to calc day 1 new infections
 96 | cases.all <- rbind( rep(0,R), cases.all )
 97 | cases.all <- as.data.frame(cases.all)
 98 | names(cases.all) <- timeSeriesInfections[,1]
 99 | # number of days of data (minus extra leading day)
100 | if( run.short ) {
101 |   T <- T.end - T.start + 1
102 | } else {
103 |   T <- dim(cases.all)[1] - 1
104 | }
105 | 
106 | # Assumed incubation distribution
107 | inc.dist <- incubation()
108 | 
109 | # back projections
110 | cat("   Back projections...\n")
111 | #backProjection <- lapply(cases.all, BackProj, dist = inc.dist)
112 | backProjection <- as.list(1:R)
113 | #for (rr in 1:R){
114 | #  bp <- BackProj( cases.all[,rr], inc.dist )
115 | #  backProjection[[rr]] <- bp
116 | #}
117 | for (rr in 1:R){
118 |   if( run.short ) {
119 |     bp.len <- short.len
120 |     for (i in 1:nTries) {
121 |       R.start <- T.start
122 |       R.end <- min( R.start+bp.len, dim(timeSeriesInfections)[2] )
123 |       R.start <- max( 2, R.end-bp.len )
124 |       cases.region <- cases.all[(R.start-1):R.end,rr]
125 |       bp <- BackProj( cases.region, inc.dist )
126 |       if (any(is.na( bp[,"infections"] ))) {
127 |         bp.len <- bp.len + nGrow
128 |       } else {
129 |         break
130 |       }
131 |     }
132 |     bp <- bp[(T.start-R.start+1):(T.start-R.start+T),]
133 |   } else {
134 |     bp <- BackProj( cases.all[,rr], inc.dist )
135 |   }
136 |   backProjection[[rr]] <- bp
137 | }
138 | 
139 | # stop cluster
140 | #stopCluster(cl)
141 | 
142 | # extract estimated new infections by time
143 | infect.total <- matrix(NA, nrow = T, ncol = R)
144 | for (rr in 1:R){
145 |   src_vec <- backProjection[[rr]][,"infections"]
146 |   if (any(is.na(src_vec))) {
147 |     src_vec[] <- NA
148 |   } else {
149 |     src_vec[1] <- src_vec[1] + init.total[rr]
150 |   }
151 |   infect.total[,rr] <- src_vec
152 | }
153 | 
154 | # express as cumulant and in JHU layout
155 | calc.cumsum <- apply(infect.total, 2, cumsum)
156 | if( run.short ) {
157 |   if (T.start > 2) {
158 |     prev.cumulative <- t(cumulative.infections[,2:(T.start-1)])
159 |     cumulative.infections <- rbind( prev.cumulative, calc.cumsum )
160 |   } else {
161 |     cumulative.infections <- calc.cumsum
162 |   }
163 |   T.start <- T.end - dim(cumulative.infections)[1] + 1
164 | } else {
165 |   cumulative.infections <- calc.cumsum
166 | }
167 | 
168 | # get undiagnosed cases
169 | if( run.short ) {
170 |   undiagnosed.infections <- cumulative.infections - t(timeSeriesInfections[,T.start:T.end])
171 | } else {
172 |   undiagnosed.infections <- cumulative.infections - t(timeSeriesInfections[,-1])
173 | }
174 | 
175 | #express both in JHU layout
176 | cumulative.infections<-data.frame(timeSeriesInfections[,1],t(cumulative.infections))
177 | if( run.short ) {
178 |   c.names <- colnames(timeSeriesInfections[,T.start:T.end])
179 |   c.names[1] <- colnames(timeSeriesInfections)[1]
180 |   colnames(cumulative.infections)<-c.names
181 | } else {
182 |   colnames(cumulative.infections)<-colnames(timeSeriesInfections)
183 | }
184 | undiagnosed.infections<-data.frame(timeSeriesInfections[,1],t(undiagnosed.infections))
185 | if( run.short ) {
186 |   c.names <- colnames(timeSeriesInfections[,T.start:T.end])
187 |   c.names[1] <- colnames(timeSeriesInfections)[1]
188 |   colnames(undiagnosed.infections)<-c.names
189 | } else {
190 |   colnames(undiagnosed.infections)<-colnames(timeSeriesInfections)
191 | }
192 | 
193 | # forward projections
194 | cat("   Forward projections...\n")
195 | projectTo <- 5 # days to project forward
196 | forwardProjection <- lapply(backProjection, ForwardProj, dist = inc.dist, proj.days=projectTo)
197 | proj.total <- matrix(NA, nrow = projectTo, ncol = R)
198 | for (rr in 1:R){
199 |   proj.total[,rr] <- forwardProjection[[rr]][,"forecast"]
200 | }
201 | proj.total <- data.frame(timeSeriesInfections[,1],t(proj.total)) # number of new cases on each day into the future
202 | 
203 | save(cumulative.infections, undiagnosed.infections, proj.total, file=paste0("dat/",orgLevel,"/estDeconv.RData"))
204 | cat("  Deconvolution complete.\n\n")
205 | 


--------------------------------------------------------------------------------
/detection/deconvFunctions.R:
--------------------------------------------------------------------------------
  1 | # 
  2 | ## ---------------------------
  3 | ##
  4 | ## Script name: BPfunctions.R
  5 | ##
  6 | ## Purpose of script: 4 functions for back projection and forward projection analyses
  7 | ##
  8 | ## Author: Ian Marschner
  9 | ##
 10 | ## Date Created: 2020-06-20
 11 | ##
 12 | ## Email: ian.marschner@ctc.usyd.edu.au
 13 | ##
 14 | ## ---------------------------
 15 | ## ---------------------------
 16 | 
 17 | # Back projection to estimate the total number of infections
 18 | BackProj <- function(cases,dist,pre.smooth=TRUE,post.smooth=TRUE,
 19 |                      pre.df=8,post.df=8,constr.final=TRUE,plot.results=FALSE,plot.start=1) {
 20 |   
 21 |   # cases is a series of cumulative cases (diagnosis numbers)
 22 |   # dist is output from function incubation and is a 2-column matrix with col1=days and col2=Pr( diagnosis - infection = days )
 23 |   # pre.smooth determines whether to smooth the cases data first
 24 |   # post.smooth determines whether to smooth the infection estimates at the end
 25 |   # pre.df is the effective degrees of freedom for the pre-smooth
 26 |   # post.df is the effective degrees of freedom for the post-smooth
 27 |   # constr.final determines whether to constrain final 2 infection estimates to be the same
 28 |   # plot.results determines whether a plot of the analyses is produced
 29 |   # plot.start is the time point to start the plots
 30 |   
 31 |   prob<-dist[,2]
 32 |   T<-length(cases)-1
 33 |   I.max<-length(prob)-1
 34 |   T.days<-T
 35 |   Cases<-c(diff(c(as.numeric(cases)))) # daily new cases
 36 |   Cases[Cases<0]<-0 # just in case the data have negative diagnoses e.g. due to re-adjustments
 37 |   Cases.fit<-Cases
 38 |   test0 <- sum(Cases>0)>pre.df # don't fit gam if there is not the variance to do so.
 39 |   if (pre.smooth & test0) {
 40 |     pre.fit <- suppressWarnings(gam(Cases~s(1:T.days,df=pre.df),
 41 |                                     family=poisson(link="log"))$fitted.values)
 42 |     Cases.fit <- round(pre.fit) #non-integers causes problems in nnpois
 43 |   }
 44 | 
 45 |   F<-design.BP(length(Cases),dist)
 46 |   F.full<-F
 47 |   if (constr.final) {
 48 |     F[,(T.days-1)]<-F[,(T.days-1)]+F[,T.days]
 49 |     F<-F[,-T.days]
 50 |   } 
 51 |   cf<-as.numeric(constr.final)
 52 |   
 53 |   # linear Poisson fit (the initial estimates and control parameters are currently hardcoded) 
 54 |   res<-nnpois(Cases.fit,F,rep(1,T.days),rep(0,T.days),rep(100,(T.days-cf)),
 55 |               control=addreg.control(epsilon=1e-07,maxit=1000000))
 56 |   if (res$conv==FALSE) {
 57 |     warning("linear Poisson fit did not converge - may need more iterations")
 58 |     infections.est <- rep(NA, T.days)
 59 |   } else {
 60 |     infections.est<-res$coef
 61 |   }
 62 |   if (constr.final) {
 63 |     infections.est[T.days-1]<-infections.est[T.days-1]/2
 64 |     infections.est[T.days]<-infections.est[T.days-1]
 65 |   }
 66 |   infections.est<-round(infections.est)
 67 |   test1 <- sum(infections.est>0)>post.df # don't fit gam if there is not the variance to do so.
 68 |   if (post.smooth & res$conv==TRUE & test1) infections.est <- suppressWarnings(gam(infections.est~s(1:T.days,df=post.df),
 69 |                                                           family=poisson(link="log"))$fitted.values)
 70 |   fits<-F.full%*%infections.est
 71 | 
 72 |   #plotting is no longer fully supported
 73 |   if (plot.results) {
 74 |     par(mfrow=c(2,1))
 75 |     ymax1<-sum(infections.est)
 76 |     ymax2<-max(c(infections.est,Cases.fit,Cases))
 77 |     plot(plot.start:T,cumsum(infections.est)[plot.start:T],type="l",xlab="day",ylab="cumumlative",lwd=2,main="diagnosed (black) and all (red) infections",col=50,ylim=c(0,ymax1))
 78 |     points(plot.start:T,cases[plot.start:T],pch=16)
 79 |     plot(plot.start:T,infections.est[plot.start:T],type="l",lwd=2,xlab="day",ylab="daily",col=50,ylim=c(0,ymax2))
 80 |     lines(plot.start:T,Cases.fit[plot.start:T],lwd=2)
 81 |     points(plot.start:T,Cases[plot.start:T],lwd=2,pch=16)
 82 |   }
 83 |   
 84 |   # output the smoothed daily case series and the estimated daily infections 
 85 |   out<-cbind(1:T,Cases,Cases.fit,fits,infections.est)
 86 |   colnames(out)<-c("day","observed","smoothed","fitted","infections")
 87 |   out
 88 | }
 89 | 
 90 | #-------------------------------------------------------------------------
 91 | 
 92 | # Forward projection to predict the number of cases in the future
 93 | ForwardProj <- function(BP,dist,proj.days=5,extrap.method="last",extrap.user=NULL,
 94 |                         plot.results=FALSE,plot.start=1) {
 95 |   
 96 |   # BP is an output object from function BackProj
 97 |   # dist is output from function incubation and is a 2-column matrix with col1=days and col2=Pr( diagnosis - infection = days )
 98 |   # proj.days is the number of days into the future to project cases
 99 |   # extrap.method is the method used to extrapolate the infection curve in BP[,5]
100 |   #               "last" means extrapolate the last infection number 
101 |   #               "lower" means extrapolate zero so that the projection is a lower bound
102 |   #               "user" means extrapolate a user defined value
103 |   # extrap.user is the user defined value if extrap.method="user" (othersise ignored)
104 |   # plot.results determines whether a plot of the analyses is produced
105 |   # plot.start is the time point to start the plots
106 |   
107 |   I.max<-length(dist[,1])-1
108 |   T<-length(BP[,1])
109 |   T.days<-T
110 |   if (extrap.method=="last") extrap.inf<-rep(BP[T,5],proj.days)
111 |   if (extrap.method=="lower") extrap.inf<-rep(0,proj.days)
112 |   if (extrap.method=="user") extrap.inf<-extrap.user
113 |   infections<-c(BP[,5],extrap.inf)
114 |   F<-design.BP(T+proj.days,dist)
115 |   cases<-F%*%infections
116 |   projs<-cbind(c((T+1):(T+proj.days)),cases[-c(1:T.days)])
117 |   colnames(projs)<-c("day","forecast")
118 |   
119 |   if (plot.results==TRUE) {
120 |     par(mfrow=c(2,1))
121 |     fit.final<-cumsum(BP[,4])[T]
122 |     projs.plot<-rbind(c(T,fit.final),projs)
123 |     projs.plot[,2]<-cumsum(projs.plot[,2])
124 |     plot(c(plot.start:T),cumsum(BP[,2])[plot.start:T],pch=16,ylab="cumulative cases",xlab="day",xlim=c(plot.start,T+proj.days),ylim=c(0,projs.plot[(proj.days+1),2]))
125 |     lines(c(plot.start:T),cumsum(BP[,4])[plot.start:T],lwd=2)
126 |     lines(projs.plot,lwd=2,col=50)
127 |     abline(v=T)
128 |     
129 |     plot(c(plot.start:T),BP[plot.start:T,2],pch=16,ylab="daily cases",xlab="day",xlim=c(plot.start,T+proj.days))
130 |     lines(c(plot.start:T),BP[plot.start:T,4],lwd=2)
131 |     lines(projs,lwd=2,col=50)
132 |     lines(c(T,(T+1)),c(BP[T,4],projs[1,2]),lwd=2,col=50)
133 |     abline(v=T)
134 |     
135 |   }
136 |   
137 |   projs
138 | }
139 | 
140 | #-----------------------------------------------------------------------
141 | 
142 | # incubation distribution
143 | incubation <- function(mean.inc=5.2,perc95=12.5,I.max=30,mean.test=2) {
144 |   
145 |   # distribution of the period between infection and diagnosis, possibly including for a testing delay
146 |   # mean = mean in days of the incubation period
147 |   # perc95 = 95% percentile of the incubation period
148 |   # I.max = maximum number of days for the incubation period
149 |   # mean.test = mean delay from symptoms to diagnosis due to test result delay
150 |   # plot.dist determines whether to plot the distribution
151 |   
152 |   # solve for parameters of the log-normal incubation distribution
153 |   a<-(1/2)
154 |   b<- -qnorm(0.95)
155 |   c<-log(perc95/mean.inc)
156 |   sigma<-(-b-sqrt(b**2-4*a*c))/(2*a)
157 |   mu<-log(5.2)-a*sigma**2
158 |   days<-seq(0,(I.max+1),.01)
159 |   days.int<-c(1:(I.max+1))
160 |   inc.dens<- (1/(days*sigma*sqrt(2*pi)))*exp(-(1/(2*sigma**2))*(log(days)-mu)**2)
161 |   inc.dens[1]<-0
162 |   inc.cdf<-pnorm((log(days)-mu)/sigma)
163 |   inc.cdf.int<-pnorm((log(days.int)-mu)/sigma)
164 |   prob.inc<-diff(c(0,inc.cdf.int))
165 |   prob.inc<-prob.inc/sum(prob.inc) # just in case not exactly equal to 1
166 |   out<-cbind((days.int-1),prob.inc)
167 |   colnames(out)<-c("day","probability function")
168 |   
169 |   # convolve with testing delay
170 |   if (mean.test>0) {
171 |     shape=1.2
172 |     rate=shape/mean.test
173 |     test.dens<-dgamma(days,shape=shape,rate=rate)
174 |     test.cdf<-pgamma(days,shape=shape,rate=rate)
175 |     count<-0
176 |     conv.cdf<-0
177 |     for (d in days) {
178 |       count<-count+1
179 |       conv.cdf[count]<-sum(inc.dens[1:count]*test.cdf[count:1])*0.01
180 |     }
181 |     out<-cbind(c(0:I.max),diff(c(0,conv.cdf[seq(101,(I.max+1)*100+1,100)]))/conv.cdf[(I.max+1)*100+1])
182 |     colnames(out)<-c("day","probability function")
183 |   }
184 |   
185 |   out
186 | }
187 | 
188 | #------------------------------------------------------------------------
189 | 
190 | # function to construct design matrix in linear model relating infections and diagnoses
191 | design.BP <- function(T,dist) {
192 |   
193 |   # T = length of case series
194 |   # dist = output from function incubation giving the distribution of time between infection and diagnosis 
195 |   
196 |   prob<-dist[,2]
197 |   I.max<-length(prob)-1
198 |   F<-matrix(0,T,T)
199 |   for (d in 1:T) {
200 |     start<-max(0,(d-I.max))+1
201 |     length.f<-length(c(start:d))
202 |     F[d,c(start:d)]<-prob[length.f:1]
203 |   }
204 |   F
205 | }  
206 | 
207 | 
208 | 


--------------------------------------------------------------------------------
/detection/estFunctionsV3.R:
--------------------------------------------------------------------------------
  1 | ## ---------------------------
  2 | ##
  3 | ## Script name: estFunctions.R
  4 | ##
  5 | ## Purpose of script: functions needed for estimation of cumulative infections and projections of new cases
  6 | ##
  7 | ## Author: Ian Marschner (using some code from the addreg package)
  8 | ##
  9 | ## Date Created: 2020-03-30
 10 | ##
 11 | ## Email: ian.marschner@ctc.usyd.edu.au
 12 | ##
 13 | ## ---------------------------
 14 | ## ---------------------------
 15 | ##
 16 | 
 17 | #design matrix for linear Poisson regression to back-project cases numbers
 18 | design <- function(T,inc.dist) {
 19 | 	prob.inc<-inc.dist[,2]
 20 | 	I.max<-length(prob.inc)-1
 21 | 	T.days<-T+I.max # to allow for incubation period of first cases
 22 | 	F<-matrix(0,T.days,T.days)
 23 | 	for (d in 1:T.days) {
 24 | 		start<-max(0,(d-I.max))+1
 25 | 		length.f<-length(c(start:d))
 26 | 		F[d,c(start:d)]<-prob.inc[length.f:1]
 27 | 	}
 28 | 	F
 29 | }
 30 | 
 31 | #design matrix for linear Poisson regression to estimate distribution between case and death
 32 | #cases is a series of cumulative daily diagnosis counts
 33 | design.dist <- function(cases,max.d=length(cases)) {
 34 |   Cases<-diff(c(0,as.numeric(cases))) # daily new cases
 35 |   Cases[Cases<0]<-0
 36 |   T<-length(Cases)
 37 |   F<-matrix(0,T,max.d)
 38 |   for (d in 1:max.d) F[,d]<-c(rep(0,(d-1)),Cases[1:(T-d+1)])
 39 |   F
 40 | }
 41 | 
 42 | #estimate of cumulative number of infections
 43 | #cases is a series of cumulative daily diagnosis counts
 44 | infect.est <- function(cases,inc.dist,F,min.cases=0) {
 45 | 	prob.inc<-inc.dist[,2]
 46 | 	T<-length(cases)
 47 | 	I.max<-length(prob.inc)-1
 48 | 	T.days<-T+I.max
 49 | 	Cases<-c(rep(0,I.max),diff(c(0,as.numeric(cases)))) # daily new cases
 50 | 	Cases[Cases<0]<-0
 51 | 	inf.est<-rep(0,T)
 52 | 	if (sum(Cases)>=min.cases) {
 53 | 		inf.est<-rep(-1,T)
 54 | 		res<-nnpois.smooth(Cases,F,rep(1,T.days),rep(0,T.days),rep(100,T.days),control=addreg.control(epsilon=1e-07,maxit=1000000))
 55 | 		if (res$conv)inf.est<-round(cumsum(res$coef))[-c(1:I.max)]
 56 | 	}
 57 | 	inf.est
 58 | }
 59 | 
 60 | #estimate of distribution of time between diagnosis and death
 61 | #cases and deaths are series of cumulative daily counts
 62 | death.est <- function(deaths,Fd,min.deaths=0) {
 63 |   T<-length(deaths)
 64 |   Deaths<-diff(c(0,as.numeric(deaths))) # daily new deaths
 65 |   Deaths[Deaths<0]<-0
 66 |   Death.dist<-rep(0,T)
 67 |   if (sum(Deaths)>=min.deaths) {
 68 |     Death.dist<-rep(-1,T)
 69 |     res<-nnpois(Deaths,Fd,rep(1,T),rep(0,T),rep(0.1,T),control=addreg.control(epsilon=1e-07,maxit=1000000))
 70 |     if (res$conv) Death.dist<-(cumsum(res$coef))
 71 |   }
 72 |   Death.dist
 73 | }
 74 | 
 75 | #projections
 76 | project <- function(infect,inc.dist,F,low=FALSE,proj.days=5,inf.extrap=20) {
 77 | out<-rep(max(infect),proj.days)
 78 | if (max(infect) > 0) {
 79 | 	prob.inc<-inc.dist[,2]
 80 | 	I.max<-length(inc.dist)-1
 81 | 	T<-length(infect)
 82 | 	infect.extrap<-infect[(T-inf.extrap+1):T]
 83 | 	y<-log(infect.extrap[infect.extrap>0])
 84 | 	x<-c((T-inf.extrap+1):T)[infect.extrap>0]
 85 | 	extrap.coef<-lm(y~x)$coef
 86 | 	inf.linear<-c(infect,exp(extrap.coef[1]+extrap.coef[2]*c((T+1):(T+proj.days))))
 87 | 	inf.low<-c(infect,rep(infect[T],proj.days))
 88 | 	if ((extrap.coef[2]<0)|(inf.linear[T+1]<inf.linear[T])) inf.linear<-inf.low
 89 | 	inf.daily<-diff(c(0,inf.linear))
 90 | 	inf.daily.low<-diff(c(0,inf.low))
 91 | 	proj<-0
 92 | 	proj.low<-0
 93 | 	for (i in 1:proj.days) {
 94 | 		proj[i]<-sum(rev(prob.inc)*inf.daily[(T-I.max+i):(T+i)])
 95 | 		proj.low[i]<-sum(rev(prob.inc)*inf.daily.low[(T-I.max+i):(T+i)])
 96 | 	}
 97 | 	out<-round(cumsum(proj))	
 98 | 	if (low) {
 99 | 		out<-cbind(round(cumsum(proj)),round(cumsum(proj.low)))
100 | 		colnames(out)<-c("projection","lower bound")
101 | 	}
102 | }
103 | out
104 | }
105 | 
106 | #plot cumulative infection estimates
107 | plot.inf <- function(cases,inc.dist,xstart=0,main=" ",xaxt="s",xlab="days since first diagnosis") {
108 | 	T=length(cases)
109 | 	designF<-design(T,inc.dist)
110 | 	inf<-infect.est(cases,inc.dist,designF,min.cases=0)
111 | 	plot(inf,xlim=c(xstart,T),cex=1.5,pch=16,
112 | 		xlab=xlab,ylab="cumulative infections",main=main,xaxt=xaxt)
113 | 	lines(inf,col=50,lwd=2)
114 | }
115 | 
116 | #plot cumulative diagnoses and projections
117 | plot.proj <-function(cases,inc.dist,low=FALSE,proj.days=5,inf.extrap=20,xstart=0,main=" ",xaxt="s",xlab="days since first diagnosis") {
118 | 	T=length(cases)
119 | 	designF<-design(T,inc.dist)
120 | 	inf<-infect.est(cases,inc.dist,designF,min.cases=0)
121 | 	proj<-project(inf,inc.dist,designF,low=low,proj.days=proj.days,inf.extrap=inf.extrap)
122 | 	plot(cases,xlim=c(xstart,T+proj.days),ylim=c(0,cases[T]+sum(proj[,1])),
123 | 		type="n",xlab=xlab,ylab="cumulative diagnoses",main=main,xaxt=xaxt)
124 | 	lines(c(T:(T+proj.days)),cumsum(c(cases[T],proj[,1])),col=50,lwd=2)
125 | 	if (low) lines(c(T:(T+proj.days)),cumsum(c(cases[T],proj[,2])),col=50,lwd=2,lty=3)
126 | 	points(cases,pch=16)
127 | }
128 | 
129 | # This is a version of the nnpois function from the addreg package with a smoothing step added
130 | nnpois.smooth <- function (y, x, standard, offset, start, control = addreg.control(), 
131 |     accelerate = c("em", "squarem", "pem", 
132 |         "qn"), control.method = list()) 
133 | {
134 |     control <- do.call("addreg.control", control)
135 |     accelerate <- match.arg(accelerate)
136 |     x <- as.matrix(x)
137 |     xnames <- dimnames(x)[[2L]]
138 |     ynames <- if (is.matrix(y)) 
139 |         rownames(y)
140 |     else names(y)
141 |     if (any(x < 0)) 
142 |         stop("x must be non-negative")
143 |     if (any(apply(x, 2, function(col) all(col == 0)))) 
144 |         stop("x contains column with all 0")
145 |     nvars <- ncol(x)
146 |     nobs <- NROW(y)
147 |     fam <- poisson(link = identity)
148 |     eval(fam$initialize)
149 |     mu.eta <- fam$mu.eta
150 |     linkinv <- fam$linkinv
151 |     dev.resids <- fam$dev.resids
152 |     aic <- fam$aic
153 |     weights <- rep(1, nobs)
154 |     if (is.null(standard)) 
155 |         standard <- rep.int(1, nobs)
156 |     if (is.null(offset)) 
157 |         offset <- rep.int(0, nobs)
158 |     if (any(offset < 0)) 
159 |         stop("offset must be non-negative")
160 |     converged <- FALSE
161 |     coefold <- if (!is.null(start)) {
162 |         if (length(start) != nvars) 
163 |             stop(gettextf("length of 'start' should equal %d and correspond to initial coefs for %s", 
164 |                 nvars, paste(deparse(xnames), collapse = ", ")), 
165 |                 domain = NA)
166 |         else if (any(start <= control$bound.tol)) 
167 |             stop("'start' is on our outside the boundary of the parameter space (consider 'bound.tol')", 
168 |                 domain = NA)
169 |         else start
170 |     }
171 |     else {
172 |         simple <- mean(y/standard)/colMeans(x) + 2 * control$bound.tol
173 |         trymat <- tryCatch(as.vector(solve(t(x) %*% x) %*% t(x) %*% 
174 |             (y)) + 2 * control$bound.tol, error = function(e) NULL)
175 |         if (is.null(trymat)) 
176 |             simple
177 |         else if (any(trymat < control$bound.tol)) 
178 |             simple
179 |         else trymat
180 |     }
181 |     fixptfn <- function(p, y, n, x, o, div, fam, bound.tol) {
182 |         eta <- drop(x %*% p) + o
183 |         y.over.fits <- y/fam$linkinv(eta)
184 |         y.over.fits[fam$linkinv(eta) == 0] <- 0
185 |         pnew <- p * colSums(y.over.fits * x) * div
186 |         pnew[pnew <= 0] <- bound.tol/2
187 |         return(pnew)
188 |     }
189 |     objfn <- function(p, y, n, x, o, div, fam, bound.tol) {
190 |         eta <- drop(x %*% p) + o
191 |         mu <- n * fam$linkinv(eta)
192 |         negll <- -sum(dpois(y, mu, log = TRUE))
193 |         return(negll)
194 |     }
195 |     validparams <- function(p) return(all(p >= 0))
196 |     conv.user <- function(old, new) return(conv.test(old, new, 
197 |         tol))
198 |     std.div <- 1/colSums(standard * x)
199 |     res <- turboEM::turboem(par = coefold, fixptfn = fixptfn, 
200 |         objfn = objfn, method = accelerate, pconstr = validparams, 
201 |         y = y, n = standard, x = x, o = offset, div = std.div, 
202 |         fam = fam, bound.tol = control$bound.tol, control.run = list(convtype = "parameter", 
203 |             tol = control$epsilon, stoptype = "maxiter", 
204 |             maxiter = control$maxit, convfn.user = conv.user, 
205 |             trace = control$trace), control.method = list(control.method))
206 |     if (res$fail[1]) 
207 |         stop(res$errors[1])
208 |     coefnew <- res$pars[1, ]
209 | 
210 | #---------------------------------------------------------------
211 | # Smoothing step:
212 | # Simple moving average of width 7 (1 week smoothing window)
213 | # Smoothing window can be changed using parameter width
214 | #---------------------------------------------------------------
215 | 
216 | coefnew<-masmth(coefnew)
217 | 
218 | #------------------------------------
219 |     names(coefnew) <- xnames
220 |     eta <- drop(x %*% coefnew) + offset
221 |     mu <- standard * linkinv(eta)
222 |     residuals <- (y - mu)/mu.eta(eta)
223 |     names(y) <- names(mu) <- names(eta) <- names(residuals) <- ynames
224 |     dev.new <- sum(dev.resids(y, mu, weights))
225 |     aic.model <- aic(y, nobs, mu, weights, dev.new) + 2 * nvars
226 |     aic.c.model <- aic.model + 2 * nvars * (nvars + 1)/(nobs - 
227 |         nvars - 1)
228 |     wtdmu <- standard * sum(weights * y/standard)/sum(weights)
229 |     nulldev <- sum(dev.resids(y, wtdmu, weights))
230 |     nulldf <- nobs - 1
231 |     resdf <- nobs - nvars
232 |     boundary <- any(coefnew < control$bound.tol)
233 |     list(coefficients = coefnew, residuals = residuals, fitted.values = mu, 
234 |         rank = nvars, family = fam, linear.predictors = eta, 
235 |         deviance = dev.new, aic = aic.model, aic.c = aic.c.model, 
236 |         null.deviance = nulldev, iter = res$itr[1], weights = weights, 
237 |         prior.weights = weights, standard = standard, df.residual = resdf, 
238 |         df.null = nulldf, y = y, converged = res$convergence[1], 
239 |         boundary = boundary, loglik = -res$value.objfn[1], nn.design = x)
240 | }
241 | 
242 | # simple moving average smoother
243 | # width means the half width of the smoothing window 
244 | # e.g. width=3 means +/- 3 so the smoothing window is 7
245 | masmth <- function(coef,width=3) {
246 |   p<-length(coef)
247 |   coefn<-0
248 |   for (i in (width+1):(p-width)) coefn[i]<-mean(coef[(i-width):(i+width)])
249 |   for (i in 1:width) coefn[i]<-mean(coef[1:(i+width)])
250 |   for (i in (p-width+1):p) coefn[i]<-mean(coef[(i-width):p])
251 | coefn
252 | }
253 | 
254 | 


--------------------------------------------------------------------------------
/getDataGeneral.R:
--------------------------------------------------------------------------------
  1 | runDeconvolution <- function(countryName, deconvProcess = 1, runShort=FALSE) {
  2 | 
  3 |   t1 = Sys.time()
  4 | 
  5 |   if (deconvProcess == 1){
  6 |     system(paste("Rscript detection/estGlobalV2.R", countryName), wait = TRUE) # old process (faster)
  7 |   } else {
  8 |     if (runShort) {
  9 |       system(paste("Rscript detection/deconvEst.R", countryName, "short"), wait = TRUE) # new process (slower, but better)
 10 |     } else {
 11 |       system(paste("Rscript detection/deconvEst.R", countryName), wait = TRUE) # new process (slower, but better)
 12 |     }
 13 |   }
 14 | 
 15 |   load(paste0("dat/",countryName,"/estDeconv.RData"))
 16 | 
 17 |   # load dataList object
 18 |   load("dat/dataList.RData")
 19 | 
 20 |   # append data to dataList
 21 |   dataList[[countryName]]$cumulative.infections <- cumulative.infections
 22 |   dataList[[countryName]]$undiagnosed.infections <- undiagnosed.infections
 23 | 
 24 |   # write datList back out
 25 |   save(dataList, file = "dat/dataList.RData")
 26 | 
 27 |   t2 = Sys.time()
 28 |   print(t2-t1)
 29 | 
 30 | }
 31 | 
 32 | 
 33 | getDataCovid19datahub <- function(countryName, ignore.deaths=FALSE) {
 34 |   getDataGeneral(countryName, paste0('./csvData/',countryName,'_confirmed.csv'),
 35 |                                                      paste0('./csvData/',countryName,'_deaths.csv'),
 36 |                                                      paste0('./csvData/',countryName,'_recovered.csv'), TRUE, ignore.deaths=ignore.deaths)
 37 | }
 38 | 
 39 | getDataCovid19datahubWithoutRecovered <- function(countryName, ignore.deaths=FALSE) {
 40 |   getDataGeneral(countryName, paste0('./csvData/',countryName,'_confirmed.csv'),
 41 |                                                      paste0('./csvData/',countryName,'_deaths.csv'),
 42 |                                                      '', TRUE, ignore.deaths=ignore.deaths)
 43 | }
 44 | 
 45 | getDataGeneral <- function(countryName, inputConfirmed, inputDeaths, inputRecovered = '', verbose, ignore.deaths=FALSE){
 46 | 
 47 | 
 48 | t1 = Sys.time()
 49 | 
 50 | source('functions.R')
 51 | 
 52 | inputRecoveredSupplied <- (inputRecovered != '')
 53 | 
 54 | 
 55 | noErrors <- TRUE
 56 | 
 57 | timeSeriesInfections <-loadData(inputConfirmed)
 58 | timeSeriesDeaths     <-loadData(inputDeaths)
 59 | if (inputRecoveredSupplied) {
 60 |   timeSeriesRecoveries <- loadData(inputRecovered)
 61 | }
 62 | 
 63 | if (verbose) {
 64 |   message(paste0(countryName, ': ', format(strptime(tail(names(timeSeriesInfections), n=1),'%m.%d.%y'), '%d %B %Y')))
 65 | }
 66 | 
 67 | printVerbose('Initially...',timeSeriesInfections, timeSeriesDeaths, timeSeriesRecoveries, inputRecoveredSupplied, verbose)
 68 | 
 69 | if (countryName == 'Global') {
 70 |   #if (verbose) {
 71 |   #  print('The main reason for difference here is that Canada has many lines in infections, but only one line in recoveries')
 72 |   #}
 73 |   timeSeriesInfections$Province.State <- NULL
 74 |   timeSeriesDeaths$Province.State     <- NULL
 75 | 
 76 |   if (inputRecoveredSupplied) {
 77 |      
 78 |     countriesToGenerateWithRecLag <- c('United Kingdom','Sweden','Netherlands','Serbia','Honduras','Namibia','Belgium','Central African Republic','Equatorial Guinea',
 79 |                                      'Greece','France','Egypt','US','Guinea-Bissau','Somalia','Mozambique','South Sudan','Kyrgyzstan','Libya','Bolivia','Congo (Brazzaville)',
 80 |                                      'Nigeria','Ecuador','Kazakhstan','Sudan','Philippines','Madagascar','Congo (Kinshasa)','Benin','Spain','Haiti','Guatemala','Gabon',
 81 |                                      'Ghana','South Africa','Suriname','Ukraine','Iraq','Afghanistan','Czechia','Liberia','Mexico','India','Canada','Brazil','Colombia')
 82 | 
 83 | 
 84 |     timeSeriesRecoveries <- subset(timeSeriesRecoveries, !(timeSeriesRecoveries$Country.Region %in% countriesToGenerateWithRecLag))
 85 |     timeSeriesRecoveries <- rbind(timeSeriesRecoveries, recLag(subset(timeSeriesInfections, timeSeriesInfections$Country.Region %in% countriesToGenerateWithRecLag),
 86 |                                                              subset(timeSeriesDeaths,     timeSeriesDeaths$Country.Region     %in% countriesToGenerateWithRecLag), active=FALSE, ignore.deaths=ignore.deaths))
 87 | 
 88 |     timeSeriesRecoveries$Province.State <- NULL
 89 |   }  
 90 | 
 91 |   #printVerbose(paste0('After excluding countries: (',toString(countriesToGenerateWithRecLag),') with known bad recovery data and using recLag to generate those instead'),timeSeriesInfections, timeSeriesDeaths, timeSeriesRecoveries, inputRecoveredSupplied, verbose)
 92 | 
 93 | } else {
 94 | 
 95 |   timeSeriesInfections <- subset(timeSeriesInfections, timeSeriesInfections$Country.Region == countryName)
 96 |   if (countryName == 'Australia') {timeSeriesInfections <- fixNSW(timeSeriesInfections)}
 97 |   timeSeriesDeaths     <- subset(timeSeriesDeaths,     timeSeriesDeaths$Country.Region     == countryName)
 98 |   if (inputRecoveredSupplied) {
 99 |     timeSeriesRecoveries <- subset(timeSeriesRecoveries, timeSeriesRecoveries$Country.Region == countryName)
100 |     if (countryName == 'Australia') {# fix the ruby princess issue and hard code ignore NSW recovery data
101 |       timeSeriesRecoveries <- fixNSW(timeSeriesRecoveries) 
102 |       tempRec <- recLag(infections = timeSeriesInfections, deaths = timeSeriesDeaths, active = FALSE, ignore.deaths=ignore.deaths)
103 |       timeSeriesRecoveries[timeSeriesRecoveries$Province.State == "New South Wales",] <- tempRec[tempRec$Province.State=="New South Wales",]
104 |     }
105 |     timeSeriesRecoveries$Country.Region <- NULL
106 |   }
107 |   timeSeriesInfections$Country.Region <- NULL
108 |   timeSeriesDeaths$Country.Region     <- NULL
109 | 
110 |   #printVerbose('After limiting to the focus country', timeSeriesInfections, timeSeriesDeaths, timeSeriesRecoveries, inputRecoveredSupplied, verbose)
111 | }
112 | 
113 | 
114 | names(timeSeriesInfections)[1] <- 'Region'
115 | names(timeSeriesDeaths)[1]     <- 'Region'
116 | if (inputRecoveredSupplied) {
117 |   names(timeSeriesRecoveries)[1] <- 'Region'
118 | }
119 | #printVerbose('Should always have one non-date column, and it should be simply called Region', timeSeriesInfections, timeSeriesDeaths, timeSeriesRecoveries, inputRecoveredSupplied, verbose)
120 | 
121 | rm(inputConfirmed, inputDeaths, inputRecovered)
122 | 
123 | # always aggregate over region column - sometimes this will do things, sometimes not.
124 | timeSeriesInfections <- regionAggregate(timeSeriesInfections)
125 | timeSeriesDeaths     <- regionAggregate(timeSeriesDeaths)
126 | if (inputRecoveredSupplied) {
127 |   timeSeriesRecoveries <- regionAggregate(timeSeriesRecoveries)
128 | }
129 | 
130 | #printVerbose('After aggregating over Region column', timeSeriesInfections, timeSeriesDeaths, timeSeriesRecoveries, inputRecoveredSupplied, verbose)
131 | 
132 | 
133 | 
134 | # test structural integrity of data
135 | test1 <- checkSameNumberOfRows(timeSeriesInfections, timeSeriesDeaths)
136 | test2 <- checkSameNumberOfCols(timeSeriesInfections, timeSeriesDeaths)
137 | 
138 | test3 <- checkNAs(timeSeriesInfections)
139 | test4 <- checkNAs(timeSeriesDeaths)
140 | 
141 | noErrors <- noErrors & test1 & test2 & test3 & test4
142 | 
143 | if (inputRecoveredSupplied) {
144 | 
145 |   test5 <- checkSameNumberOfCols(timeSeriesInfections, timeSeriesRecoveries)
146 |   test6 <- checkNAs(timeSeriesRecoveries)
147 | 
148 |   noErrors <- noErrors & test5 & test6
149 | 
150 | }
151 | 
152 | 
153 | if (noErrors) {
154 | 
155 |   # All tests passed successfully
156 |   
157 |   ## get date range 
158 |   dCols<-dateCols(timeSeriesInfections)
159 |   dates<-as.Date(colnames(timeSeriesInfections)[dCols], format = "%m.%d.%y")
160 |   
161 | 
162 |   ## generate recovery data if it doesn't already exist
163 |   if (!inputRecoveredSupplied) {
164 |     timeSeriesRecoveries <- recLag(timeSeriesInfections, timeSeriesDeaths, active = FALSE, ignore.deaths=ignore.deaths)
165 |   }
166 | 
167 |   # Standardise dataframes and compute active cases
168 |   std <- activeCases(timeSeriesInfections, timeSeriesDeaths, timeSeriesRecoveries)
169 |   
170 |   #printVerbose('Function activeCases complete', timeSeriesInfections, timeSeriesDeaths, timeSeriesRecoveries, inputRecoveredSupplied, verbose)
171 | 
172 |   # report to console countries that have been recLagged within activeCases()
173 |   if (nrow(std$failedRecovery)>0 & verbose) {
174 |     message('')
175 |     message(paste("There are", nrow(std$failedRecovery), "region(s) failing recovery data test and treated with recLag: "))
176 |     print(std$failedRecovery)
177 |     cat("\n\n")
178 |   }
179 | 
180 |   # exclude data where there are large errors in the infection and death cumulants
181 |   checkI <- cumulantCheck(std$tsI)
182 |   checkD <- cumulantCheck(std$tsD)
183 |   checkR <- cumulantCheck(std$tsR, tolerance = 0.5)
184 | 
185 |   cumSub <- checkI & checkD & checkR
186 |   if (sum(!cumSub)>10) stop("More than five suspect regions in this dataset.")
187 |   if (sum(!cumSub)>0 & verbose) {
188 |     message(paste("These regions will be completely excluded, due to failed cumulants:", sum(!cumSub)))
189 |     print(cbind(std$tsI[!cumSub, 1:2], checkI = checkI[!cumSub], checkD = checkD[!cumSub], checkR = checkR[!cumSub]))
190 |     cat("\n\n")
191 |   }
192 |   timeSeriesInfections <- std$tsI[cumSub,]
193 |   timeSeriesDeaths     <- std$tsD[cumSub,]
194 |   timeSeriesRecoveries <- std$tsR[cumSub,]
195 |   timeSeriesActive     <- std$tsA[cumSub,]
196 |   rm(checkI, checkD, checkR, cumSub)
197 | 
198 |   #printVerbose('After excluding regions which failed cumulants', timeSeriesInfections, timeSeriesDeaths, timeSeriesRecoveries, TRUE, verbose)
199 |   printVerbose('Finally...', timeSeriesInfections, timeSeriesDeaths, timeSeriesRecoveries, TRUE, verbose)
200 | 
201 |   if (countryName == 'Global') {
202 |     # create global aggregate row
203 |     timeSeriesInfections <- natAgg(timeSeriesInfections, aggName = "Global aggregate")
204 |     timeSeriesDeaths     <- natAgg(timeSeriesDeaths,     aggName = "Global aggregate")
205 |     timeSeriesRecoveries <- natAgg(timeSeriesRecoveries, aggName = "Global aggregate")
206 |     timeSeriesActive     <- natAgg(timeSeriesActive,     aggName = "Global aggregate")
207 | 
208 |     # Make continent aggregates
209 |     load("dat/Continents/continentData.RData")
210 |     timeSeriesInfections <- continentAgg(timeSeriesInfections, continentData)
211 |     timeSeriesDeaths     <- continentAgg(timeSeriesDeaths,     continentData)
212 |     timeSeriesRecoveries <- continentAgg(timeSeriesRecoveries, continentData)
213 |     timeSeriesActive     <- continentAgg(timeSeriesActive,     continentData)
214 | 
215 |   } else {
216 |     timeSeriesInfections <- natAgg(timeSeriesInfections, aggName = paste("National aggregate -", countryName))
217 |     timeSeriesDeaths     <- natAgg(timeSeriesDeaths,     aggName = paste("National aggregate -", countryName))
218 |     timeSeriesRecoveries <- natAgg(timeSeriesRecoveries, aggName = paste("National aggregate -", countryName))
219 |     timeSeriesActive     <- natAgg(timeSeriesActive,     aggName = paste("National aggregate -", countryName))
220 |   }
221 | 
222 |   ## Define menus
223 |   # get region names with 20 or more cases as of yesterday
224 |   ddNames <- timeSeriesInfections$Region[timeSeriesInfections[[ncol(timeSeriesInfections)-1]]>19]
225 |   ddReg        <- ddNames
226 |   names(ddReg) <- ddNames
227 |   
228 |   ## write data caches out
229 |   dir.create(paste0("dat/", countryName), showWarnings = FALSE) # if the directory doesn't exist, create it.
230 |   save(ddReg, ddNames, file = paste0("dat/",countryName,"/menuData.RData"))
231 |   save(timeSeriesInfections, timeSeriesDeaths, timeSeriesRecoveries, timeSeriesActive, dates, file = paste0("dat/",countryName,"/cacheData.RData"))
232 |   
233 |   load(paste0("dat/",countryName,"/estDeconv.RData"))
234 | 
235 |   # load dataList object
236 |   load("dat/dataList.RData")
237 | 
238 |   if (verbose) {
239 |     message('')
240 |     message(paste('These', length(ddNames), 'regions now have data:'))
241 |     print(ddNames)
242 |   }
243 |   
244 |   # append data to dataList
245 | 
246 |   dataList[[countryName]] <- list(timeSeriesInfections   = timeSeriesInfections,
247 |                                   timeSeriesDeaths       = timeSeriesDeaths,
248 |                                   timeSeriesRecoveries   = timeSeriesRecoveries,
249 |                                   timeSeriesActive       = timeSeriesActive,
250 |                                   dates                  = dates,
251 |                                   ddReg                  = ddReg,
252 |                                   ddNames                = ddNames,
253 |                                   cumulative.infections  = cumulative.infections,
254 |                                   undiagnosed.infections = undiagnosed.infections)
255 | 
256 |   # write datList back out
257 |   save(dataList, file = "dat/dataList.RData")
258 |   if (verbose) {
259 |     message('')
260 |     message("Complete")
261 |   }
262 | } else {
263 |   if (verbose) {
264 |     message('No data was saved')
265 |   }
266 | }
267 | 
268 | t2 = Sys.time()
269 | if (verbose) { 
270 |   print(t2 - t1)
271 | }
272 | 
273 | }
274 | 
275 | 
276 | printVerbose <- function(initialMessage,timeSeriesInfections, timeSeriesDeaths, timeSeriesRecoveries, inputRecoveredSupplied, verbose) {
277 |   if (verbose) {
278 |     message('')
279 |     message('----------------------------------')
280 |     message(initialMessage)
281 |     print(names(timeSeriesInfections)[1:4])
282 |     message(paste(toString(dim(timeSeriesInfections)), '- dimensions of infections'))
283 |     message(paste(toString(dim(timeSeriesDeaths)),     '- dimensions of deaths'))
284 |     if (inputRecoveredSupplied) {
285 |       message(paste(toString(dim(timeSeriesRecoveries)), '- dimensions of recoveries'))
286 |     }
287 |   }
288 | }
289 | 


--------------------------------------------------------------------------------
/functions.R:
--------------------------------------------------------------------------------
  1 | ## ---------------------------
  2 | ##
  3 | ## Script name: functions.R
  4 | ##
  5 | ## Purpose of script: Hold a bunch of functions for coronaRisk app
  6 | ##
  7 | ## Author: Ben Phillips
  8 | ##
  9 | ## Date Created: 2020-03-12
 10 | ##
 11 | ## Email: phillipsb@unimelb.edu.au
 12 | ##
 13 | ## ---------------------------
 14 | ##
 15 | ## Notes:
 16 | ##   
 17 | ##
 18 | ## --------------------------
 19 | ## load up the packages we will need 
 20 | 
 21 | ## ---------------------------
 22 | 
 23 | ## function definitions
 24 | 
 25 | 
 26 | checkSameNumberOfCols <- function(a, b) {
 27 |   test <- ncol(a)==ncol(b)
 28 |   if (!test) {
 29 |     print(paste0('ERROR: ', deparse(substitute(a)), ' and ', deparse(substitute(b)), ' do not have the same number of columns (', ncol(a), ' vs. ', ncol(b), ')'))
 30 |   }
 31 |   test
 32 | }
 33 | 
 34 | checkSameNumberOfRows <- function(a, b) {
 35 |   test <- nrow(a)==nrow(b)
 36 |   if (!test) {
 37 |     print(paste0('ERROR: ', deparse(substitute(a)), ' and ', deparse(substitute(b)), ' do not have the same number of rows (', nrow(a), ' vs. ', nrow(b), ')'))
 38 |   }
 39 |   test
 40 | }
 41 | 
 42 | checkNAs <- function(a) {
 43 |   test <- sum(is.na(a))==0
 44 |   if (!test) {
 45 |    print(paste0('ERROR: There are NAs somewhere in the ', deparse(substitute(a)), ' data: ', sum(is.na(a))))
 46 |  }
 47 |  test
 48 | }
 49 | 
 50 | 
 51 | # function to check that all data are (semi-strictly) increasing over time
 52 |   # x is dataframe in JHU format
 53 |   # tolerance is how large an error we can live with
 54 |   # returns vector of rows that meet inclusion criteria
 55 | cumulantCheck <- function(x, tolerance = 0.25){
 56 |   d <- as.matrix(x[, dateCols(x)])
 57 |   rcFun <- function(y){
 58 |     out <- diff(y)/y[-length(y)]
 59 |     out[is.nan(out)] <- 0
 60 |     sum(out < (-tolerance) & y[-length(y)] > 100) # ignore bumps in early reporting
 61 |   }
 62 |   rowCheck <- apply(d, 1, rcFun)
 63 |   rowCheck == 0
 64 | }
 65 | 
 66 | # function to catch when regions stop reporting recoveries
 67 | recoveryCheck <- function(recoveries, infections, tolerance = 7){
 68 |   dR <- as.matrix(recoveries[, dateCols(recoveries)])
 69 |   dI <- as.matrix(infections[, dateCols(infections)])
 70 |   diffR <- dR-cbind(rep(0, nrow(dR)), dR[,-ncol(dR)]) # change in recoveries 
 71 |   diffI <- dI-cbind(rep(0, nrow(dI)), dI[,-ncol(dI)]) # change in infections
 72 |   test <- diffR == 0 & diffI != 0 & dR != dI # no change in recoveries, and change in infections, and recoveries not equal to infections
 73 |   test <- test[, (ncol(test)-9): ncol(test)] # last ten days
 74 |   apply(test, 1, sum) > tolerance
 75 | }
 76 | 
 77 | 
 78 | # Function to load data and standardise names, columns and so on
 79 | loadData <- function(path){
 80 |   d <- read.csv(file = path, stringsAsFactors = FALSE) # read data
 81 |   names(d) <- gsub(pattern = "_", replacement = ".", x = names(d))
 82 |   d <- d[, colnames(d) %in% c("Country.Region", "Province.State") | dateCols(d)] # throw away unwanted columns
 83 |   names(d)[dateCols(d)] <- gsub(pattern = "X", replacement = "", x = names(d)[dateCols(d)])
 84 |   d <- d[c(2, 1, 3:ncol(d))] # reorder so country.region is first
 85 |   # remove unwanted rows
 86 |   unwanted <- c("Diamond Princess", "MS Zaandam") # cruise ships
 87 |   d <- subset(d, !(d$Country.Region %in% unwanted | d$Province.State %in% unwanted))
 88 |   # rename Burma
 89 |   d$Country.Region[d$Country.Region=="Burma"] <- "Myanmar"
 90 |   d <- emancipate(d) # elevate semi-autonomous colonies to countries for reporting
 91 |   d
 92 | }
 93 | 
 94 | 
 95 | # Takes infection, death, and recovrey time series and converts to active cases
 96 |   # returns standardised dataframes
 97 | activeCases <- function(infections, deaths, recoveries){
 98 |   ssCol <- dateCols(infections) # get date columns
 99 |   test1 <- checkSameNumberOfRows(infections,deaths)
100 |   test2 <- checkSameNumberOfRows(infections,recoveries)
101 |   if (test1) {
102 |     # Standardise order
103 |     infections <- infections[order(infections$Region),]
104 |     deaths     <- deaths[order(deaths$Region),]
105 |     recoveries <- recoveries[order(recoveries$Region),]
106 |     orderTest <- sum(!(infections$Region == deaths$Region & infections$Region == recoveries$Region)) != 0
107 |     if (orderTest) stop("Region labels do not align")
108 |     # check for countries with inadequate reporting of recoveries, and apply recLag estimation
109 |     recCheck <- recoveryCheck(recoveries, infections, tolerance = 7)
110 |     recoveries[recCheck,] <- recLag(infections[recCheck,], deaths[recCheck,], active = FALSE)
111 |     # subset to case data
112 |     infMat <- infections[,ssCol]
113 |     deathMat <- deaths[,ssCol]
114 |     recMat <- recoveries[,ssCol]
115 |     # generate active case data frame
116 |     active <- infections
117 |     active[,ssCol] <- infMat - deathMat - recMat
118 |     # return standardised data frames
119 |     list(tsI = infections, 
120 |          tsD = deaths, 
121 |          tsR = recoveries, 
122 |          tsA = active, 
123 |          failedRecovery = infections[recCheck, 1:2])
124 |   } else {
125 |     stop('Error - activeCases failed')
126 |   }
127 | }
128 | 
129 | # Adjusts cumulative infections to get active cases
130 |   # cumulative infections and deaths, ttr = time to recovery
131 |   # if active = false, returns recoveries rather than active cases
132 | recLag <- function(infections, deaths, datCols = dateCols(infections), ttr = 22, active = TRUE, ignore.deaths=FALSE){
133 |   matI<-as.matrix(infections[, datCols])
134 |   matD<-as.matrix(deaths[, datCols])
135 |   if (ignore.deaths) {
136 |     matA <- matI #calculate 'removed' instead of 'recovered'
137 |   } else {
138 |     matA<-matI-matD #remove deaths
139 |   }
140 |   if (length(ttr)==1){
141 |     matR <- cbind(matrix(0, nrow = nrow(matA), ncol = 22), matA[, -((ncol(matA)-21):ncol(matA)), drop = FALSE]) # "recovered"
142 |   } else {
143 |     if (length(ttr) != nrow(infections)) stop("ttr vector is not of correct length")
144 |     matR <- matrix(NA, nrow = nrow(matI), ncol = ncol(matI))
145 |     for (ii in 1:nrow(matR)){
146 |       matR[ii,] <- c(rep(0, ttr[ii]), matA[ii, -((ncol(matA)-(ttr[ii]-1)):ncol(matA)), drop = FALSE]) # lagged by theta days
147 |     }
148 |   }
149 |   matA <- matA - matR
150 |   if (ignore.deaths) {
151 |     matR <- matR - matD #recovered = 'removed' minus deaths
152 |   }
153 |   if (active) {
154 |     out <- data.frame(infections[,!datCols], matA) # "active" cases
155 |   } else {
156 |     out <- data.frame(infections[,!datCols], matR) # "recovered"
157 |   }
158 |   colnames(out) <- colnames(infections)
159 |   out
160 | }
161 | 
162 | # given time series data on recoveries, establishes optimum value for time to recovery in recLag function
163 | recLagOptim <- function(infections, deaths, recoveries){
164 |   errorFunc <- function(theta, infections, deaths, recoveries){
165 |     activeTrue <- infections - deaths - recoveries
166 |     estR <- infections - deaths # non-deaths
167 |     estR <- c(rep(0, theta), estR[-((length(estR)-(theta-1)):length(estR))]) # lagged by theta days
168 |     activeEst <- infections - deaths - estR
169 |     ss <- activeTrue!=0 & activeEst>0
170 |     sum(log(activeEst[ss]/activeTrue[ss])^2)/sum(ss) # mean squared error (log transformed data)
171 |   }
172 |   out <- round(optimize(f = errorFunc, interval = c(10, 32), infections=infections, deaths=deaths, recoveries=recoveries)$minimum, 0) # get the optimum
173 |   if (out==32) out <- 23 # catch cases where optimize has crashed
174 |   out
175 | }
176 | 
177 | 
178 | 
179 | 
180 | # growth rate
181 | growthRate <- function(cases, inWindow=10){
182 |   nn <- length(cases)
183 |   ss <- (nn - inWindow + 1):nn
184 |   rate <- numeric(length(ss))
185 |   rate[ss] <- 100 * (cases[ss] - cases[ss-1]) / cases[ss-1]
186 | }
187 | 
188 | 
189 | 
190 | # aggregates results to relevant region (regionCol allows to specify whether Provinc.State, or COuntry.Region)
191 | regionAgg<-function(x, regionCol, regionName = "Region"){
192 |   xSelect<-x[, dateCols(x)]
193 |   out <- aggregate(xSelect, by = list(regionCol), FUN = sum)
194 |   names(out)[1] <- regionName
195 |   out
196 | }
197 | 
198 | # same as above but on column labelled 'Region'
199 | regionAggregate <- function(x) {
200 |   xSelect <- x[, dateCols(x)]
201 |   out <- aggregate(xSelect, by = list(Region = x$Region), sum)
202 |   out
203 | }
204 | 
205 | 
206 | # calculates a national aggregate and appends to dataframe
207 |   # must be called after regionAgg
208 | natAgg <-function(tsDF, aggName = "National aggregate"){
209 |   cAgg <- colSums(tsDF[,-1])
210 |   dim(cAgg)<-c(1, length(cAgg))
211 |   cAgg <- data.frame(Region = aggName, cAgg, stringsAsFactors = FALSE)
212 |   colnames(cAgg) <- colnames(tsDF)
213 |   rbind(cAgg, tsDF)
214 | }
215 | 
216 | # aggregates to ocontinents
217 |   # called after regionAgg and natAgg takes case data frame and continent datafram
218 |   # returns case data fram with continents in rows 2-7
219 | continentAgg <- function(tsDF, contData){
220 |   ts <- subset(tsDF, tsDF$Region %in% contData$Country)
221 |   ts$Region <- contData$Continent[match(ts$Region, contData$Country)]
222 |   ts <- regionAgg(ts, regionCol = ts$Region)
223 |   rbind(tsDF[1,], ts, tsDF[-1,])
224 | }
225 | 
226 | # calculates the curve flatenning index.
227 |   # it is the second derivative of logA wrt t (the change in growth rate) divided by first differential (the current growth rate).
228 | cfi <- function(active){
229 |   lnact <-log(active)
230 |   cfiInd <- -diff(diff(lnact))/abs(diff(lnact)[-1])
231 |   cfiInd[abs(cfiInd)>10]<-NA # remove crazy values associated with changed test/diagnosis
232 |   cfiInd
233 | }
234 | 
235 | # estimates detection rate based on assumptions about caseFatalityRatio, ttd
236 | detRate<-function(infd, deaths, caseFatalityRatio = 3.3, ttd=17, window=5, pointEst = TRUE){
237 |   obs<-c(rep(NA, window), diff(infd, window)) # observed new cases
238 |   deathDiff<-diff(deaths, window) # observed new deaths
239 |   deathDiff[deathDiff<0] <- 0 # catch negative deaths (which shouldn't exist, but...)
240 |   expd<-deathDiff/(caseFatalityRatio/100) #expected new cases given caseFatalityRatio
241 |   expd<-expd[-(1:(ttd-window))]
242 |   expd<-c(expd, rep(NA, ttd))
243 |   detRate<-obs/expd
244 |   detRate[detRate==0]<-NA
245 |   detRate[is.infinite(detRate)]<-NA
246 |   if (pointEst) {
247 |     out<-mean(tail(na.omit(detRate), 5), na.rm = TRUE) # take mean over last 5 observations
248 |     if (is.nan(out)) return(NA)
249 |     if (out>1) out <- 1
250 |   } else {
251 |     out <- detRate
252 |     out[is.nan(out)] <- NA
253 |     out[out>1] <- 1
254 |     }
255 |   out
256 | }
257 | 
258 | # Simple projection based on growth over last inWindow days
259 |   # returns extended plotting data
260 | projSimple<-function(rawN, rawTime, inWindow=10, extWindow=10, timeVaryingGrowth = FALSE){
261 |   nn <- length(rawN)
262 |   ss <- (nn-inWindow+1):nn
263 |   x <- c(rawTime[ss], rawTime[nn]+1:extWindow)
264 |   if (sum(rawN[ss]==0)>=(length(rawN[ss])-2)){ #if there are two or fewer non-zero data points...
265 |     yPieces <- rep(0, length(x))
266 |     y <- data.frame(fit = yPieces, lwr = yPieces, upr = yPieces)
267 |     return(list(lDat = data.frame(dates = x, y), date_at_peak = NULL, value_at_peak = -99, doubling_time = Inf))
268 |   }
269 |   lnN <- log(rawN[ss]) 
270 |   lnN[is.infinite(lnN)]<-NA
271 |   tIn <- rawTime[ss]
272 |   if (timeVaryingGrowth){
273 |     mFit <- lm(lnN~poly(tIn, 2))
274 |     fit_based_on_integers_instead_of_dates <- lm(lnN ~ I(ss) + I(ss^2))
275 |     intercept <- summary(fit_based_on_integers_instead_of_dates)$coefficients[1,1]
276 |     poly1     <- summary(fit_based_on_integers_instead_of_dates)$coefficients[2,1]
277 |     poly2     <- summary(fit_based_on_integers_instead_of_dates)$coefficients[3,1]
278 |     if (poly2 != 0){
279 |       date_at_peak <- tail(tIn,n=1) + (round(-poly1 / (2*poly2)) - nn)
280 |       value_at_peak <- exp(intercept)*exp(poly1^2/(2*(-poly2)))*exp(poly2*(poly1/(2*poly2))^2)
281 |     
282 |       if (poly2 > 0 | date_at_peak > max(x)) {
283 |        value_at_peak <- NULL
284 |       }
285 |       if (date_at_peak < min(x)){
286 |         date_at_peak <-NULL
287 |       }
288 |     } else if (poly1 == 0 & intercept == 0) {
289 |       value_at_peak <- -99
290 |       date_at_peak <-NULL
291 |     } else {
292 |       value_at_peak <- -99
293 |       date_at_peak <- NULL
294 |     }
295 |   } else {
296 |     mFit <- lm(lnN~tIn)
297 |     r <- coefficients(mFit)[2]
298 |     doubling_time <- log(2)/r
299 |   }
300 |   extFit <- predict(mFit, newdata = list(tIn = x), interval = "confidence")
301 |   y <- exp(extFit)
302 |   if(timeVaryingGrowth) {
303 |     list(lDat = data.frame(dates = x, y), date_at_peak = date_at_peak, value_at_peak = value_at_peak)
304 |   } else {
305 |     list(lDat = data.frame(dates = x, y), doubling_time = doubling_time)
306 |   }
307 | 
308 | }
309 | 
310 | # to identify the date columns in ts dataframes
311 | dateCols<-function(x){
312 |   grepl(pattern = "\\d.\\d", x = colnames(x))
313 | }
314 | 
315 | # To subset time series data and aggregate totals
316 | tsSub <- function(x, subset){
317 |   xSub<-x[subset, dateCols(x)]
318 |   colSums(xSub)
319 | }
320 | 
321 | # Elevates semi-autonomous colonies to countries for reporting
322 | emancipate <- function(timeSeriesDataFrame, withHead = TRUE){
323 |   colonials <- timeSeriesDataFrame$Country.Region %in% c("Netherlands", "United Kingdom", "France", "Denmark")
324 |   province <-  timeSeriesDataFrame$Province.State != ''
325 |   ss <- colonials & province
326 |   if (withHead) {
327 |     timeSeriesDataFrame$Country.Region[ss] <- paste0(timeSeriesDataFrame$Province.State[ss], " (", timeSeriesDataFrame$Country.Region[ss], ")")
328 |   } else {
329 |     timeSeriesDataFrame$Country.Region[ss] <- timeSeriesDataFrame$Province.State[ss]
330 |   }
331 |   timeSeriesDataFrame$Province.State[ss] <- ""
332 |   timeSeriesDataFrame
333 | }
334 | 
335 | # Removes the Ruby Princess accounting that drops 189 cases into NSW on 3rd July
336 | fixNSW <- function(timeSeriesDataFrame){
337 |   rr <- timeSeriesDataFrame$Country.Region=="Australia" & timeSeriesDataFrame$Province.State=="New South Wales"
338 |   getJumpCol <- which(names(timeSeriesDataFrame)=="7.3.20")
339 |   timeSeriesDataFrame[rr,(getJumpCol):ncol(timeSeriesDataFrame)] <- timeSeriesDataFrame[rr,(getJumpCol):ncol(timeSeriesDataFrame)]-189
340 |   timeSeriesDataFrame
341 | }
342 | 


--------------------------------------------------------------------------------
/doc/about.Rmd:
--------------------------------------------------------------------------------
  1 | ---
  2 | title: "About"
  3 | author: "Ben Phillips"
  4 | date: "30/03/2020"
  5 | output: html_document
  6 | ---
  7 | 
  8 | This app was built to give people a sense of how the coronavirus epidemic is progressing in each country.  The initial impetus was to move the conversation past daily updates on case numbers, and towards the near future.  The hope was that doing so might help move us past the paralysis of daily shock, and towards important action.  
  9 | 
 10 | As action is taken, the app gives us a sense of how well our actions are working.  Difficult decisions have been made at all levels, from the individual to the nation, and it is important that we get feedback on those decisions as soon as possible.
 11 | 
 12 | Given how fast the epidemic was progressing, the app was built in a hurry at the University of Melbourne on the 12 March 2020.  It has been continually refined since then, and work continues (see acknowledgements).  The code is available [here](https://github.com/benflips/nCovForecast) and contributions/suggestions are welcome.  If you have data that you would like to see represented here, please get in contact. 
 13 | 
 14 | The app relies on relatively simple heuristics rather than comprehensive analyses.  The emphasis has been on speed, and continually updated data, rather than sophisticated analyses.  Sophisticated analyses will likely come in time.
 15 | 
 16 | ## Methods
 17 | 
 18 | ### Raw case numbers
 19 | 
 20 | Our data is updated daily.  Our global data come from the [Johns Hopkins University dataset](https://github.com/CSSEGISandData/COVID-19).  Our Indian data comes from [Ministry of Health and Family Welfare data](http://mohfw.gov.in), and are [compiled by a team from Panjab university](https://github.com/vipinbhatnagar/covid19).  Our German data comes from the [Robert Koch Institut](https://www.rki.de/EN), and [the csv is available here](https://www.arcgis.com/home/item.html?id=f10774f1c63e40168479a1feb6c7ca74).  Our Belgian, Colombian, Haitian, Italian, Japanese, Peruvian, and Swiss data come via the [Covid-19 Data Hub](https://covid19datahub.io). Belgian data is from [Epistat, Belgian Infectious Diseases](https://epistat.sciensano.be).  Colombian data is from [Ministerio de Salud y Protección Social de Colombia](https://datos.gov.co).  Haitian data is from the Ministry of Public Health and Population of Haiti.  Italian data is from [Istituto Nazionale di Statistica](https://istat.it).  Japanese data is from the Japan COVID-19 Bulletin Board.  Peruvian data is from COVID-19 Peru Data Repository.  Swiss data is from [Swiss Federal Statistical Office](https://bfs.admin.ch).  Our Afghan and Brazilian data come via the R package [covidregionaldata](https://cran.r-project.org/web/packages/covidregionaldata/).  Afghan data is from [Afghanistan: Coronavirus(COVID-19) Subnational Cases](https://data.humdata.org/dataset/afghanistan-covid-19-statistics-per-province).  Brazilian data is from [Número de casos confirmados de COVID-19 no Brasil](https://github.com/wcota/covid19br).
 21 | 
 22 | These data report for each country the cumulative number of infections ($I_t$), number of deaths ($D_t$), and (for most countries) number of recoveries ($R_t$).  The raw case numbers we report are simply a report of the JHU raw numbers for the selected country at the current date.  We also provide aggregate tallies for each continent, and the world.  Country to continent associations (for aggregating data to continent) were modified from [this file](https://github.com/dbouquin/IS_608/blob/master/NanosatDB_munging/Countries-Continents.csv).  If a region is missing from the app it is either because the region is yet to reach 20 confirmed cases, or because there are currently serious issues with the data (we exclude regions when cumulative case numbers decrease by 25% between days).
 23 | 
 24 | ### Current active cases
 25 | 
 26 | Active cases ($A_t$) are simply $A_t = I_t-D_t-R_t$ where $t$ denotes time.  As of 23 March, however, JHU no longer report recoveries in the US and Canada at State/Province level, and recovery data at State level are not available for India.  As a consequence, for these countries. we have had to assume that active cases take 22 days to resolve.  Thus, $A_t = I_t-D_t-(I_{t-22}-D_{t-22})$.
 27 | 
 28 | ### Growth models and forecast of active cases
 29 | 
 30 | We are interested in knowing how the number of active cases is going to change in the near term.  We provide two alternative growth models.  The growth dynamics of epidemics are complex, but we make the simplifying assumption that the epidemic is a long way from population saturation (i.e. that there is a ready supply of susceptible hosts) such that simple exponential growth provides a reasonable short-term approximation.  The first model -- the "constant growth" model -- assumes a constant growth rate, in which case active cases follow an exponential growth model such that $E(A_t) = A_0e^{rt}$, where $A_0$ is the initial number of active cases, and $r$ is the (constant) intrinsic growth rate.  The second model -- "time-varying growth" -- assumes that the growth rate changes linearly over time.  Empirically, linear change in growth is what we have been observing in this epidemic as populations enact physical distancing and quarantine measures.  Under this model, growth follows a Gaussian function such that $E(A_t) = A_0e^{r_0t+\frac{b}{2}t^2}$.  Here, $r_0$ is the initial growth rate, and $b$ is the rate of change in growth rate over time.
 31 | 
 32 | To fit both models, we take the natural logarithm of both sides, yielding $\ln A_t = rt + \ln A_0$ (constant growth) and $\ln A_t = \frac{b}{2}t^2+rt + \ln A_0$ (time-varying growth).  This shows us that we can fit a simple linear regression of $\ln A_t$ against $t$ in the constant growth scenario, and a quadratic function in the case of the time-varying model.  
 33 | 
 34 | These fits give us an estimate of intrinsic growth rate, $r$, or $r(t)$.  
 35 | 
 36 | We fit each model to the last $n$ days of $A_t$ data (where $n$ is determined by the user with the input slider) and extrapolate the fitted model to capture ten day into the future from now.  Fitting and extrapolation is effected with the log-transformed model (lower plot on "10-day forecast" tab, with 95% confidence intervals) and the log of expected active case numbers is back-transformed to the original scale to produce the top plot on the "10-day forecast" tab.  We provide a larger number of days for fit input for the time-varying growth model because this model estimates an additional parameter, so requires more data.
 37 | 
 38 | When public health interventions are rapidly changing the growth rate, this can be seen as deviations from the expected straight line on the log-plot.  In these situations, when growth rate is declining rapidly (the curve is flattening), forecasts from the constant growth model (averaging growth over the last ten days) will be biased upwards.  By altering the slider you can adjust the window over which growth rate is averaged, so you can get a sense of how recent shifts are affecting the forecast.  The time-varying growth rate forecast should be less sensitive to changes in $n$, and is the better model when growth rates are changing in a steady linear manner.
 39 | 
 40 | ### Doubling time
 41 | 
 42 | Under the constant growth model, the doubling time ($t_d$) is an intuitive measure of how fast a population is growing.  It reports the number of days for the population to double in size and is calculated by setting $A_t/A_0 = 2$, yielding $t_d = \frac{\ln 2}{r}$.  We calculate this directly from the previous estimate of $r$.
 43 | 
 44 | ### Detection 
 45 | 
 46 | One of the key sources of uncertainty in this pandemic has been undetected cases. Covid-19 has been quite a sneaky pathogen, and surveillance has often not been adequate, often resulting in a large number of infections in a country before it is noticed.  We were interested to know how bad this problem might be for each country; deaths will be a function of the true number of cases, not the reported number.
 47 | 
 48 | Not all cases are detected and so the reported number of active cases is almost always lower than the true number of cases. There are two classes of non-detected cases: 
 49 | 
 50 | 1. Those that will be diagnosed soon ("Undiagnosed").  Individuals that are infected but yet to present symptoms, and;
 51 | 2. Those that will never be diagnosed ("Undetected").  Individuals that present with mild symptoms, or are missed by surveillance.
 52 | 
 53 | The first class is caused by a delay between infection and diagnosis. However, if we know something about the length of this delay then we can use this information to inflate the observed case numbers to produce estimates of the number of undiagnosed cases. The most important determinant of the delay is the incubation period of the infection. This is known to be highly variable but can be modelled using a probability distribution, which is called the incubation distribution.  Information about the incubation distribution exists from external sources. It is known that the average incubation period is 5-6 days and that 95% of infections have an incubation period of no more than 12-13 days. In our model, a log-normal distribution incorporating these features is used to model the incubation distribution. Given this model for the incubation distribution, the daily diagnosis counts reflect a larger and unknown number of Covid-19 infections in the population, aggregated with the incubation distribution. Using the observed diagnosis counts, we can therefore estimate the unknown number of Covid-19 infections by disaggregating the infection numbers from the incubation distribution. This process is known as statistical deconvolution, also called back-projection or back-calculation. From a technical perspective, the method is a non-negatively constrained linear Poisson regression analysis, where the $y$-variable is the observed diagnosis counts and the linear model is the linear convolution of the mean infection numbers and the incubation distribution. This is a non-standard analysis but reliable model fitting algorithms exist and are incorporated here.  We use these to estimate undiagnosed cases, $U_t$.
 54 | 
 55 | The second class of non-detections, "Undetected" cases, are cases that are completely missed by surveillance.  To estimate these, we use a heuristic that assumes: that deaths do not go undetected, that there is community transmission (and a closed population), that there is a case fatality ratio, $f$, for symptomatic cases (set by the user with a slider; lower numbers will cause our detection estimate to be lower), that detection is constant, and that there is a fixed time (here assumed to be 17 days) between onset and death.  Observed new cases in the five days to time $t$ are calculated as $N_t = A_t-A_{t-5}$, and under the previous assumptions, the expected number of new cases $\hat{N_t} = (D_{t+17}-D_{t+12})/f$ (Our choice of a five day window for making this estimate reflects our interest in estimating detection early, when there are still few cases.  A five-day window typically catches at least one death.)  We then estimate detection probability, $p(t)=N_t/\hat{N_t}$.  We average $p(t)$ over $t$ to estimate the total number of active symptomatic cases $T_t = A_t/p$, shown on the first tab of the site. Because detection changes over time also, as of 14 April we take this average over only the last five days of detection observations (typically 17-22 days in the past) to give the most up-to-date estimate for each region.  We also show detection over time $p(t)$ for the selected region as a plot on the forecast tab.
 56 | 
 57 | The large number of assumptions behind this last method for estimating detection mean we should be very cautious with it, but it suffices to show that detection probability in some countries is very high (i.e. relatively few undetected cases), while in others it is very low indeed (many undetected cases).  In countries with many undetected cases, there are many more deaths than there should be given reported case numbers.  One obvious source of bias in both estimates is imported cases.  When there is movement between countries and large differences in caseload, countries with small numbers of cases will have a large proportion of imported cases.  These will cause us to estimate a larger number of non-detections than there are.
 58 | 
 59 | Detection is our weakest estimate in this app; please take it as indicative only. You can expect the estimates to have large uncertainty ranges, however, they do provide working estimates that give a sense of scale. At present we have not included uncertainty ranges such as confidence intervals but may consider this in future implementations if feasible. It would require computationally intensive methods such as bootstrapping. We note also that the estimates of undiagnosed infections can also be used as an alternative way to forecast new cases, since future diagnoses depend on past infections. This is another area of possible future development. 
 60 | 
 61 | ### Growth rate
 62 | 
 63 | Public health interventions are firmly aimed at reducing virus transmission and so reducing the growth in the number of active cases.  The earliest indications of intervention success will manifest in lowered growth rates (around ten days after the intervention is made in the case of covid-19).  The per-capita growth rate can be calculated each day as $G_t = \frac{A_t-A_{t-1}}{A_{t-1}}$.  The top plot on the "Growth rates" tab shows this rate as a percentage, $100\times G_t$, expressing population growth in more broadly understood terms; as a compound interest rate.  The plot uses a three-day moving average.
 64 | 
 65 | ### Curve flattening
 66 | 
 67 | To capture the idea of "flattening the curve", we made an index ($C_t$) that captures how growth rate changes over time.  Conceptually, we were thinking of the second differential of $\ln A_t$ (scaled against the first differential), but the resultant index is a bit of a mouthful:
 68 | $$
 69 | C_t = -\frac{(\ln A_t-\ln A_{t-1}) - (\ln A_{t-1}-\ln A_{t-2})}{\sqrt{(\ln A_{t-1}-\ln A_{t-2})^2}}
 70 | $$
 71 | Which could be simplified, but has been written so as to help you see that it is the change in growth rate divided by the magnitude of the growth rate, and then the whole multiplied by negative 1 so that positive values are "good", negative values "bad".  It should rightly be thought of as an index of "suppression" rather than an index of "curve-flattening".
 72 | 
 73 | The second plot on the "Growth rate and curve-flattening" tab places a smoother through this index across all time for which we have data.  Times when countries are reducing their growth rate are indicated by the smoother sitting in positive territory.
 74 | 
 75 | The denominator in this index does make it very sensitive as growth rates come close to zero.
 76 | 
 77 | ## The University
 78 | 
 79 | For more information about University of Melbourne's COVID-19 research visit <a href="https://pursuit.unimelb.edu.au/" target="_blank">Pursuit</a>, and the <a href="https://www.doherty.edu.au/our-work/institute-themes/viral-infectious-diseases/emerging-infections/2019-novel-coronavirus/about-2019-novel-coronavirus" target="_blank">Doherty Institute</a>.
 80 | 
 81 | If you would like to support the University's work, please consider giving to our [covid-19 emergency appeal](https://secure.alumni.unimelb.edu.au/s/1182/giving/form.aspx?sid=1182&gid=1&pgid=19033&cid=27067&DIDS=676.677&BLEDIT=&utm_source=WEB&utm_medium=believe&utm_campaign=covid19emergencyappeal).
 82 | 
 83 | 
 84 | ### Media enquiries
 85 | 
 86 | [University of Melbourne Newsroom](https://about.unimelb.edu.au/newsroom), Phone +61 3 8344 4123; Email [media-enquiries@unimelb.edu.au](<mailto:media-enquiries@unimelb.edu.au?subject=covid19forecast.science.unimelb.edu.au>)
 87 | 
 88 | 
 89 | ## Acknowledgements
 90 | 
 91 | First, to the people on the front line who are collecting these data.  You are a legion of brave and selfless people.  We hope this work has in some diffuse way made your job easier.  Second, a big thanks to the team at Johns Hopkins University.  Their early decision to make these data open access, and their ongoing commitment to curating a large dynamic dataset has provided a critical resource for helping the world understand and manage this pandemic.
 92 | 
 93 | The app was conceived by [Ben Phillips](https://findanexpert.unimelb.edu.au/profile/652103-ben-phillips) at the University of Melbourne and has been greatly improved by Drs Melissa Makin and Uli Felzmann (University of Melbourne).  These three are the core development and maintenance team.  State-level data for India are curated by Drs Vipin Bhatnagar and Jyoti Tripathi (Panjab University).  The app has been improved by contributions from Matteo Tomasini (Michigan State University); Sergio Valqui (UoM); and several other contributors on github.  Ian Marschner (University of Sydney) provided deconvolution code for estimating undiagnosed infection numbers. Valuable feedback has come from Daniel Bolnick (U Connecticut), Alison Hill (Harvard), Amy Hurford (Memorial University of Newfoundland), Taegan Calnan (Charles Darwin Uni), the Spatial Ecology and Evolution Lab (Melbourne), and a suite of people on the internet who are too numerous to name (but we thank them anyway).
 94 | 
 95 | Thanks to Defne Şahin from the University of Western Australia for the Turkish translation.  Thanks also to the following, who are all from the University of Melbourne: Sanjana Ratan for the Hindi translation, Uli Felzmann for the German translation, Sergio Valqui for the Spanish translation, and Kostantin Borovkov for the Russian translation.
 96 | 
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102 | 
103 | 


--------------------------------------------------------------------------------
/translations/translations.json:
--------------------------------------------------------------------------------
  1 | {
  2 |   "languages": ["en","de","tr","hi","es", "ru"],
  3 |   "translation": [
  4 | {"en": "As of",
  5 |  "de": "Datenstand vom",
  6 |  "tr": "",
  7 |  "hi":"इस तारीख तक,",
  8 |  "es": "Hasta el Día",
  9 |  "ru": "По состоянию на"
 10 | },
 11 | {"en": "Select a language:",
 12 |  "de": "Sprache auswählen:",
 13 |  "tr": "Dil seçiniz:",
 14 |  "hi": "भाषा चुनें",
 15 |  "es": "Seleccione un lenguaje",
 16 |  "ru": "Выберите лангуаже:"
 17 | },
 18 | {"en": "Case fatality ratio:",
 19 |  "de": "Sterblichkeitsrate",
 20 |  "tr": "Vaka vefat oranı:",
 21 |  "hi": "मामला घातक अनुपात",
 22 |  "es": "Tasa de mortalidad",
 23 |  "ru": "Коэффициент летальности:"
 24 | },
 25 | {"en": "Fit window:",
 26 |  "de": "Kurve anpassen:",
 27 |  "tr": "Pencereyi ayarla:",
 28 |  "hi": "इस सीमा में:",
 29 |  "es": "Días para ajustar la proyección",
 30 |  "ru": "Установите окно:"
 31 | },
 32 | {"en": "Select global or country:",
 33 |  "de": "Entweder 'Global' or Land auswählen:",
 34 |  "tr": "Seçiniz (Dünya çapında veya ülke):",
 35 |  "hi": "सार्वत्रिक या देश चुनें:",
 36 |  "es": "Seleccione Global o País",
 37 |  "ru": "Выберите (по всему миру или по стране)"
 38 | },
 39 | {"en": "Skip to:",
 40 |  "de": "Gehe zu:",
 41 |  "tr": "Şuraya git:",
 42 |  "hi": "इसे देखे:",
 43 |  "es": "Ir a:",
 44 |  "ru": "Перейти к:"
 45 | },
 46 | {"en": "Please select a country or region...",
 47 |  "de": "Bitte Land oder Region auswählen...",
 48 |  "tr": "Lütfen ülke ya da bölge seçiniz...",
 49 |  "hi": "कृपया एक देश या राज्य चुनें...",
 50 |  "es": "Por favor seleccione un país o región...",
 51 |  "ru": "Пожалуйста, выберите страну или регион ..."
 52 | },
 53 | {"en": "Total infections",
 54 |  "de": "Gesamtzahl der Infizierten",
 55 |  "tr": "Toplam vaka sayısı ",
 56 |  "hi": "कुल संक्रमण",
 57 |  "es": "Total infectados",
 58 |  "ru": "Общее количество зараженных"
 59 | },
 60 | {"en": "Deaths",
 61 |  "de": "Todesfälle",
 62 |  "tr": "Toplam vefat sayısı ",
 63 |  "hi": "कुल मौतें",
 64 |  "es": "Fallecidos",
 65 |  "ru": "Всего смертей"
 66 | },
 67 | {"en": "Active cases",
 68 |  "de": "Aktive Fälle",
 69 |  "tr": "Aktif vakalar",
 70 |  "hi": "कुल कोरोनावाइरस केस",
 71 |  "es": "Casos activos",
 72 |  "ru": "Активные случаи заболевания"
 73 | },
 74 | {"en": "Confirmed active cases",
 75 |  "de": "Labortechnisch bestätigte aktive Fälle",
 76 |  "tr": "Doğrulanmış aktif vaka sayısı ",
 77 |  "hi": "कुल कोरोनावाइरस केस",
 78 |  "es": "Casos activos confirmados",
 79 |  "ru": "Лабороторно подтвержденные активные случаи"
 80 | },
 81 | {"en": "Estimated peak",
 82 |  "de": "Erwarteter Höchstwert",
 83 |  "tr": "Beklenen pik ",
 84 |  "hi": "अनुमानित शिर",
 85 |  "es": "Pico estimado",
 86 |  "ru": "Ожидаемый пик"
 87 | },
 88 | {"en": "Best fit",
 89 |  "de": "Beste Approximation",
 90 |  "tr": "En uygun fit",
 91 |  "hi": "श्रेष्ठ योग्य",
 92 |  "es": "Mejor ajuste",
 93 |  "ru": "Наилучшая аппроксимация"
 94 | },
 95 | {"en": "New cases",
 96 |  "de": "Neue Fälle",
 97 |  "tr": "Yeni vaka sayısı ",
 98 |  "hi": "नई केस ",
 99 |  "es": "Casos nuevos",
100 |  "ru": "Новые случаи"
101 | },
102 | {"en": "Daily deaths",
103 |  "de": "Tägliche COVID19-Todesfälle",
104 |  "tr": "Günlük vefat sayısı ",
105 |  "hi": "दैनिक मौतें",
106 |  "es": "Fallecidos por día",
107 |  "ru": "Ежедневные смерти"
108 | },
109 | {"en": "Date",
110 |  "de": "Datum",
111 |  "tr": "Tarih ",
112 |  "hi": "तारीख",
113 |  "es": "Fecha",
114 |  "ru": "Дата"
115 | },
116 | {"en": "Detection",
117 |  "de": "Detektion",
118 |  "tr": "Tespit",
119 |  "hi": "खोज",
120 |  "es": "Detección",
121 |  "ru": "Обнаружение"
122 | },
123 | {"en": "Cases successfully detected %",
124 |  "de": "Fälle erfolgreich entdeckt %",
125 |  "tr": "Tespitinde başarılı olunmuş vaka sayısı (%) ",
126 |  "hi": "मामलों का सफलतापूर्वक पता चला (%)",
127 |  "es": "Casos diagnosticados %",
128 |  "ru": "Успешно обнаруженных случаев,%"
129 | },
130 | {"en": "Confirmed cases (log scale)",
131 |  "de": "Bestätigte Fälle (logarithmisch)",
132 |  "tr": "Doğrulanmış vakala sayısı (logaritmik ölçekte)",
133 |  "hi": "वे केस जिनकी पुष्टि हो चुकी है (लॉग स्केल)",
134 |  "es": "Casos diagnosticados (escala logarítmica)",
135 |  "ru": "Подтвержденные случаи (логарифмическая шкала)"
136 | },
137 | {"en": "Number of days since 100 cases",
138 |  "de": "Tage seit 100 bestätigter Fälle",
139 |  "tr": "100 vakaya ulaşıldığından beri geçen gün sayısı ",
140 |  "hi": "100 केस के बाद से दिनों की संख्या",
141 |  "es": "Días desde que hay más de 100 casos",
142 |  "ru": "Количество дней с момента достижения 100 случаев"
143 | },
144 | {"en": "Doubling every",
145 |  "de": "Verdopplungszeit",
146 |  "tr": "Her _ günde bir ikiye katlanır",
147 |  "hi": "दोगुना होना हर",
148 |  "es": "Se duplica cada",
149 |  "ru": "Удваивается каждые"
150 | },
151 | {"en": "days",
152 |  "de": "Tage",
153 |  "tr": "günler",
154 |  "hi": "दिन में",
155 |  "es": "días",
156 |  "ru": "дней"
157 | },
158 | {"en": "Active cases estimated to peak beyond the forecast horizon",
159 |  "de": "Höhepunkt des Ausbruchs liegt in der fernen Zukunft",
160 |  "tr": "Tahmin eşiğinin ötesinde pik etmesi beklenen vakalar",
161 |  "hi": "पूर्वानुमान क्षितिज से परे चोटी पर सक्रिय मामलों का अनुमान",
162 |  "es": "El pico de casos activos estimados cae fuera del rango de la proyección, no se muestra",
163 |  "ru": "Ожидаемое пиковое число активных случаев за горизонтом прогнозирования"
164 | },
165 | {"en": "Active cases peaked in the past",
166 |  "de": "Höhepunkt des Ausbruchs ausserhalb des Berechnungszeitraums erwartet",
167 |  "tr": "Geçmişte pik etmiş aktif vakalar",
168 |  "hi": "पिछले दिनों सक्रिय मामले चरम पर",
169 |  "es": "Casos activos alcanzó pico en el pasado",
170 |  "ru": "Пиковое число активных случаев в прошлом"
171 | },
172 | {"en": "Active cases estimated to peak at",
173 |  "de": "Höhepunkt des Ausbruchs erwartet am",
174 |  "tr": "aktif vakaların _ vaka ile _ tarihinde pik etmesi bekleniyor",
175 |  "hi": "सक्रिय मामलों के चरम पर होने का अनुमान है",
176 |  "es": "El pico de casos activos estimado es",
177 |  "ru": "Ожидаемая дата достижения пикового числа активных случаев"
178 | },
179 | {"en": "cases on",
180 |  "de": "Fälle am",
181 |  "tr": "",
182 |  "hi": "मामलों पर",
183 |  "es": "Casos en",
184 |  "ru": "случаев на"
185 | },
186 | {"en": "Doubling time is",
187 |  "de": "Verdopplungszeit ist",
188 |  "tr": "İkiye katlanma süresi",
189 |  "hi": "दोहरीकरण का समय",
190 |  "es": "Días que tarda en duplicar",
191 |  "ru": "Время удвоения"
192 | },
193 | {"en": "Halving time is",
194 |  "de": "Halbierungszeit ist",
195 |  "tr": "Yarılanma süresi",
196 |  "hi": "आधा  होने का समय है",
197 |  "es": "Días que tarda en bajar a la mitad",
198 |  "ru": "Время уполовинивания"
199 | },
200 | {"en": "Insufficient data for estimation",
201 |  "de": "Nicht genügend Daten für Prognose",
202 |  "tr": "Tahmin için yetersiz veri",
203 |  "hi": "अनुमान के लिए अपर्याप्त डेटा",
204 |  "es": "Insuficiente datos para hacer el cálculo",
205 |  "ru": "Недостаточно данных для прогноза"
206 | },
207 | {"en": "Now",
208 |  "de": "Akutuell",
209 |  "tr": "Güncel ",
210 |  "hi": "अभी",
211 |  "es": "A la fecha",
212 |  "ru": "Сейчас"
213 | },
214 | {"en": "In 10 days (min-max)",
215 |  "de": "In 10 Tagen (min-max)",
216 |  "tr": "10 gün içinde (min - maks)",
217 |  "hi": "10 दिनों में (न्यूनतम- अधिकतम)",
218 |  "es": "En 10 Días (min-max)",
219 |  "ru": "Через 10 дней (min-max)"
220 | },
221 | {"en": "Diagnosed",
222 |  "de": "Diagnostiziert",
223 |  "tr": "Tanısı konulmuş",
224 |  "hi": "निदान",
225 |  "es": "Diagnosticados",
226 |  "ru": "Диагностируемых"
227 | },
228 | {"en": "Undiagnosed",
229 |  "de": "Nicht diagnostiziert",
230 |  "tr": "Tanısı konulmamış",
231 |  "hi": "निदान नहीं",
232 |  "es": "Sin diagnóstico",
233 |  "ru": "Недиагностируемых"
234 | },
235 | {"en": "Undetected",
236 |  "de": "Nicht entdeckt",
237 |  "tr": "Tespit edilemeyen",
238 |  "hi": "पता नहीं लगा",
239 |  "es": "No detectados",
240 |  "ru": "Необнаруженных"
241 | },
242 | {"en": "Total",
243 |  "de": "Gesamt",
244 |  "tr": "Toplam ",
245 |  "hi": "कुल मिलाकर",
246 |  "es": "Total",
247 |  "ru": "Всего"
248 | },
249 | {"en": "Curve-flattening index",
250 |  "de": "Glättungskoeffizient der Kurve",
251 |  "tr": "Eğri düzleştime indeksi",
252 |  "hi": "क्र-चपटा सूचकांक",
253 |  "es": "Índice de aplanamiento de curva",
254 |  "ru": "Индекс выравнивания кривой"
255 | },
256 | {"en": "Growth rate (% per day)",
257 |  "de": "Wachstumsrate (% pro Tag)",
258 |  "tr": "Üreme katsayısı (günlük %)",
259 |  "hi": "विकास दर (% हर दिन)",
260 |  "es": "Ratio de crecimiento (% por día)",
261 |  "ru": "Скорость роста (% в день)"
262 | },
263 | {"en": "Coronavirus 10-day forecast",
264 |  "de": "Coronavirus Vorhersage für die nächsten 10 Tage",
265 |  "tr": "10-günlük Koronavirüs tahminleri",
266 |  "hi": "कोरोनावायरस 10 दिन का पूर्वानुमान",
267 |  "es": "COVID-19 proyección de 10 días",
268 |  "ru": "Прогноз COVID-19 на ближайшие 10 дней"
269 | },
270 | {"en": "Provides estimates of COVID-19 growth rate, detection, and near-future case load in each country, updated daily, based on global data collated by Johns Hopkins University",
271 |  "de": "Prognose von COVID-19 Wachstum, Detektion und Fallzahlen in der nahen Zukunft je nach Land, täglich aktualisert, basierend auf den Daten von der Johns Hopkins Universität",
272 |  "tr": "COVID-19 için üreme katsayısı tahminleri, vaka tespiti, ve her ülke için yakın zamanlı vaka sayısı sunar. Günlük güncellenen veriler Johns Hopkins Üniversitesi tarafından oluşturulan global veri setine dayanmaktadır.",
273 |  "hi": "जॉन हॉपकिंस विश्वविद्यालय द्वारा वैश्विक आंकड़ों के आधार पर COVID-19 विकास दर, पता लगाने और प्रत्येक देश में निकट भविष्य के मामले के भार का अनुमान प्रदान करता है।",
274 |  "es": "Brinda proyecciones del COVID-19 ratio de crecimiento, detección y casos en los próximos días para cada país, actualizado diariamente, utiliza datos coleccionados por Johns Hopkins University",
275 |  "ru": "Прогноз роста, обнаружения и количества случаев заболевания COVID-19 в ближайшем будущем по странам, ежедневно обновляемый на основе данных из Университета Джона Хопкинса"
276 | },
277 | {"en": "Are you interested in other countries?  This site is locked to one country, but go to our main site for all countries with more than 20 cases of covid19.",
278 |  "de": "Interessiert in anderen Ländern? Gehe zu der Hauptseite für eine Übersicht mit allen Ländern mit mehr als 20 Infizierten.",
279 |  "tr": "Diğer ülkeler ilginizi çekiyor mu? Şu anda tek bir ülkeyi görüyorsunuz, ama şurayı ziyaret edin ana sayfamız vaka sayısı 20'den fazla olan bütün ülkeler için.",
280 |  "hi": "क्या आप अन्य देशों में रुचि रखते हैं? यह साइट केवल एक देश के लिए है, लेकिन इसे देखे हमारी मुख्य साइट COVID19 के 20 से अधिक मामलों वाले सभी देशों के लिए",
281 |  "es": "Esta interesado en otros países? Este sitio web es para un solo país, vaya a",
282 |  "ru": "Заинтересованы в других странах? Перейдите на нашу главную страницу, где приведена информация для всех стран с более чем 20 случаями COVID-19"
283 | },
284 | {"en": "We have specific sites for certain countries.  Please visit",
285 |  "de": "Wir haben individualle Seiten für bestimmte Länder. Bitte mal besuchen.",
286 |  "tr": "Belli ülkeler için ayrı sayfalarımız bulunmaktadır. Bunları görmek için şurayı ziyaret edin ",
287 |  "hi": "हमारे पास कुछ देशों के लिए विशिष्ट साइटें हैं",
288 |  "es": "Tenemos sitios web específicos para ciertos países, Por favor visite",
289 |  "ru": "У нас есть отдельные страницы для ряда стран. Пожалуйста, посетите их"
290 | },
291 | {"en": "our",
292 |  "de": "unsere",
293 |  "tr": "bizim ",
294 |  "hi": "हमारी",
295 |  "es": "nuestro",
296 |  "ru": "наша"
297 | },
298 | {"en": "site",
299 |  "de": "Seite",
300 |  "tr": "site",
301 |  "hi": "साइट",
302 |  "es": "sitio web",
303 |  "ru": "страница"
304 | },
305 | {"en": "Growth rates",
306 |  "de": "Wachstumsraten",
307 |  "tr": "Üreme katsayısı",
308 |  "hi": "वृद्धि दरें",
309 |  "es": "Ratio de crecimiento",
310 |  "ru": "Темпы роста"
311 | },
312 | {"en": "About",
313 |  "de": "Über uns",
314 |  "tr": "Hakkımızda",
315 |  "hi": "साइट के बारे में",
316 |  "es": "Acerca",
317 |  "ru": "О нас"
318 | },
319 | {"en": "10-day forecasts",
320 |  "tr": "10-günlük tahminler",
321 |  "hi": "10-दिन का पूर्वानुमान",
322 |  "es": "Proyección de 10 días",
323 |  "ru": "Прогноз на 10 дней"
324 | },
325 | {"en": "Location",
326 |  "de": "Standort",
327 |  "tr": "Yer",
328 |  "hi": "स्थान",
329 |  "es": "Lugar",
330 |  "ru": "Место"
331 | },
332 | {"en": "Select Country/Region:",
333 |  "de": "Auswahl Land oder Region",
334 |  "tr": "Ülke/Bölge seçiniz:",
335 |  "hi": "देश / क्षेत्र का चयन करें",
336 |  "es": "Seleccione país o región",
337 |  "ru": "Выберите страну/регион"
338 | },
339 | {"en": "Raw case numbers:",
340 |  "de": "Unbearbeitete Fallzahlen:",
341 |  "tr": "Güncel vaka sayıları:",
342 |  "hi": "अनिश्चित मामलों की संख्या",
343 |  "es": "Número de casos en bruto",
344 |  "ru": "Число необработанных случаев"
345 | },
346 | {"en": "Active cases:",
347 |  "de": "Aktive Fälle:",
348 |  "tr": "Aktif vakalar:",
349 |  "hi": "कुल कोरोनावाइरस केस",
350 |  "es": "Casos activos",
351 |  "ru": "Активных случаев:"
352 | },
353 | {"en": "Active cases are total number of infections minus deaths and recoveries.",
354 |  "de": "Aktive Fälle misst die Anzahl von Infizierten minus Verstorbenden und Genesenen.",
355 |  "tr": "Aktif vaka sayısı, toplam vaka sayısından toplam vefat sayısı ve toplam iyileşen hasta sayısı çıkartılarak elde edilmiştir.",
356 |  "hi": "सक्रिय मामलों में संक्रमण की कुल संख्या माइनस डेथ और रिकवरी है",
357 |  "es": "Casos activos son los números total de infectados menos los fallecidos y recuperados.",
358 |  "ru": "Число активных случаев есть полное число инфицированных за вычетом числа смертей и выздоровлений"
359 | },
360 | {"en": "Forecast metrics:",
361 |  "de": "Parameter für Vorhersage:",
362 |  "tr": "Tahmin metrikleri:",
363 |  "hi": "पूर्वानुमान मेट्रिक्स",
364 |  "es": "Métricas para el cálculo de la proyección",
365 |  "ru": "Метрики для прогноза"
366 | },
367 | {"en": "Time-varying growth",
368 |  "de": "Zeitabhängiges Wachstum",
369 |  "tr": "Zamana bağlı büyüme",
370 |  "hi": "समय-भिन्न विकास",
371 |  "es": "Crecimiento en serie de tiempo",
372 |  "ru": "Зависящий от времени рост"
373 | },
374 | {"en": "Constant growth",
375 |  "de": "Konstantes Wachstum",
376 |  "tr": "Sabit büyüme",
377 |  "hi": "लगातार विकास",
378 |  "es": "Crecimiento constante",
379 |  "ru": "Постоянный рост"
380 | },
381 | {"en": "When growth rates are changing fast, reduce the fit window to average growth over more recent history",
382 |  "de": "Wenn sich die Wachstumsraten sehr schnell verändern, sollte man den Bereich für die Kurvenanpassung verkürzen.",
383 |  "tr": "Üreme katsayıları hızlı değişirse, gün sayısını düşürerek ortalamayı daha yakın tarihe getirin",
384 |  "hi": "जब विकास दर तेजी से बदल रहे हैं, तो अधिक हाल के इतिहास में वृद्धि के लिए औसत विंडो फिट करें",
385 |  "es": "Cuando el ratio de crecimiento cambia rápidamente, reduzca los días para que estos últimos datos tengan más peso al calcular el promedio",
386 |  "ru": "Если темпы роста меняются быстро, сократите ширину окна, чтобы усреднить рост по недавнему прошлому"
387 | },
388 | {"en": "Cases successfully detected:",
389 |  "de": "Fälle entdeckt:",
390 |  "tr": "Tespitinde başarılı olunmuş vaka sayısı:",
391 |  "hi": "मामलों का सफलतापूर्वक पता चला:",
392 |  "es": "Casos diagnosticados",
393 |  "ru": "Число обнаруженных случаев"
394 | },
395 | {"en": "Possible number of active cases now given imperfect detection:",
396 |  "de": "Wahrscheinliche aktive Fälle bei Einbezug der Dunkelziffer:",
397 |  "tr": "Kusurlu tespit koşulları altında olası aktif vaka sayısı:",
398 |  "hi": "सक्रिय मामलों की संभावित संख्या अब अपूर्ण पहचान बताती है:",
399 |  "es": "Posible número de casos nuevos a la fecha considerando que hay casos asintomáticos que aun están sin diagnóstico y otros que no serán detectados",
400 |  "ru": "Возможное число активных случаев, принимая во внимание неполное обнаружение"
401 | },
402 | {"en": "To see how the assumed case fatality ratio affects detection (and so possible true case numbers) adjust the slider.",
403 |  "de": "Um die Auswirkung der angenommenen Sterblichkeitsrate auf die Detektion (und damit die wirklichen Fallzahlen) zu sehen, bitte den Schieber regulieren.",
404 |  "tr": "Varsayılan ölüm oranlarının tespit değerlerini (ve olası doğru vaka sayılarını) nasıl etkilediğini görmek için çubuğu ayarlayınız.",
405 |  "hi": "यह देखने के लिए कि ग्रहण किए गए मामले के घातक अनुपात का पता लगाने को कैसे प्रभावित करता है (और इसलिए संभव है कि सही केस संख्या) स्लाइडर को समायोजित करें",
406 |  "es": "Para ver como la tasa de mortalidad asumida afecta el número de diagnosticados y posiblemente el número real de nuevos casos, ajuste el indicador.",
407 |  "ru": "Чтобы увидеть, как предположенный коэффициент летальности влияет на обнаружение (и тем самым на возможное истинное числщ случаев), настройте ползунок"
408 | },
409 | {"en": "Take these last numbers with a grain of salt; they are rough.  Undiagnosed cases are current infections yet to develop symptoms and be diagnosed.  Undetected cases are current infections that will not be diagnosed.  Large numbers of undetected cases indicate that there are many more deaths in the region than there should be given reported case numbers (so there are many undetected cases or a large number of imported cases).",
410 |  "de": "Bitte diese Zahlen mit Vorsicht genießen, sie könnten ein bisschen ungenau sein. Nicht diagnostizierte Fälle sind aktuell Infizierte, die noch Symtome entwickeln und diagnostiziert werden müssen. Nicht entdeckte Fälle sind aktuelle Infizierte, die niemals diagnostiziert werden. Viele nicht entdeckte Fälle deuten darauf hin, dass es viel mehr Todesfälle in der Region gibt, als es eigentlich geben sollte basiered auf den registrierten Fallzahlen.",
411 |  "tr": "Buradaki son sayılar yaklaşık olarak verilmiştir. Tanısı konulmamış vakalar, semptomları geliştirmesi ve tanı konulması beklenen güncel vakaları ifade eder. Tespit edilememiş vakalar tanısı konulamayacak güncel vakaları ifade eder. Tespit edilememiş vakaların sayısının fazla olması bölgede raporlanan vaka sayıları göz önüne alındığında olması gerekenden daha fazla vefat vakası olduğunu gösterir (yani çok sayıda tespit edilememiş veya ithal vaka).",
412 |  "hi": "नमक के एक दाने के साथ इन अंतिम संख्याओं को लें; वे अपरिष्कृत हैं । लक्षणों को विकसित करने और निदान करने के लिए अभी तक असंक्रमित मामले मौजूदा संक्रमण हैं । पूर्व अनपेक्षित मामले वर्तमान संक्रमण हैं जिनका निदान नहीं किया जाएगा। बड़ी संख्या में अनिर्धारित मामलों से संकेत मिलता है कि इस क्षेत्र में रिपोर्ट की गई संख्याओं की तुलना में कई अधिक मौतें हैं(इसलिए कई अनडेटेड मामले या बड़ी संख्या में आयातित मामले हैं)",
413 |  "es": "Tome estos números de manera ligera son basados en promedios. Casos sin diagnóstico son personas asintomáticas que aún no han sido diagnosticadas. Casos no detectados son personas infectadas que no serán diagnosticadas. Un número alto de casos no detectados indica que hay muchos más fallecidos en el lugar que los que han sido reportados es decir muchos casos no diagnosticados o son casos importados.",
414 |  "ru": "Пожалуйста, относитесь к этим числам с осторожностью, они довольно грубые. Недиагностированные случаи суть инфицированые индивидуумы, у которых ещё не проявились симптомы и которых ещё надо диагностировать. Необнаруженные случаи -- это инфицированые индивидуумы, которые никогда не будут диагностированы. Большое число необнаруженных случаев указывает то, что в данном регионе зарегистрировано больше смертей, чем должно было бы быть согласно количеству зарегистрированных случаев."
415 | },
416 | {"en": "The last plot is the percentage of new cases that are successfully detected, and how this has changed over time.  Values near 100% are good, indicating that most cases are being detected/reported.  Unexpected outbreaks cause temporary reductions in detection.",
417 |  "de": "Die unterste Grafik zeigt den Prozentsatz der neu Infizierten, die entdeckt wurden, und derren zeitliche Entwicklung. 100% sind gut, und bedingen dass die meisten Fälle nachgewiesen und registriert sind. Nicht erwartete Ausbrüche verursachen einen kurzzeitlichen Rückgang der entdeckten Fälle.",
418 |  "tr": "Son grafik, tespitinde başarılı olunmuş yeni vakaların yüzdesini ve bunun zaman içindeki değişimini göstermektedir. %100'e yakın değerler iyi olup vakaların çoğunluğunun tespit edildiğini/raporlandığını göstermektedir. Beklenmedik salgınlar tespitlerde geçici düşüşlere yol açmaktadır.",
419 |  "hi": "अंतिम लेखाचित्र नए मामलों का प्रतिशत है जो सफलतापूर्वक पता चला है, और यह समय के साथ कैसे बदल गया है। 100% के पास मूल्य अच्छे हैं, और यह दर्शाता है कि अधिकांश मामलों का पता / रिपोर्ट किया जा रहा है। अप्रत्याशित प्रकोपों का पता लगाने में अस्थायी कटौती का कारण बनता है",
420 |  "es": "El último diagrama muestra el porcentaje de casos nuevos que han sido diagnosticados, y como esto ha cambiado a través del tiempo. Valores de 100% son buenos reflejan que casi todos los casos han sido diagnosticados. Los brotes inesperados causan la reducción del diagnóstico." ,
421 |  "ru": "Последний график показывает процент обнаруженных новых случаев и как он изменяется со временем. Значения, близкие к 100% -- это хороший индикатор, указывающий на то, что большинство случаев были обнаружены и зарегистрированы. Неожиданные вспышки вызывают временное снижение числа обнаруженных случаев"
422 | },
423 | {"en": "Detection can only be calculated up to 17 days in the past, and estimates are often patchy in countries/regions with few deaths.",
424 |  "de": "Die Aufklärungsrate kann maximal 17 Tage in die Vergangenkeit berechnet werden, und ist oft in Regionen mit wenigen Todesfällen sehr ungenau.",
425 |  "tr": "Tespitler geriye dönük en fazla 17 gün için yapılabilirken, tahminler vefat sayısı düşük olan ülke/bölgelerde genellikle eşitsiz aralıklarla yapılmaktadır.",
426 |  "hi": "सुरागरसानी की गणना केवल अतीत में 17 दिनों तक की जा सकती है, और अनुमान कुछ मौतों वाले देशों / क्षेत्रों में अक्सर बद होते हैं",
427 |  "es": "La detección solo puede ser calculada hasta 17 días pasados, y los cálculos son un poco imprecisos en lugares con pocos fallecidos",
428 |  "ru": "Выявление может быть рассчитано только до 17 дней в прошлом, и оценки часто бывает неточными в странах/регионах с небольшим количеством смертей."
429 | },
430 | {"en": "For more information, see ",
431 |  "de": "Weitere Information, siehe ",
432 |  "tr": "Daha fazla bilgi için, bakınız",
433 |  "hi": "अधिक जानकारी के लिए देखें",
434 |  "es": "Para mayor información, vea",
435 |  "ru": "Для получения дополнительной информации смотри"
436 | },
437 | {"en": "Growth trajectories",
438 |  "de": "Entwicklung der Wachstumsrate",
439 |  "tr": "Büyüme eğrisi ",
440 |  "hi": "विकास के प्रक्षेपवक्र",
441 |  "es": "Trayectorias de crecimiento",
442 |  "ru": "Траектории скорости роста"
443 | },
444 | {"en": "This is the growth trajectory of selected regions: growth in total cases standardised to start when a region reaches 100 cases.",
445 |  "de": "Entwicklung der Wachstumsrates für bestimmte Regionen: Wachstum der kumulativen Fallzahlen bezogen auf den Tag seit eine Region 100 COVID-19 Fälle erreicht hat.",
446 |  "tr": "Seçili bölgeler için büyüme eğrisi: bir bölge 100 vakaya ulaştığında toplam vaka sayısındaki büyüme başlangıca göre standardize edilmiştir.",
447 |  "hi": "यह चयनित क्षेत्रों का विकास पथ है: कुल मामलों में वृद्धि जब कोई क्षेत्र 100 मामलों तक पहुंचता है, तो शुरू करने के लिए मानकीकृत है",
448 |  "es": "Esta es la trayectoria de crecimiento de los lugares seleccionados: crecimiento en el total de casos es estandarizado a comenzar cuando en el lugar se han alcanzado 100 casos.",
449 |  "ru": "Это траектория роста отдельных регионов: рост в общем количестве случаев стандартизирован и начинается, когда число случаев в регионе достигает 100."
450 | },
451 | {"en": "This plot does not account for detection.  Because it is on the log scale, the slope at any time is indicative of the doubling time in total cases at that point in time (indicative slopes shown).",
452 |  "de": "Diese Darstellung bezieht nicht die angenommene Dunkelziffer mit ein. Aufgrund der logarithmischen Darstellung gibt die Steigung der Kurve die Vordoppelungszeit der kumulativen Fallzahlen zu einem bestimmten Zeitpunkt an (Trendkurven mit einbezogen). ",
453 |  "tr": "Bu grafik tespit değerlerini açıklamamaktadır. Logaritmik ölçüde olduğu için, herhangi bir noktadaki eğim o noktadaki toplam vaka sayısındaki ikiye katlanmanın göstergesidir. (eğimler gösterildiği gibidir).",
454 |  "hi": "यह लेखाचित्र पता लगाने के लिए नहीं है क्योंकि यह लॉग स्केल पर है किसी भी समय ढलान उस समय कुल मामलों में दोहरीकरण समय का संकेत है (सांकेतिक ढलान दिखाया गया है)",
455 |  "es": "Este diagrama no considera la detección. Por que usa la escala logarítmica, cuando hay una cuesta indica que en ese momento el total de casos se está duplicando(indicadores de crecimiento se muestran en líneas punteadas).",
456 |  "ru": "Этот график не учитывает обнаружение. Поскольку он приведён в логарифмической шкале, наклон в любое время указывает на время удвоения общего числа случаев в тот момент времени (показаны примерные наклоны)."
457 | },
458 | {"en": "This is the daily growth in active cases, plotted over the last 20 days.  It can be thought of as the interest rate, compounded daily.",
459 |  "de": "Darstellung der täglichen Zunahme der aktiven Fälle für die letzten 20 Tage. Kann man sich als Zinssatz vorstellen, täglich neu berechnet.",
460 |  "tr": "Aktif vakaların son 20 gündeki günlük üreme katsayıları. Bu, günlük faiz oranı gibi düşünülebilir.",
461 |  "hi": "यह पिछले 20 दिनों में सक्रिय मामलों में दैनिक वृद्धि है। इसे रोजाना चक्रवृद्धि ब्याज दर के रूप में सोचा जा सकता है",
462 |  "es": "Este es el diagrama de crecimiento diario de casos activos en los últimos 20 días. Puede considerarlo como la tasa de interés compuesto diaria.",
463 |  "ru": "Показан ежедневный прирост активных случаев за последние 20 дней. Его можно рассматривать как ежедневно пересчитываемую сложную процентную ставку."
464 | },
465 | {"en": "Positive is bad, negative is good. Progress in control would be indicated by steady decline in growth rate over time, and holding in negative territory.",
466 |  "de": "Positiv ist gut, negativ is schlecht. Fortschritte bei der Eindämmung würde man bei einer stetigen Abnahme der Wachstumsrate sehen, die dann im negativen Bereich bleibt.",
467 |  "tr": "Pozitif değerler kötüyü, negatif değerler ise iyiyi göstermektedir. Üreme katsayısının negatif bölgede sabit bir düşüşte seyretmesi kontrol yöntemlerindeki başarıyı işaret eder.",
468 |  "hi": "सकारात्मक बुरा है, नकारात्मक अच्छा है। समय के साथ विकास दर में लगातार गिरावट से नियंत्रण में प्रगति का संकेत मिलेगा, और नकारात्मक क्षेत्र में जारी है",
469 |  "es": "Positivo es malo, negativo es bueno. Progreso en el control de la pandemia será indicado por un estable declinamiento en el ratio de crecimiento, y mantenerse en la zona negativa.",
470 |  "ru": "Положительные значения -- это плохой знак, отрицательные – хороший. Прогресс в борьбе с пандемией будет виден как устойчивое снижение темпов роста и пребывание в отрицательной зоне"
471 | },
472 | {"en": "Curve flattening index",
473 |  "de": "Koeffizient für das Abflachen der Kurve",
474 |  "tr": "Eğri düzleştime indeksi ",
475 |  "hi": "क्र-चपटा सूचकांक",
476 |  "es": "Indice de aplanamiento de curva",
477 |  "ru": "Коэффициент выравнивания кривой"
478 | },
479 | {"en": "This is a measure of how well a region is flattening the pandemic curve at any point in time.  Positive values mean growth rates are declining at that point in time.",
480 |  "de": "Dieser Wert gibt an, wie gut eine Region die Kurve der Pandemie zu einem bestimmten Zeitpunkt abflacht. Positive Werte bedeuten, dass die Wachstumsraten zu diesem Zeitpunkt abnehmen.",
481 |  "tr": "Bir bölgenin, zaman içinde pandemi eğrisini düzleştirmedeki başarısını gösterir. Pozitif değerler büyüme oranının düşüşe geçtiğini göstermektedir.",
482 |  "hi": "यह इस बात का माप है कि कोई क्षेत्र किसी भी समय महामारी वक्र को कितनी अच्छी तरह समतल कर रहा है। सकारात्मक मूल्यों का मतलब है कि विकास दर उस समय घट रही है",
483 |  "es": "Esta es una medida de cuan bien una región esta aplanando la curva de crecimiento a la fecha. Valores positivos significan que el ratio de crecimiento está bajando en esos días.",
484 |  "ru": "Это мера того, насколько хорошо регион выравнивает кривую пандемии в любой момент времени. Положительные значения означают, что темпы роста снижаются в этот момент времени"
485 | },
486 | {"en": "Note, this last plot covers the entire time period of the pandemic, not just the last twenty days.",
487 |  "de": "Anmerkung: Diese Darstellung ziegt den gesamten Verlauf der Pandemie, nicht nur die letzten 20 Tage.",
488 |  "tr": "Not: son grafik sadece son yirmi günü değil bütün pandemi süresini kapsamaktadır.",
489 |  "hi": "ध्यान दें, यह अंतिम भूखंड पिछले बीस दिनों की ही नहीं, महामारी की संपूर्ण समयावधि को समाहित करता है",
490 |  "es": "Nota, este último diagrama cubre el periodo entero de la pandemia, no solo los últimos veinte días.",
491 |  "ru": "Обратите внимание, что последний график охватывает весь период времени пандемии, а не только последние двадцать дней"
492 | },
493 | {"en": "For more details see the",
494 |  "de": "Für weitere Erklärungen, siehe",
495 |  "tr": "Daha fazla bilgi için, bakınız",
496 |  "hi": "अधिक जानकारी के लिए, यह देखें",
497 |  "es": "Para mayor detalles vea la",
498 |  "ru": "Для получения дополнительной информации смотри"
499 | },
500 | {"en": "section",
501 |  "de": "Abschnitt",
502 |  "tr": "bölüm",
503 |  "hi": "अनुभाग",
504 |  "es": "sección",
505 |  "ru": "раздел"
506 | },
507 | {"en": "Country/region",
508 |  "de": "Land oder Region",
509 |  "tr": "Ülke/Bölge",
510 |  "hi": "देश / क्षेत्र",
511 |  "es": "país o región",
512 |  "ru": "Страна/регион"
513 | },
514 | {"en": "Days since last new case",
515 |  "de": "Tage seit letztem neuen Fall",
516 |  "tr": "Son yeni vakadan bu zamana kadar geçen gün sayısı",
517 |  "hi": "पिछले दिनों से नया मामला",
518 |  "es": "Días desde el último caso nuevo",
519 |  "ru": "Количество дней с момента последнего нового случая"
520 | },
521 | {"en": "Days since last death",
522 |  "de": "Tage seit letztem Todesfall",
523 |  "tr": "Son ölümden bu zamana kadar geçen gün sayısı",
524 |  "hi": "अंतिम मृत्यु के बाद के दिन",
525 |  "es": "Días desde la última muerte",
526 |  "ru": "Количество дней с момента последней смерти"
527 | },
528 | {"en": "Log scale",
529 |  "de": "Logarithmisch",
530 |  "tr": "Logaritmik ölçekte",
531 |  "hi": "लॉग स्केल",
532 |  "es": "Escala logarítmica",
533 |  "ru": "Логарифмическая шкала"
534 | },
535 | {"en": "Linear scale",
536 |  "de": "Lineare Skala",
537 |  "tr": "Lineer ölçek",
538 |  "hi": "रैखिक पैमाने",
539 |  "es": "Escala lineal",
540 |  "ru": "Линейная шкала"
541 | },
542 | {"en": "Growth",
543 |   "de": "Wachstum",
544 |   "tr": "Büyüme",
545 |   "hi": "विकास",
546 |   "es": "Crecimiento",
547 |   "ru": "Рост"
548 |  },
549 |  {"en": "Time-varying",
550 |   "de": "Zeitlich variierend",
551 |   "tr": "Zamanla değişen",
552 |   "hi": "समय-परिवर्तनीय",
553 |   "es": "Variable en el tiempo",
554 |   "ru": "Изменяющийся во времени"
555 |  },
556 |  {"en": "Constant",
557 |   "de": "Konstante",
558 |   "tr": "Sabit",
559 |   "hi": "स्थिर",
560 |   "es": "Constante",
561 |   "ru": "Постоянный"
562 |  },
563 |  {"en": "Time",
564 |   "de": "Zeit",
565 |   "tr": "Zaman",
566 |   "hi": "समय",
567 |   "es": "Hora",
568 |   "ru": "Время"
569 |  },
570 |  {"en": "All",
571 |   "de": "Alle",
572 |   "tr": "Herşey",
573 |   "hi": "सब",
574 |   "es": "Todos",
575 |   "ru": "Все"
576 |  },
577 |  {"en": "Past 4 months",
578 |   "de": "Letzte 4 Monate",
579 |   "tr": "Son 4 ay",
580 |   "hi": "पिछले 4 महीने",
581 |   "es": "Últimos 4 meses",
582 |   "ru": "Последние 4 месяца"
583 |  }
584 |   ]
585 | }
586 | 
587 | 


--------------------------------------------------------------------------------
/app.R:
--------------------------------------------------------------------------------
  1 | ## ---------------------------
  2 | ##
  3 | ## Script name: 
  4 | ##
  5 | ## Purpose of script:
  6 | ##
  7 | ## Author: Ben Phillips
  8 | ##
  9 | ## Date Created: 2020-03-12
 10 | ##
 11 | ## Email: phillipsb@unimelb.edu.au
 12 | ##
 13 | ## ---------------------------
 14 | ##
 15 | ## Notes:
 16 | ##   
 17 | ##
 18 | ## --------------------------
 19 | ## load up the packages we will need 
 20 | library(shiny)
 21 | library(shiny.i18n)
 22 | library(plotly)
 23 | 
 24 | ## ---------------------------
 25 | 
 26 | ## source files
 27 | source("functions.R") # makes functions available to the instance.
 28 | source("getDataLocal.R") #makes data available to the instance.
 29 | 
 30 | ## ---------------------------
 31 | options(scipen=9)
 32 | 
 33 | 
 34 | # Define server logic 
 35 | server <- function(input, output, session) {
 36 |   
 37 |   ##### Set Language #####
 38 |   i18n <- Translator$new(translation_json_path = "translations/translations.json")
 39 | 
 40 |   observe({
 41 |     url_search <- parseQueryString(session$clientData$url_search)
 42 |     if ( is.null(names(url_search))) {
 43 |       i18n$set_translation_language('en') # default to English
 44 |     } else if ('lang' %in% names(url_search)) {
 45 |       selectedLanguage <- parseQueryString(session$clientData$url_search)$lang
 46 |       if (selectedLanguage %in% i18n$languages) {
 47 |         i18n$set_translation_language(selectedLanguage)
 48 |       } else {
 49 |         i18n$set_translation_language('en') # default to English
 50 |       }
 51 |     } else {
 52 |       i18n$set_translation_language('en') # default to English
 53 |     }
 54 |   })
 55 | 
 56 |   ##### Some useful variables #####
 57 |   please_select_a_country <- reactive({please_select_a_country <- i18n$t('Please select a country or region...')})
 58 |   clrDark   <- "#273D6E"
 59 |   clrLight  <- "#B2C3D5"
 60 |   clrOrange <- "#FF7F0E"  
 61 |   shortDateFormat <- "%d %b:"
 62 |   
 63 |   ##### Flags #####
 64 |   output$flagAustralia <- renderImage({
 65 |     list(src = normalizePath(file.path('./img/australia-flag-xs.png')),                height=50, alt = 'Australian site', title = 'Australian site')
 66 |   }, deleteFile = FALSE)
 67 | 
 68 |   output$flagUS        <- renderImage({
 69 |     list(src = normalizePath(file.path('./img/united-states-of-america-flag-xs.png')), height=50, alt = 'US site',         title = 'US site')
 70 |   }, deleteFile = FALSE)
 71 | 
 72 |   output$flagChina     <- renderImage({
 73 |     list(src = normalizePath(file.path('./img/china-flag-xs.png')),                    height=50, alt = 'Chinese site',    title = 'Chinese site')
 74 |   }, deleteFile = FALSE)
 75 | 
 76 |   output$flagIndia     <- renderImage({
 77 |     list(src = normalizePath(file.path('./img/india-flag-xs.png')),                    height=50, alt = 'Indian site',     title = 'Indian site')
 78 |   }, deleteFile = FALSE)
 79 | 
 80 |   output$flagCanada    <- renderImage({
 81 |     list(src = normalizePath(file.path('./img/canada-flag-xs.png')),                   height=50, alt = 'Canadian site',   title = 'Canadian site')
 82 |   }, deleteFile = FALSE)
 83 | 
 84 |   output$flagGermany   <- renderImage({
 85 |     list(src = normalizePath(file.path('./img/germany-flag-xs.png')),                  height=50, alt = 'German site',     title = 'German site')
 86 |   }, deleteFile = FALSE)
 87 | 
 88 |   output$flagItaly     <- renderImage({
 89 |     list(src = normalizePath(file.path('./img/italy-flag-xs.png')),                    height=50, alt = 'Italian site',    title = 'Italian site')
 90 |   }, deleteFile = FALSE)
 91 | 
 92 |   output$flagColombia   <- renderImage({
 93 |     list(src = normalizePath(file.path('./img/colombia-flag-xs.png')),                 height=50, alt = 'Colombian site',  title = 'Colombian site')
 94 |   }, deleteFile = FALSE)
 95 | 
 96 |   output$flagJapan   <- renderImage({
 97 |     list(src = normalizePath(file.path('./img/japan-flag-xs.png')),                    height=50, alt = 'Japanese site',  title = 'Japanese site')
 98 |   }, deleteFile = FALSE)
 99 | 
100 |   output$flagPeru   <- renderImage({
101 |     list(src = normalizePath(file.path('./img/peru-flag-xs.png')),                     height=50, alt = 'Peruvian site',  title = 'Peruvian site')
102 |   }, deleteFile = FALSE)
103 | 
104 |   output$flagSwitzerland   <- renderImage({
105 |     list(src = normalizePath(file.path('./img/switzerland-flag-xs.png')),              height=50, alt = 'Swiss site',  title = 'Swiss site')
106 |   }, deleteFile = FALSE)
107 | 
108 |   output$flagBelgium   <- renderImage({
109 |     list(src = normalizePath(file.path('./img/belgium-flag-xs.png')),                  height=50, alt = 'Belgian site',  title = 'Belgian site')
110 |   }, deleteFile = FALSE)
111 | 
112 |   output$flagHaiti   <- renderImage({
113 |     list(src = normalizePath(file.path('./img/haiti-flag-xs.png')),                    height=50, alt = 'Haitian site',  title = 'Haitian site')
114 |   }, deleteFile = FALSE)
115 | 
116 |   output$flagBrazil   <- renderImage({
117 |     list(src = normalizePath(file.path('./img/brazil-flag-xs.png')),                    height=50, alt = 'Brazilian site',  title = 'Brazilian site')
118 |   }, deleteFile = FALSE)
119 | 
120 |   list2env(dataList[["Global"]], envir = environment()) # make global data available to session
121 | 
122 |   ##### Text to be translated #####
123 |   ### the text is here rather than base.html so that it can easily be translated to other languages
124 |   ## header (anything on a dark blue background)
125 |   output$siteName         <- renderText({i18n$t('Coronavirus 10-day forecast')})
126 |   output$byline           <- renderText({i18n$t('Provides estimates of COVID-19 growth rate, detection, and near-future case load in each country, updated daily, based on global data collated by Johns Hopkins University')})
127 |   output$TenDayForecasts  <- renderText({i18n$t('10-day forecasts')})
128 |   output$growthRates      <- renderText({i18n$t('Growth rates')})
129 |   output$about            <- renderText({i18n$t('About')})
130 | 
131 |   ## skip to (can't reuse these unfortunately)
132 |   output$skipTo1          <- renderText({i18n$t('Skip to:')})
133 |   output$skipTo2          <- renderText({i18n$t('Skip to:')})
134 |   output$skipTo3          <- renderText({i18n$t('Skip to:')})
135 | 
136 |   ## section 0
137 |   output$selectALanguage  <- renderText({i18n$t('Select a language:')})
138 |   output$selectGlobal     <- renderText({i18n$t('Select global or country:')})
139 |   output$specificSites    <- renderText({i18n$t('We have specific sites for certain countries.  Please visit')})
140 |   output$otherCountries   <- renderText({i18n$t('Are you interested in other countries?  This site is locked to one country, but go to our main site for all countries with more than 20 cases of covid-19.')})
141 | 
142 |   ## section 1
143 |   output$location          <- renderText({i18n$t('Location')})
144 |   output$selectCountryRegion1 <- renderText({i18n$t('Select Country/Region:')})
145 |   output$rawCaseNumbers    <- renderText({i18n$t('Raw case numbers:')})
146 |   output$activeCases       <- renderText({i18n$t('Active cases:')})
147 |   output$activeCasesP      <- renderText({i18n$t('Active cases are total number of infections minus deaths and recoveries.')})
148 |   output$forecastMetricsH  <- renderText({i18n$t('Forecast metrics:')})
149 |   output$growthLegend      <- renderText({i18n$t('Growth')})
150 |   output$timeVaryingGrowth <- renderText({i18n$t('Time-varying')})
151 |   output$constantGrowth    <- renderText({i18n$t('Constant')})
152 |   output$timeLegend        <- renderText({i18n$t('Time')})
153 |   output$recentTime        <- renderText({i18n$t('Past 4 months')})
154 |   output$allTime           <- renderText({i18n$t('All')})
155 |   output$fitWindow         <- renderText({i18n$t('Fit window:')})
156 |   output$growthRatesP      <- renderText({i18n$t('When growth rates are changing fast, reduce the fit window to average growth over more recent history')})
157 |   output$detection         <- renderText({i18n$t('Detection')})
158 |   output$casesSuccessfullyDetected <- renderText({i18n$t('Cases successfully detected:')})
159 |   output$possibleNumberOf  <- renderText({i18n$t('Possible number of active cases now given imperfect detection:')})
160 |   output$caseFatalityRatio <- renderText({i18n$t('Case fatality ratio:')})
161 |   output$toSeeHowThe       <- renderText({i18n$t('To see how the assumed case fatality ratio affects detection (and so possible true case numbers) adjust the slider.')})
162 |   output$takeTheseLast     <- renderText({i18n$t('Take these last numbers with a grain of salt; they are rough.  Undiagnosed cases are current infections yet to develop symptoms and be diagnosed.  Undetected cases are current infections that will not be diagnosed.  Large numbers of undetected cases indicate that there are many more deaths in the region than there should be given reported case numbers (so there are many undetected cases or a large number of imported cases).')})
163 |   output$theLastPlot       <- renderText({i18n$t('The last plot is the percentage of new cases that are successfully detected, and how this has changed over time.  Values near 100% are good, indicating that most cases are being detected/reported.  Unexpected outbreaks cause temporary reductions in detection.')})
164 |   output$detectionCanOnly  <- renderText({i18n$t('Detection can only be calculated up to 17 days in the past, and estimates are often patchy in countries/regions with few deaths.')})
165 |   output$logScale          <- renderText({i18n$t('Log scale')})
166 |   output$linearScale       <- renderText({i18n$t('Linear scale')})
167 | 
168 |   ## section 2
169 |   output$locationH2       <- renderText({i18n$t('Location')})
170 |   output$selectCountryRegion2 <- renderText({i18n$t('Select Country/Region:')})
171 |   output$growthTrajectories <- renderText({i18n$t('Growth trajectories')})
172 |   output$growthRates2     <- renderText({i18n$t('Growth rates')})
173 |   output$thisIsTheGrowth  <- renderText({i18n$t('This is the growth trajectory of selected regions: growth in total cases standardised to start when a region reaches 100 cases.')})
174 |   output$thisPlotDoesNot  <- renderText({i18n$t('This plot does not account for detection.  Because it is on the log scale, the slope at any time is indicative of the doubling time in total cases at that point in time (indicative slopes shown).')})
175 |   output$thisIsTheDaily   <- renderText({i18n$t('This is the daily growth in active cases, plotted over the last 20 days.  It can be thought of as the interest rate, compounded daily.')})
176 |   output$positiveIsBad    <- renderText({i18n$t('Positive is bad, negative is good. Progress in control would be indicated by steady decline in growth rate over time, and holding in negative territory.')})
177 |   output$curveFlatteningIndex <- renderText({i18n$t('Curve flattening index')})
178 |   output$thisIsAMeasure   <- renderText({i18n$t('This is a measure of how well a region is flattening the pandemic curve at any point in time.  Positive values mean growth rates are declining at that point in time.')})
179 |   output$noteThisLast     <- renderText({i18n$t('Note, this last plot covers the entire time period of the pandemic, not just the last twenty days.')})
180 | #  output$<- renderText({i18n$t('')})
181 | 
182 |   ##### Observer function -- set country names from url ####
183 |    observe({
184 |      cname <- strsplit(session$clientData$url_hostname, '\\.')[[1]][1]
185 |      if (cname == "au") {
186 |        output$country_name_in_header <- renderText({'Australia'})
187 |        updateSelectizeInput(session, "global_or_country",  selected = "Australia")
188 |      } else if (cname == "us") {
189 |        output$country_name_in_header <- renderText({'United States of America'})
190 |        updateSelectizeInput(session, "global_or_country",  selected = "US")
191 |      } else if (cname == "ca") {
192 |        output$country_name_in_header <- renderText({'Canada'})
193 |        updateSelectizeInput(session, "global_or_country",  selected = "Canada")
194 |      } else if (cname == "de") {
195 |        output$country_name_in_header <- renderText({'Germany'})
196 |        updateSelectizeInput(session, "global_or_country",  selected = "Germany")
197 |      } else if (cname == "it") {
198 |        output$country_name_in_header <- renderText({'Italy'})
199 |        updateSelectizeInput(session, "global_or_country",  selected = "Italy")
200 |      } else if (cname == "co") {
201 |        output$country_name_in_header <- renderText({'Colombia'})
202 |        updateSelectizeInput(session, "global_or_country",  selected = "Colombia")
203 |      } else if (cname == "cn") {
204 |        output$country_name_in_header <- renderText({'China'})
205 |        updateSelectizeInput(session, "global_or_country",  selected = "China")
206 |      } else if (cname == "in") {
207 |        output$country_name_in_header <- renderText({'India'})
208 |        updateSelectizeInput(session, "global_or_country",  selected = "India")
209 |      } else if (cname == "jp") {
210 |        output$country_name_in_header <- renderText({'Japan'})
211 |        updateSelectizeInput(session, "global_or_country",  selected = "Japan")
212 |      } else if (cname == "pe") {
213 |        output$country_name_in_header <- renderText({'Peru'})
214 |        updateSelectizeInput(session, "global_or_country",  selected = "Peru")
215 |      } else if (cname == "ht") {
216 |        output$country_name_in_header <- renderText({'Haiti'})
217 |        updateSelectizeInput(session, "global_or_country",  selected = "Haiti")
218 |      } else if (cname == "ch") {
219 |        output$country_name_in_header <- renderText({'Switzerland'})
220 |        updateSelectizeInput(session, "global_or_country",  selected = "Switzerland")
221 |      } else if (cname == "be") {
222 |        output$country_name_in_header <- renderText({'Belgium'})
223 |        updateSelectizeInput(session, "global_or_country",  selected = "Belgium")
224 |      } else if (cname == "br") {
225 |        output$country_name_in_header <- renderText({'Brazil'})
226 |        updateSelectizeInput(session, "global_or_country",  selected = "Brazil")
227 |      } 
228 |    })
229 |   
230 |   #### Observer function -- Global or Country level ####
231 |   # if we observe that global_or_country is changing, then update the choices in countryFinder
232 |   observe({
233 |     # change data inputs
234 |     list2env(dataList[[input$global_or_country]], envir = parent.env(environment()))
235 | 
236 |     output$asOf           <- renderText({paste(i18n$t('As of'),format(dates[length(dates)], "%d %B %Y"))})
237 | 
238 |     if (input$global_or_country == 'Global') {
239 |       updateSelectizeInput(session, "countryFinder",  choices = ddReg)
240 |       updateSelectizeInput(session, "countryGrowthRate", selected = c("US", "Italy", "Australia", "China"), choices = ddReg)
241 |     } else {
242 | 
243 |       regionsWithFinalInfectionsCases <- cbind(timeSeriesInfections$Region, timeSeriesInfections[,ncol(timeSeriesInfections)])
244 |       regionsWithFinalInfectionsCases <- regionsWithFinalInfectionsCases[order(as.numeric(as.character(regionsWithFinalInfectionsCases[,2]))),]
245 | 
246 |       threeMostInfectionsRegions <- head(tail(regionsWithFinalInfectionsCases,n=4),n=3)
247 |       if (is.vector(threeMostInfectionsRegions)) {
248 |         dim(threeMostInfectionsRegions) <- c(1,2)
249 |       }
250 |       threeMostInfectionsRegions <- rev(as.vector(threeMostInfectionsRegions[,1]))
251 |       
252 |       if (input$global_or_country == 'Canada') {
253 |         ddReg = ddReg[! ddReg %in% c('Diamond Princess','Recovered')] # for some reason, these states do not work.  TOFIX.
254 |       } else if (input$global_or_country == 'China') {
255 |         ddReg = ddReg[! ddReg %in% c('Anhui', 'Guangxi', 'Guizhou', 'Hainan', 'Ningxia', 'Qinghai', 'Tibet', 'Xinjiang')] # for some reason, these states do not work.  TOFIX.
256 |       } else if (input$global_or_country == 'US') {
257 |         ddReg = ddReg[! ddReg %in% c('American Samoa')] # for some reason, these states do not work.  TOFIX.
258 |       }
259 | 
260 |       updateSelectizeInput(session, "countryFinder", choices = ddReg)
261 |       updateSelectizeInput(session, "countryGrowthRate", selected = threeMostInfectionsRegions, choices = ddReg)
262 | 
263 |     }  
264 |   
265 |   })
266 | 
267 | 
268 | #### Reactive expressions for forecast page ####
269 |   yfCast <-reactive({ # subset country for forecast page
270 |     yI <- tsSub(timeSeriesInfections,timeSeriesInfections$Region %in% input$countryFinder)  
271 |     yD <- tsSub(timeSeriesDeaths,timeSeriesDeaths$Region %in% input$countryFinder) 
272 |     yR <- tsSub(timeSeriesRecoveries,timeSeriesRecoveries$Region %in% input$countryFinder)  
273 |     yA <- tsSub(timeSeriesActive,timeSeriesActive$Region %in% input$countryFinder)
274 |     yIplus <- tsSub(cumulative.infections,cumulative.infections$Region %in% input$countryFinder)
275 |     yUndiag <- tsSub(undiagnosed.infections,undiagnosed.infections$Region %in% input$countryFinder)
276 |     list(yI = yI, yD = yD, yR = yR, yA = yA, yIplus = yIplus, yUndiag = yUndiag)
277 |   })
278 | 
279 |   projfCast <- reactive({ # projection for forecast
280 |     projSimple(yfCast()$yA, dates, inWindow = input$fitWinSlider, timeVaryingGrowth = input$modelType)
281 |   })
282 |   
283 |   # adjust slide input given model type
284 |   observe({
285 |     if (input$modelType){
286 |       updateSliderInput(session, "fitWinSlider", value = 14, min = 6, max = 20)
287 |     } else {
288 |       updateSliderInput(session, "fitWinSlider", value = 7, min = 3, max = 10)
289 |     }
290 |   })
291 |   
292 |   plotRange <- reactive({ # get date range to plot
293 |     yA <- yfCast()$yA
294 |     dFrame <- data.frame(dates = as.Date(names(yA), format = "%m.%d.%y"), yA)
295 |     dFrame_without_na <- na.omit(dFrame$yA)
296 |     if (max(dFrame_without_na)>200) {minDate <- min(dFrame$dates[dFrame_without_na>20]); maxDate <- max(dFrame$dates)+10} else {
297 |       minDate <- min(dFrame$dates); maxDate <- max(dFrame$dates)+10
298 |     }
299 |     if (input$timeRange) minDate <- maxDate - 120 # recent time is last 4 months
300 |     list(minDate, maxDate)
301 |   })
302 | 
303 |   deathsInCountries <- reactive({
304 |     subset(timeSeriesDeaths, timeSeriesDeaths$Region %in% input$countryGrowthRate)
305 |   })
306 | 
307 |   
308 |   ##### Days since last... Table #####
309 |   output$daysSinceLast <- renderTable({
310 | 
311 |     countryNames       <- c()
312 |     daysOfZeroNewCases <- c()
313 |     daysOfZeroDeaths   <- c()
314 | 
315 |     for (country in input$countryGrowthRate) {
316 |       countryNames <- append(countryNames, country)
317 |       yI <- subset(log100cases(),             log100cases()$Region == country)
318 |       yD <- subset(deathsInCountries(), deathsInCountries()$Region == country)
319 | 
320 |       # remove column with name Region
321 |       yI <- yI[,-1]
322 |       yD <- yD[,-1]
323 | 
324 |       dailyNewCases <- diff(as.numeric(yI))
325 |       dailyDeaths   <- diff(as.numeric(yD))
326 | 
327 |       moreThanZeroNewCases <- which(dailyNewCases > 0)
328 |       moreThanZeroDeaths   <- which(dailyDeaths > 0)
329 | 
330 |       if (length(moreThanZeroDeaths) == 0) {
331 |         daysOfZeroDeaths   <- append(daysOfZeroDeaths,'N/A')
332 |       } else {
333 |         daysOfZeroDeaths   <- append(daysOfZeroDeaths,  length(yD) - moreThanZeroDeaths[length(moreThanZeroDeaths)]     - 1)
334 |       }
335 | 
336 |       if (length(moreThanZeroNewCases) == 0) {
337 |         daysOfZeroNewCases <- append(daysOfZeroNewCases, 'N/A')
338 |       } else {
339 |         daysOfZeroNewCases <- append(daysOfZeroNewCases,length(yI) - moreThanZeroNewCases[length(moreThanZeroNewCases)] - 1)
340 |       }
341 | 
342 |     }
343 | 
344 |     out <- data.frame(countryNames, daysOfZeroNewCases, daysOfZeroDeaths)
345 |     colnames(out) <- c(i18n$t("Country/region"), i18n$t("Days since last new case"), i18n$t("Days since last death"))
346 |     format(out, big.mark = ",")
347 | 
348 |   }, rownames = FALSE, align = "lcc")
349 |  
350 | 
351 | 
352 |   ##### Raw stats #####  
353 |   output$rawStats <- renderTable({
354 |     yA <- yfCast()$yA
355 |     yD <- yfCast()$yD
356 |     yI <- yfCast()$yI
357 |     nn <-length(yI)
358 |     if (is.na(yA[nn])) nn <- nn-1
359 |     out <- as.integer(c(yI[nn], yD[nn]))
360 |     dim(out) <-c(1,2)
361 |     colnames(out) <- c(i18n$t("Total infections"), i18n$t("Deaths"))
362 |     format(out, big.mark = ",")
363 |   }, rownames = FALSE)
364 |   
365 | ##### Raw plot #####
366 |   output$activeCasesPlot <- renderPlotly({
367 |     if (input$countryFinder != '') {
368 |       yA <- yfCast()$yA
369 |       yA <- data.frame(dates = as.Date(names(yA), format = "%m.%d.%y"), yA)
370 |       yAmax <- max(yA$yA, na.rm=TRUE)
371 |       lDat <- projfCast()$lDat
372 |       date_at_peak <- projfCast()$date_at_peak
373 |       value_at_peak <- projfCast()$value_at_peak
374 |       pDat <- merge(yA, lDat, all = TRUE)
375 |       yMax <- max(c(lDat$fit, yA$yA), na.rm = TRUE)*1.05
376 |       yMax <- min(c(2*yAmax, yMax)) # twice the data ymax, or the fit max, whatever is smaller
377 |       if (input$activeCasesPlotIsLinear) {
378 |         plotType <- 'linear'
379 |         theRange <- list(0, yMax)
380 |       } else {
381 |         plotType <- 'log'
382 |         theRange <- list(log10(1), log10(yMax))
383 |       }
384 |       fig <- plot_ly(pDat, type = "scatter", mode = "none") %>%
385 |                 add_trace(y = ~fit,
386 |                           x = ~dates, 
387 |                           mode = "lines", 
388 |                           line = list(color = clrDark), 
389 |                           name = i18n$t("Best fit"), 
390 |                           hoverinfo = "text+name", 
391 |                           text = paste(format(pDat$dates, shortDateFormat), format(round(pDat$fit, 0), big.mark = ","))) %>%
392 |                 add_trace(y = ~lwr,
393 |                           x = ~dates,
394 |                           mode = "lines", 
395 |                           line = list(color = clrDark, dash = "dash"), 
396 |                           name = "CI lower bound",
397 |                           hoverinfo = "text+name", 
398 |                           text = paste(format(pDat$dates, shortDateFormat), format(round(pDat$lwr, 0), big.mark = ","))) %>%
399 |                 add_trace(y = ~upr, 
400 |                           x = ~dates,
401 |                           mode = "lines", 
402 |                           line = list(color = clrDark, dash = "dash"), 
403 |                           name = "CI upper bound",
404 |                           hoverinfo = "text+name", 
405 |                           text = paste(format(pDat$dates, shortDateFormat), format(round(pDat$upr, 0), big.mark = ","))) %>%
406 |                 add_trace(y = ~yA, 
407 |                           x = ~dates,
408 |                           mode = "markers", 
409 |                           marker = list(color = clrLight), 
410 |                           name = i18n$t("Active cases"),
411 |                           hoverinfo = "text+name", 
412 |                           text = paste(format(pDat$dates, shortDateFormat), format(round(pDat$yA, 0), big.mark = ","))) %>%
413 |                 layout(showlegend = FALSE, 
414 |                        yaxis = list(type  = plotType,
415 |                                     range = theRange,
416 |                                     title = list(text = i18n$t("Confirmed active cases")),
417 |                                     fixedrange = TRUE),
418 |                        xaxis = list(range = plotRange(),
419 |                                     title = list(text = ""),
420 |                                     fixedrange = TRUE),
421 |                        title = list(text = input$countryFinder)
422 |                 ) %>%
423 |                 config(displayModeBar = FALSE)
424 |       if (!is.null(value_at_peak)){
425 |           fig %>% add_trace(y = c(0,value_at_peak), 
426 |                   x = c(date_at_peak,date_at_peak),
427 |                   mode = "lines", 
428 |                   line = list(color = clrLight),
429 |                   name = i18n$t("Estimated peak"),
430 |                   hoverinfo = "text+name",
431 |                   text = format(date_at_peak, "%b, %d"))
432 |       } else {fig}
433 |     }
434 |   })
435 |   
436 | ##### New cases ##### 
437 |   output$newCases <- renderPlotly({
438 |     if (input$countryFinder != '') {
439 |       yI <- yfCast()$yI # Infected
440 |       yD <- yfCast()$yD # Deaths
441 |       
442 |       # Calculate new cases
443 |       newCases <- diff(yI) 
444 |       # Format the date
445 |       newCases <- data.frame(dates = as.Date(names(newCases), format = "%m.%d.%y"), newCases) 
446 |       
447 |       # Calculate daily deaths
448 |       dailyDeaths <- diff(yD) 
449 |       
450 |       # Plot new cases
451 |       fig1 <- plot_ly(newCases) %>%
452 |         add_bars(y = ~newCases,
453 |                  x = ~dates, 
454 |                  name = i18n$t("New cases"), 
455 |                  marker = list(color = clrOrange), 
456 |                  text = paste(format(newCases$dates, shortDateFormat),format(newCases$newCases, big.mark = ",")),
457 |                  hoverinfo = "text+name"
458 |         ) %>%
459 |         layout(yaxis = list(title = list(text = i18n$t("New cases")))
460 |         )  
461 |       
462 |       # Plot daily deaths
463 |       fig2 <- plot_ly(newCases) %>%
464 |         add_bars(y = ~dailyDeaths,
465 |                  x = ~dates,
466 |                  name = i18n$t("Daily deaths"),
467 |                  marker = list(color = clrDark),
468 |                  text = paste(format(newCases$dates, shortDateFormat),format(dailyDeaths, big.mark = ",")),
469 |                  hoverinfo = "text+name"
470 |         ) %>%
471 |         layout(xaxis = list(range = plotRange(),
472 |                             title = list(text = i18n$t("Date"))),
473 |                yaxis = list(title = list(text = i18n$t("Deaths")), side = 'left')
474 |         )
475 |       
476 |       # Composite
477 |       fig <- subplot(fig1, fig2, heights = c(0.8,0.2), nrows=2, shareX = TRUE, titleY = TRUE
478 |       ) %>%
479 |         config(displayModeBar = FALSE) %>%
480 |         layout(legend = list(x=0))
481 |     }
482 |   })
483 | 
484 | ##### Undiagnosed plot #####  
485 |   output$undiagPlot <- renderPlotly({
486 |     if (input$countryFinder != '') {
487 |     rawI <- yfCast()$yI # diagnosed infection totals at t
488 |     estI <- yfCast()$yIplus # estimated true infections at t (diagnosed + undiagnosed)
489 |     rawIdates <- as.Date(names(rawI), format = '%m.%d.%y')
490 |     estIdates <- as.Date(names(estI), format = '%m.%d.%y')
491 |     fig <- plot_ly(type = "scatter", mode = "none")
492 |     if (sum(estI)>0){
493 |       fig <- add_trace(fig,
494 |                        y = ~estI,
495 |                        x = estIdates,
496 |                        mode = "lines", 
497 |                        line = list(color = clrDark), 
498 |                        name = paste(i18n$t("Diagnosed"), " + ", i18n$t("Undiagnosed")), 
499 |                        hoverinfo = "text+name",
500 |                        text = paste(format(estIdates, shortDateFormat), format(round(estI), big.mark = ",")))
501 |     }
502 |       fig <- add_trace(fig, 
503 |                 y = ~rawI,
504 |                 x = rawIdates,
505 |                 mode = "lines+markers",
506 |                 marker = list(color = clrLight), 
507 |                 line = list(color = clrLight), 
508 |                 name = i18n$t("Diagnosed"), 
509 |                 hoverinfo = "text+name",
510 |                 text = paste(format(rawIdates, shortDateFormat), format(rawI, big.mark = ","))) %>%
511 |       layout(xaxis = list(range = plotRange(),
512 |                           title = list(text = i18n$t("Date"))),
513 |              yaxis = list(title = list(text = i18n$t("Total infections")), 
514 |                           side = 'left'),
515 |              legend = list(x = 0, y = 1.05)) %>%
516 |       config(displayModeBar = FALSE)
517 |     }
518 |   })
519 |     
520 | ##### Detection Plot #####   
521 |   output$detPlot <- renderPlotly({
522 |     if (input$countryFinder != '') {
523 |       # get data subsets
524 |       datI <- yfCast()$yI
525 |       datD <- yfCast()$yD
526 |       # generate detection vector
527 |       detVec <- detRate(infd = datI, deaths = datD, pointEst = FALSE, caseFatalityRatio = input$fatalityRatioSlider)*100
528 |       # smooth with moving average
529 |       detVec <- stats::filter(detVec, rep(1 / 3, 3), sides = 1) #3-day moving average
530 |       # organise into dataframe for plotting
531 |       pDet <- data.frame(dates = as.Date(names(datI), format = "%m.%d.%y"), detVec)
532 |       xRange <- as.list(range(na.omit(pDet)$dates))
533 |       # make the plot
534 |       fig <- plot_ly(pDet, type = "scatter", mode = "none", showlegend = FALSE)
535 |       fig <- fig %>% add_trace(y = ~detVec,
536 |                                x = ~dates,
537 |                                mode = "lines+markers", 
538 |                                name = i18n$t("Detection"),
539 |                                hoverinfo = "text+name", 
540 |                                text = paste(format(pDet$dates, shortDateFormat), round(pDet$detVec, 1), "%"))
541 |       fig <- fig %>% layout(xaxis = list(title = list(text = i18n$t("Date")),
542 |                                          range = plotRange()),
543 |                             yaxis = list(title = list(text = i18n$t("Cases successfully detected %")))
544 |       ) %>%
545 |         config(displayModeBar = FALSE)
546 |     }
547 |   })
548 |   
549 | 
550 |   log100cases <- reactive({
551 |     subset(timeSeriesInfections, timeSeriesInfections$Region %in% input$countryGrowthRate)
552 |   })
553 | 
554 | ##### Log100 cases Plot ##### 
555 |   output$log100casesPlot <- renderPlotly({
556 |       yI <- yfCast()$yI
557 |       yI <- subset(yI, yI >= 100)
558 |       yI <- data.frame(yI)
559 |       fig <- plot_ly(yI, type = "scatter", mode = "none")
560 |       fig <- fig %>% layout(showlegend = TRUE, 
561 |                        height = 600,
562 |                        yaxis = list(type = "log",
563 |                                     title = list(text = i18n$t("Confirmed cases (log scale)")),
564 |                                     fixedrange = TRUE),
565 |                        xaxis = list(range = plotRange(),
566 |                                     title = list(text = i18n$t("Number of days since 100 cases")),
567 |                                     fixedrange = TRUE),
568 |                        legend = list(orientation="h", xanchor="center",x=0.5,y=-0.2)
569 |                 )
570 |       fig <- fig %>% config(displayModeBar = FALSE)
571 |       doubling_lines <- c(2,3,5)
572 |       ymax <- max(log100cases()[,-1],na.rm=TRUE)
573 |       ymax <- 2^(log2(ymax)*1.05) # just a little higher
574 |       for (doubling_line in doubling_lines) {
575 |         linename <- paste(i18n$t('Doubling every'),doubling_line,i18n$t('days'))
576 |         fig <- fig %>% add_trace(x    = c(0,   log2(ymax/100)*doubling_line),
577 |                                  y    = c(100, ymax),
578 |                                  mode = "lines",
579 |                                  line = list(color = clrLight, dash = "dot"),
580 |                                  hoverinfo = "text",
581 |                                  text = linename,
582 |                                  name = linename)
583 |       }
584 |       for (country in input$countryGrowthRate) {
585 |         myY <- subset(log100cases(), log100cases()$Region == country)
586 |         # remove column with name Region
587 |         myY <- myY[,-1]
588 |         # remove dates as these are unnecessary
589 |         myY <- as.vector(t(myY))
590 |         # only get values bigger than 100
591 |         myY <- subset(myY, myY >= 100)
592 |         x   <- 0:(length(myY)-1)
593 |         fig <- fig %>% add_trace(y    = myY,
594 |                                  x    = x,
595 |                                  mode = "lines",
596 |                                  name = country,
597 |                                  hoverinfo = "text+name",
598 |                                  text = paste(format(myY,big.mark=","), "total cases at", x, "days since 100 cases"))
599 |       }
600 |       fig
601 | })
602 | 
603 | 
604 | ##### Forecast metrics ##### 
605 |   output$forecastMetrics <- renderText({
606 |     if (input$countryFinder == '') {
607 |       please_select_a_country
608 |     } else {
609 |     if (input$modelType) {
610 |       if (is.null(projfCast()$value_at_peak)) {
611 |         i18n$t("Active cases estimated to peak beyond the forecast horizon")
612 |       } else if (is.null(projfCast()$date_at_peak)){
613 |         i18n$t("Active cases peaked in the past")
614 |       } else {
615 |         paste(i18n$t("Active cases estimated to peak at"), format(as.integer(projfCast()$value_at_peak), big.mark=","),i18n$t("cases on"), format(projfCast()$date_at_peak, "%d %B"))
616 |       }
617 |     } else {
618 |         doubTime <- round(projfCast()$doubling_time, 1)
619 |         if (doubTime > 0) {
620 |           dTime <- paste(i18n$t("Doubling time is"), doubTime, i18n$t('days'))
621 |         } else {
622 |           dTime <- paste(i18n$t("Halving time is"), -doubTime, i18n$t('days'))
623 |         }
624 |     }
625 |     }
626 |   })
627 | 
628 | 
629 | ##### Detection rate #####    
630 |   output$detRate <- renderText({
631 |     if (input$countryFinder == '') {
632 |       please_select_a_country
633 |     } else {
634 |       yI <- yfCast()$yI
635 |       yD <- yfCast()$yD
636 |       dR<-round(detRate(yI, yD, caseFatalityRatio = input$fatalityRatioSlider), 4)*100
637 |       if (is.na(dR)) i18n$t("Insufficient data for estimation") else paste(dR,'%')
638 |     }
639 |   })
640 |   
641 | ##### Prediction table confirmed #####    
642 |   output$tablePredConf <- renderTable({
643 |     if (input$countryFinder != '') {
644 |       yA <- yfCast()$yA
645 |       lDat <- projfCast()$lDat
646 |       nowThen <- format(as.integer(c(tail(yA[!is.na(yA)], 1), tail(lDat$lwr,1), tail(lDat$upr,1))), big.mark = ",")
647 |       nowThen <- c(nowThen[1], paste(nowThen[2], "-", nowThen[3]))
648 |       dim(nowThen) <- c(1, 2)
649 |       colnames(nowThen)<-c(i18n$t("Now"), i18n$t("In 10 days (min-max)"))
650 |       nowThen
651 |     }
652 |   }, rownames = FALSE)
653 |   
654 | ##### Prediction table true #####    
655 |   output$tablePredTrue <- renderTable({
656 |     if (input$countryFinder != '') {
657 |       yA <- yfCast()$yA
658 |       yD <- yfCast()$yD
659 |       yI <- yfCast()$yI
660 |       yUndiag <- yfCast()$yUndiag
661 |       yIplus <- yfCast()$yIplus
662 |       dRate <- detRate(yI, yD, caseFatalityRatio = input$fatalityRatioSlider)
663 |       nowDiag <- tail(yA[!is.na(yA)], 1)
664 |       nowUndet <- nowDiag/dRate - nowDiag
665 |       nowUndiag <- yUndiag[length(yUndiag)]
666 |       if (nowUndiag<0) nowUndiag <- 0
667 |       if (sum(yIplus)==0) nowUndiag <- NA
668 |       nowTotal <- nowDiag+nowUndiag+nowUndet
669 |       nowTable <- format(round(c(nowDiag, nowUndiag, nowUndet, nowTotal), 0), big.mark = ",")
670 |       dim(nowTable) <- c(4, 1)
671 |       rownames(nowTable)<-c(i18n$t("Diagnosed"), i18n$t("Undiagnosed"), i18n$t("Undetected"), i18n$t("Total"))
672 |       nowTable
673 |     }
674 |   }, rownames = TRUE, colnames = FALSE)
675 |   
676 | ##### Reactive expressions for growth page #####    
677 |   growthSub <- reactive({
678 |     subset(timeSeriesActive, timeSeriesActive$Region %in% input$countryGrowthRate)
679 |   })
680 | 
681 | 
682 | ##### Curve-flattening #####    
683 |   output$cfi <- renderPlotly({
684 |     pDat <- growthSub()
685 |     pMat<-as.matrix(log(pDat[,-1]))
686 |     row.names(pMat)<-pDat$Region
687 |     cfiDat<-apply(pMat, MARGIN = 1, FUN = "cfi")
688 |     cfiDat[!is.finite(cfiDat)]<-0
689 |     dateSub<-3:length(dates) # date subset
690 |     for (cc in 1:ncol(cfiDat)){
691 |       cfiSmooth<-loess(cfiDat[,cc]~as.numeric(dates[dateSub]))
692 |       cfiDat[,cc] <- predict(cfiSmooth, newdata = as.numeric(dates[dateSub]))
693 |     }
694 |     yRange <- as.list(range(cfiDat)*1.05)
695 |     cfiDat <- data.frame(dates = dates[dateSub], cfiDat)
696 |     fig <- plot_ly(type = "scatter", mode = "none", name = "")
697 |     for (cc in 2:ncol(cfiDat)){
698 |       fig <- fig %>% add_trace(y = cfiDat[,cc],
699 |                                x = dates[dateSub],
700 |                                mode = "lines",
701 |                                name = colnames(cfiDat)[cc],
702 |                                hoverinfo = "text+name", 
703 |                                text = paste(format(cfiDat$dates, shortDateFormat), round(cfiDat[,cc], 2)))
704 |     }
705 |     fig <- fig %>% layout(xaxis = list(title = list(text = i18n$t("Date"))),
706 |                           yaxis = list(title = list(text = i18n$t("Curve-flattening index")),
707 |                                        range = yRange)
708 |                     ) %>%
709 |                     config(displayModeBar = FALSE)
710 |     
711 |   })
712 |   
713 | ##### Growth rate #####    
714 |   output$growthRate <- renderPlotly({
715 |     pDat <- growthSub()
716 |     gRate <- as.matrix(growthRate(pDat[,-1], inWindow = ncol(pDat)-2)) #daily growth over all time
717 |     gRateMA <- apply(gRate, # get moving average (three day window)
718 |                      MARGIN = 1, 
719 |                      FUN = function(x, n = 3){stats::filter(x, rep(1 / n, n), sides = 1)})
720 |     gRateMA[is.infinite(gRateMA)]<-NA #remove Infs
721 |     # reorganise for plotting
722 |     gRateMA <- data.frame(dates = as.Date(colnames(gRate), format = "%m.%d.%y"), gRateMA)
723 |     if (nrow(gRateMA)>20) gRateMA <-tail(gRateMA, 20)
724 |     colnames(gRateMA)[-1] <- pDat$Region
725 |     # and plot
726 |     fig <- plot_ly(gRateMA, type = "scatter", mode = "none")
727 |     for (cc in 2:ncol(gRateMA)){
728 |       fig <- fig %>% add_trace(y = gRateMA[,cc],
729 |                                x = ~dates,
730 |                                mode = "lines", 
731 |                                name = colnames(gRateMA)[cc],
732 |                                hoverinfo = "text+name", 
733 |                                text = paste(format(gRateMA$dates, shortDateFormat), round(gRateMA[,cc], 1), "%"))
734 |     }
735 |     fig <- fig %>% layout(xaxis = list(title = list(text = i18n$t("Date"))),
736 |                           yaxis = list(title = list(text = i18n$t("Growth rate (% per day)")))
737 |                     ) %>%
738 |                    config(displayModeBar = FALSE)
739 |   })
740 | 
741 |   
742 | } # end of server expression
743 | 
744 | 
745 | shinyApp(ui = htmlTemplate('base.html'), server)
746 | 
747 | 


--------------------------------------------------------------------------------
/base.html:
--------------------------------------------------------------------------------
  1 | <!DOCTYPE html>
  2 | <html lang="en" class="no-js">
  3 | <head>
  4 |   <meta charset="utf-8" />
  5 |   <meta content="width=device-width, initial-scale=1.0" name="viewport" />
  6 |   <meta content="IE=edge" http-equiv="X-UA-Compatible" />
  7 |   <title>Coronavirus 10-day forecast</title>
  8 |   <link rel="icon" href="https://cms.unimelb.edu.au/__data/assets/image/0006/1456044/favicon.gif" type="image/gif" sizes="16x16">
  9 |   <link rel="stylesheet" href="https://d2h9b02ioca40d.cloudfront.net/v8.0.1/uom.css">
 10 |  <!-- <script src="https://d2h9b02ioca40d.cloudfront.net/v8.0.1/uom.js"></script>-->
 11 |   <script async src="https://www.googletagmanager.com/gtag/js?id=UA-105861992-6"></script>
 12 |   <script>
 13 |     window.dataLayer = window.dataLayer || [];
 14 |     function gtag(){dataLayer.push(arguments);}
 15 |     gtag('js', new Date());
 16 |     gtag('config', 'UA-105861992-6');
 17 |   </script>
 18 |   {{ headContent() }}
 19 |   <script src="https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML" type="text/javascript"></script>
 20 | </head>
 21 | 
 22 | <body>
 23 |   <div class="uomcontent" id="top">
 24 |     <div class="page-header" role="banner">
 25 |     <header style="background:#094183;">
 26 |       <a class="page-header-logo" style="margin-left:29px;text-decoration:none;" href="https://www.unimelb.edu.au">
 27 |         <img src="https://d2h9b02ioca40d.cloudfront.net/v8.0.1/assets/logo-105a9.svg" width="117" height="117" alt="The University of Melbourne homepage">
 28 |       </a>
 29 |       <nav class="page-header-navigation" aria-label="breadcrumbs" style="display:inline-block;">
 30 |         <ol class="page-local-history" itemscope itemtype="http://schema.org/BreadcrumbList">
 31 |           <li class="root" itemprop="itemListElement" itemscope itemtype="http://schema.org/ListItem" style="margin-left:0px;">
 32 |             <a href="/" title="Home" itemprop="item" style="text-decoration:none;">
 33 |               <span itemprop="name">
 34 |                 <span data-icon="home" data-bound-iconhelper="ck8hroqvj0001315vhnfr2bie">
 35 |                   <svg class="icon" role="img" focusable="false">
 36 |                     <use xlink:href="#icon-home">
 37 |                       <svg id="icon-home" viewBox="0 0 48 48" width="18px" length="auto" style="color:white;">
 38 |                         <path d="M20 41V29h8v12h10V25h6L24 7 4 25h6v16h10z"></path>
 39 |                       </svg>
 40 |                     </use>
 41 |                   </svg>
 42 |                 </span>Coronavirus 10-day forecast
 43 |               </span>
 44 |             </a>
 45 |           </li>
 46 |         </ol>
 47 |       </nav>
 48 |     </header>
 49 |     </div>
 50 |   </div>
 51 | 
 52 |   <div class="uomcontent">
 53 |     <div class="page-inner" style="padding-top:0px!important;">
 54 |       <div role="main">
 55 |         <br><br><br><br>
 56 | 
 57 |         <header>
 58 |           <h1 class="center" style="padding-top:20px;">
 59 |             {{ textOutput(outputId = "country_name_in_header") }} {{ textOutput(outputId = "siteName") }}<br>
 60 |             {{ textOutput(outputId = "asOf") }}
 61 |           </h1>
 62 |           <br>
 63 |           <h3>
 64 |             {{ textOutput(outputId = "byline") }}
 65 |           </h3>
 66 |         </header>
 67 | 
 68 |         <br>
 69 | 
 70 |         <div class="form-group shiny-input-container center">
 71 |           <fieldset>
 72 |             <label class="control-label" for="translationLanguage">{{ textOutput(outputId = "selectALanguage") }}</label>
 73 |             <select class="form-control" style="padding-bottom:6px;padding-top:6px;border-color:lightgray;background-color:white;border-radius:3px;" id="translationLanguage" onchange="window.location.href = '/?lang='.concat(this.value);">
 74 |               <option value="en">English</option>
 75 |               <option value="de">Deutsch</option>
 76 |               <option value="ru">Russian</option>
 77 |               <option value="tr">Turkish</option>
 78 |               <option value="hi">Hindi</option>
 79 |               <option value="es">Español</option>
 80 |             </select>
 81 |           </fieldset>
 82 |         </div>
 83 | 
 84 |         <script>
 85 |           const urlParams = new URLSearchParams(window.location.search);
 86 |           const urlLanguage = urlParams.get('lang');
 87 |           if (urlLanguage == 'tr') {
 88 |             $('#translationLanguage').val('tr')
 89 |           } else if (urlLanguage == 'hi') {
 90 |             $('#translationLanguage').val('hi')
 91 |           } else if (urlLanguage == 'de') {
 92 |             $('#translationLanguage').val('de')
 93 |           } else if (urlLanguage == 'es') {
 94 |             $('#translationLanguage').val('es')
 95 |           } else if (urlLanguage == 'ru') {
 96 |             $('#translationLanguage').val('ru')
 97 |           } else {
 98 |             $('#translationLanguage').val('en')
 99 |           }
100 |         </script>
101 | 
102 |         <br>
103 | 
104 |         <div class="form-group shiny-input-container center" id="div_global_or_country">
105 |           <label class="control-label" for="global_or_country">{{ textOutput(outputId = "selectGlobal") }}</label>
106 |           {{ selectizeInput(inputId = "global_or_country",
107 |                             label   = "",
108 |                             choices = c("Global","Australia","Belgium","Brazil","Canada","China","Colombia","Germany","Haiti","Italy","India","Japan","Peru","Switzerland","US"),
109 |                             selected = "Global") }}
110 |           <script type="application/json" data-for="global_or_country" data-nonempty="">{}</script>
111 |         </div>
112 | 
113 |         <div id="go_to_main_site" style="display:none;" class="center">
114 |             {{ textOutput(outputId = "otherCountries") }}
115 |             <a class="button-small" href="http://covid19forecast.science.unimelb.edu.au">Main site</a>
116 |         </div>
117 | 
118 |         <div class="center">
119 |           <p>
120 |             {{ textOutput(outputId = "specificSites") }}
121 |             <a href="http://cn.covid19forecast.science.unimelb.edu.au">{{ imageOutput("flagChina",       inline = TRUE) }}</a><!-- pop: 1.4 billion -->
122 |             <a href="http://in.covid19forecast.science.unimelb.edu.au">{{ imageOutput("flagIndia",       inline = TRUE) }}</a><!-- pop: 1.3 billion -->
123 |             <a href="http://us.covid19forecast.science.unimelb.edu.au">{{ imageOutput("flagUS",          inline = TRUE) }}</a><!-- pop: 328 million -->
124 |             <a href="http://br.covid19forecast.science.unimelb.edu.au">{{ imageOutput("flagBrazil",      inline = TRUE) }}</a><!-- pop: 209 million -->
125 |             <a href="http://jp.covid19forecast.science.unimelb.edu.au">{{ imageOutput("flagJapan",       inline = TRUE) }}</a><!-- pop: 126 million -->
126 |             <a href="http://de.covid19forecast.science.unimelb.edu.au">{{ imageOutput("flagGermany",     inline = TRUE) }}</a><!-- pop: 83 million -->
127 |             <a href="http://it.covid19forecast.science.unimelb.edu.au">{{ imageOutput("flagItaly",       inline = TRUE) }}</a><!-- pop: 60 million -->
128 |             <a href="http://co.covid19forecast.science.unimelb.edu.au">{{ imageOutput("flagColombia",    inline = TRUE) }}</a><!-- pop: 49 million -->
129 |             <a href="http://ca.covid19forecast.science.unimelb.edu.au">{{ imageOutput("flagCanada",      inline = TRUE) }}</a><!-- pop: 37.59 million -->
130 |             <a href="http://pe.covid19forecast.science.unimelb.edu.au">{{ imageOutput("flagPeru",        inline = TRUE) }}</a><!-- pop: 32 million -->
131 |             <a href="http://au.covid19forecast.science.unimelb.edu.au">{{ imageOutput("flagAustralia",   inline = TRUE) }}</a><!-- pop: 25 million -->
132 |             <a href="http://be.covid19forecast.science.unimelb.edu.au">{{ imageOutput("flagBelgium",     inline = TRUE) }}</a><!-- pop: 11.46 million -->
133 |             <a href="http://ht.covid19forecast.science.unimelb.edu.au">{{ imageOutput("flagHaiti",       inline = TRUE) }}</a><!-- pop: 11.12 million -->
134 |             <a href="http://ch.covid19forecast.science.unimelb.edu.au">{{ imageOutput("flagSwitzerland", inline = TRUE) }}</a><!-- pop: 8.6 million -->
135 |           </p>
136 |         </div>
137 | 
138 |         <div class="center">
139 |           {{ textOutput(outputId = "skipTo1") }} <a class="button-small" onclick="jQuery('html,body').animate({scrollTop: jQuery('#10-day-forecast').offset().top}, 1000);">10-day forecasts</a>
140 |                                                  <a class="button-small" onclick="jQuery('html,body').animate({scrollTop: jQuery('#growth-rates').offset().top},    1000);">Growth rates</a>
141 |                                                  <a class="button-small" onclick="jQuery('html,body').animate({scrollTop: jQuery('#about').offset().top},           1000);">About</a>
142 |         </div>
143 | 
144 |         <script>
145 |           var hostname = window.location.hostname;
146 |           if (['covid19forecast','127','localhost'].indexOf(hostname.split('.')[0]) == -1) {
147 |             document.getElementById('div_global_or_country').style.display = 'none';
148 |             document.getElementById('go_to_main_site').style.display = 'block';
149 |           }
150 |           var language =  window.navigator.userLanguage || window.navigator.language;
151 |         </script>
152 | 
153 |     <section id="10-day-forecast">
154 |       <header>
155 |         <h1>{{ textOutput(outputId = "TenDayForecasts") }}</h1>
156 |       </header>
157 |       <br>
158 | 
159 |       <div class="left-panel">
160 |         <h2>{{ textOutput(outputId = "location") }}</h2>
161 |         <div class="form-group shiny-input-container">
162 |           <label class="control-label" for="countryFinder">{{ textOutput(outputId = "selectCountryRegion1") }}</label>
163 |           {{ selectizeInput(inputId  = "countryFinder",
164 |                          label    = "",
165 |                          choices  = ddReg) }}
166 |           <script type="application/json" data-for="countryFinder" data-nonempty="">{}</script>
167 |         </div>
168 | 
169 |         <h3>{{ textOutput(outputId = "rawCaseNumbers") }}</h3>
170 |         {{ tableOutput(outputId = "rawStats") }}
171 | 
172 |         <h3>{{ textOutput(outputId = "activeCases") }}</h3>
173 |         {{ tableOutput(outputId = "tablePredConf") }}
174 |         <p>{{ textOutput(outputId = "activeCasesP") }}</p>
175 | 
176 |         <h5>{{ textOutput(outputId = "forecastMetricsH") }}</h5>
177 |         <div class="center" style="padding-bottom: 10px">{{ textOutput(outputId = "forecastMetrics") }}</div>
178 | 
179 |         <br>
180 | 
181 |         <div id="modelType" class="form-group shiny-input-radiogroup shiny-input-container">
182 |           <label class="control-label" for="modelType"></label>
183 |           <div class="shiny-options-group">
184 |             <legend>
185 |               {{ textOutput(outputId = "growthLegend", inline = TRUE) }}
186 |             </legend>
187 |             <input type="radio" name="modelType" value="True" id="growthTrue"/>
188 |             <label for="growthTrue">
189 |               {{ textOutput(outputId = "timeVaryingGrowth", inline=TRUE) }}
190 |             </label>
191 |             <input type="radio" name="modelType" value="False" checked="checked" id="growthFalse"/>
192 |             <label for="growthFalse">
193 |               {{ textOutput(outputId = "constantGrowth", inline=TRUE) }}
194 |             </label>
195 |           </div>
196 |         </div>
197 | 
198 |         <br>
199 | 
200 |         <div id="timeRange" class="form-group shiny-input-radiogroup shiny-input-container">
201 |           <label class="control-label" for="timeRange"></label>
202 |           <div class="shiny-options-group">
203 |             <legend>
204 |               {{ textOutput(outputId = "timeLegend", inline = TRUE) }}
205 |             </legend>
206 |             <input type="radio" name="timeRange" value="False" id="timeRangeFalse"/>
207 |             <label for="timeRangeFalse">
208 |               {{ textOutput(outputId = "allTime", inline = TRUE) }}
209 |             </label>
210 |             <input type="radio" name="timeRange" value="True" checked="checked" id="timeRangeTrue"/>
211 |             <label for="timeRangeTrue">
212 |               {{ textOutput(outputId = "recentTime", inline = TRUE) }}
213 |             </label>
214 |           </div>
215 |         </div>
216 | 
217 | 
218 |         <br>
219 |         <label class="control-label" for="fitWinSlider">{{ textOutput(outputId = "fitWindow") }}</label>
220 |         {{ sliderInput(inputId = "fitWinSlider", min = 3, max = 10, value = 7, ticks = FALSE, label = "", post = " days") }}
221 |         <p>{{ textOutput(outputId = "growthRatesP") }}</p>
222 | 
223 |         <h2>{{ textOutput(outputId = "detection") }}</h2>
224 | 
225 |         <p>{{ textOutput(outputId = "casesSuccessfullyDetected") }}</p>
226 |         <div class="center" style="padding-bottom: 24px">{{ textOutput(outputId = "detRate") }}</div>
227 | 
228 |         <p>{{ textOutput(outputId = "possibleNumberOf") }} {{ tableOutput(outputId = "tablePredTrue") }}</p>
229 | 
230 |         <label class="control-label" for="fatalityRatioSlider">{{ textOutput(outputId = "caseFatalityRatio") }}</label>
231 |          {{ sliderInput(inputId = "fatalityRatioSlider", min=0.5, max=3.5, value=1.6, step=0.1, label = "", post="%") }}
232 |         <p>{{ textOutput(outputId = "toSeeHowThe") }}</p>
233 |         <p>{{ textOutput(outputId = "takeTheseLast") }}</p>
234 | 
235 |         <p>{{ textOutput(outputId = "theLastPlot") }}</p>
236 |         <p>{{ textOutput(outputId = "detectionCanOnly") }}</p>
237 |         <p>For more information, see <a class="button-small" onclick="jQuery('html,body').animate({scrollTop: jQuery('#about').offset().top}, 1000);">About</a></p>
238 |       </div>
239 | 
240 |       <div class="right-panel">
241 |         {{ plotlyOutput("activeCasesPlot") }}
242 |         <div id="activeCasesPlotIsLinear" class="form-group shiny-input-radiogroup shiny-input-container">
243 |           <label class="control-label" for="activeCasesPlotIsLinear"></label>
244 |           <div class="shiny-options-group">
245 |             <div class="radio">
246 |               <label>
247 |                 {{ textOutput("linearScale") }}
248 |                 <input type="radio" name="activeCasesPlotIsLinear" value="True" checked="checked"/>
249 |               </label>
250 |             </div>
251 |             <div class="radio">
252 |               <label>
253 |                 {{ textOutput("logScale") }}
254 |                 <input type="radio" name="activeCasesPlotIsLinear" value="False"/>
255 |               </label>
256 |             </div>
257 |           </div>
258 |         </div>
259 | 
260 |         <p></p>
261 |         {{ plotlyOutput("undiagPlot") }}
262 |         <p></p>
263 |         {{ plotlyOutput("newCases") }}
264 |         <p></p>
265 |         {{ plotlyOutput("detPlot") }}
266 |       </div>
267 |     </section>
268 | 
269 |     <section>
270 |       <br><br>
271 |       <header>
272 |         <h1 id="growth-rates">{{ textOutput(outputId = "growthRates") }}</h1>
273 |       </header>
274 |       <br>
275 | 
276 |       <div class="left-panel" style="width:700px!important;">
277 |         <h2>{{ textOutput(outputId = "locationH2") }}</h2>
278 |         <div class="form-group shiny-input-container">
279 |           <label class="control-label" for="countryGrowthRate">{{ textOutput(outputId = "selectCountryRegion2") }}</label>
280 |           {{ selectizeInput(inputId  = "countryGrowthRate",
281 |                             label    = "",
282 |                             choices  = ddReg,
283 |                             multiple = TRUE) }}
284 |           <script type="application/json" data-for="countryGrowthRate">{}</script>
285 |         </div>
286 |         <br>
287 |         {{ tableOutput(outputId = "daysSinceLast") }}
288 |         <p></p>
289 |         <div class="center">
290 |           {{ textOutput(outputId = "skipTo2") }} <a class="button-small" onclick="jQuery('html,body').animate({scrollTop: jQuery('#10-day-forecast').offset().top}, 1000);">10 day forecasts</a>
291 |                                                  <a class="button-small" onclick="jQuery('html,body').animate({scrollTop: jQuery('#growth-rates').offset().top},    1000);">Growth rates</a>
292 |                                                  <a class="button-small" onclick="jQuery('html,body').animate({scrollTop: jQuery('#about').offset().top},           1000);">About</a>
293 |         </div>
294 | 
295 |       </div>
296 | 
297 |       <div class="right-panel">
298 |         <h3>{{ textOutput(outputId = "growthTrajectories") }}</h3>
299 |         <p>{{ textOutput(outputId = "thisIsTheGrowth") }}</p>
300 |         <p>{{ textOutput(outputId = "thisPlotDoesNot") }}</p>
301 |         {{ plotlyOutput("log100casesPlot") }}
302 |         <br><br><br><br><br><br><br><br><br>
303 |         <h3>{{ textOutput(outputId = "growthRates2") }}</h3>
304 |         <p>{{ textOutput(outputId = "thisIsTheDaily") }}</p>
305 |         <p>{{ textOutput(outputId = "positiveIsBad") }}</p>
306 | 
307 |         {{ plotlyOutput("growthRate") }}
308 | 
309 |         <h3>{{ textOutput(outputId = "curveFlatteningIndex") }}</h3>
310 |         <p>{{ textOutput(outputId = "thisIsAMeasure") }}</p>
311 |         <p>{{ textOutput(outputId = "noteThisLast") }}</p>
312 |         {{ plotlyOutput("cfi") }}
313 |         <p>For more information, see <a class="button-small" href="#about">About</a></p>
314 |       </div>
315 |     </section>
316 | 
317 |     <section>
318 |       <header>
319 |         <h1 id="about">{{ textOutput(outputId = "about") }}</h1>
320 |       </header>
321 |       <br>
322 |       {{ includeMarkdown("doc/about.Rmd") }}
323 |       <div class="center">
324 |         {{ textOutput(outputId = "skipTo3") }} <a class="button-small" onclick="jQuery('html,body').animate({scrollTop: jQuery('#10-day-forecast').offset().top}, 1000);">10 day forecasts</a>
325 |                                                <a class="button-small" onclick="jQuery('html,body').animate({scrollTop: jQuery('#growth-rates').offset().top},    1000);">Growth rates</a>
326 |                                                <a class="button-small" onclick="jQuery('html,body').animate({scrollTop: jQuery('#about').offset().top},           1000);">About</a>
327 |       </div>
328 |     </section>
329 | </div>
330 | 
331 | <style>
332 | 
333 | section {
334 |   width: 95%;
335 |   margin: auto;
336 | }
337 | 
338 | .left-panel {
339 |   background: #F0F0F0;
340 | }
341 | 
342 | .right-panel {
343 |   width: 100%;
344 | }
345 | 
346 | @media(min-width: 10px) {
347 |   .left-panel {
348 |     max-width: 100%!important;
349 |   }
350 |   .right-panel {
351 |     max-width: 100%!important;
352 |   }
353 | }
354 | @media(min-width: 768px) {
355 |   .left-panel {
356 |     max-width: 49%!important;
357 |     float: left;
358 |     padding: 20px;
359 |   }
360 |   .right-panel {
361 |     max-width: 49%!important;
362 |     float: right;
363 |   }
364 | }
365 | 
366 | 
367 | div.shiny-input-container {width:100%!important;}
368 | 
369 | .tablinks.active {
370 |   border-bottom-color: rgb(173, 205, 240)!important;
371 |   border-bottom-width: 6px;
372 |   border-bottom-style: solid;
373 | }
374 | 
375 | @media(min-width: 10px) {
376 |   #div_global_or_country {width:95%!important;}
377 |   a.tablinks
378 |     {padding-left:2px!important;
379 |      padding-right:2px!important;}
380 | }
381 | @media(min-width: 768px) {
382 |   #div_global_or_country {width:50%!important;}
383 |   a.tablinks
384 |     {padding-left:30px!important;
385 |      padding-right:30px!important;}
386 | }
387 | 
388 | </style>
389 | 
390 | <footer id="page-footer" class="page-footer gen-2-ported" style="padding-bottom:0px;">
391 |   <div class="page-footer__top">
392 |     <div class="page-footer__inner max">
393 |       <div class="page-footer__header">
394 |         <div class="page-footer__header-logo">
395 |           <div class="logo">
396 |             <a href="https://www.unimelb.edu.au" class="logo__link"><img alt="The University of Melbourne homepage" src="https://d2h9b02ioca40d.cloudfront.net/v8.0.1/assets/logo-105a9.svg" class="logo__image logo__image--lg logo--no-padding-lg"></a>
397 |           </div>
398 |         </div>
399 |       </div>
400 |       <div class="page-footer__main grid">
401 |         <div class="page-footer__main__users cell cell--wide-1of4">
402 |           <ul class="page-footer__users list-reset list-chevrons links-reset">
403 |             <li>
404 |               <a href="https://students.unimelb.edu.au/">Current students</a>
405 |             </li>
406 |             <li><a href="https://staff.unimelb.edu.au/">Staff</a></li>
407 |             <li><a href="https://www.alumni.unimelb.edu.au/">Alumni</a></li>
408 |           </ul>
409 |         </div>
410 |         <div class="page-footer__main__popular cell cell--wide-1of2">
411 |           <ul class="page-footer__popular list-reset links-reset">
412 |             <li>
413 |               <a href="https://about.unimelb.edu.au/strategy/our-structure/faculties-and-graduate-schools" class="link-icon"><svg aria-label="faculty" focusable="false" role="img" class="link-icon__icon svg">
414 |                   <use xlink:href="#icon-faculty">
415 |                     <svg id="icon-faculty" viewBox="0 0 48 48"><path d="M24 14V6H4v36h40V14H24zM12 38H8v-4h4v4zm0-8H8v-4h4v4zm0-8H8v-4h4v4zm0-8H8v-4h4v4zm8 24h-4v-4h4v4zm0-8h-4v-4h4v4zm0-8h-4v-4h4v4zm0-8h-4v-4h4v4zm20 24H24v-4h4v-4h-4v-4h4v-4h-4v-4h16v20zm-4-16h-4v4h4v-4zm0 8h-4v4h4v-4z"></path></svg>
416 |                   </use>
417 |                   <title>Faculty</title>
418 |                 </svg>
419 |                 <span class="link-icon__text">Faculties &amp; graduate schools</span></a>
420 |             </li>
421 |             <li>
422 |               <a href="https://maps.unimelb.edu.au/" class="link-icon"><svg aria-label="location" focusable="false" role="img" class="link-icon__icon svg">
423 |                   <use xlink:href="#icon-location">
424 |                     <svg id="icon-location" viewBox="0 0 48 48"><path d="M24 4c-7.73 0-14 6.27-14 14 0 10.5 14 26 14 26s14-15.5 14-26c0-7.73-6.27-14-14-14zm0 19c-2.76 0-5-2.24-5-5s2.24-5 5-5 5 2.24 5 5-2.24 5-5 5z"></path></svg>
425 |                   </use>
426 |                   <title>Location</title>
427 |                 </svg>
428 |                 <span class="link-icon__text">Maps</span></a>
429 |             </li>
430 |             <li>
431 |               <a href="https://library.unimelb.edu.au/" class="link-icon"><svg aria-label="library" focusable="false" role="img" class="link-icon__icon svg">
432 |                   <use xlink:href="#icon-library">
433 |                     <svg id="icon-library" viewBox="0 0 48 48"><path d="M36 4H12C9.79 4 8 5.79 8 8v32c0 2.21 1.79 4 4 4h24c2.21 0 4-1.79 4-4V8c0-2.21-1.79-4-4-4zM12 8h10v16l-5-3-5 3V8z"></path></svg>
434 |                   </use>
435 |                   <title>Library</title>
436 |                 </svg>
437 |                 <span class="link-icon__text">Library</span></a>
438 |             </li>
439 |             <li>
440 |               <a href="https://www.alumni.unimelb.edu.au/give" class="link-icon"><svg aria-label="campaign" focusable="false" role="img" class="link-icon__icon svg">
441 |                   <use xlink:href="#icon-campaign">
442 |                     <svg id="icon-campaign" viewBox="0 0 48 48"><path d="M9 21v14h6V21H9zm12 0v14h6V21h-6zM5 45h38v-6H5v6zm28-24v14h6V21h-6zM24 3L5 13v4h38v-4L24 3z"></path></svg>
443 |                   </use>
444 |                   <title>Campaign</title>
445 |                 </svg>
446 |                 <span class="link-icon__text">Support the campaign</span></a>
447 |             </li>
448 |             <li>
449 |               <a href="https://www.unimelb.edu.au/contact/" class="link-icon"><svg aria-label="phone" focusable="false" role="img" class="link-icon__icon svg">
450 |                   <use xlink:href="#icon-phone">
451 |                     <svg id="icon-phone" viewBox="0 0 48 48"><path d="M13.25 21.59c2.88 5.66 7.51 10.29 13.18 13.17l4.4-4.41c.55-.55 1.34-.71 2.03-.49C35.1 30.6 37.51 31 40 31c1.11 0 2 .89 2 2v7c0 1.11-.89 2-2 2C21.22 42 6 26.78 6 8c0-1.11.9-2 2-2h7c1.11 0 2 .89 2 2 0 2.49.4 4.9 1.14 7.14.22.69.06 1.48-.49 2.03l-4.4 4.42z"></path></svg>
452 |                   </use>
453 |                   <title>Phone</title>
454 |                 </svg>
455 |                 <span class="link-icon__text">Contact us</span></a>
456 |             </li>
457 |             <li>
458 |               <a href="https://about.unimelb.edu.au/careers" class="link-icon"><svg aria-label="jobs" focusable="false" role="img" class="link-icon__icon svg">
459 |                   <use xlink:href="#icon-jobs">
460 |                     <svg id="icon-jobs" viewBox="0 0 48 48"><path d="M38.4 14.2h-7.2v-3.6c0-1.998-1.602-3.6-3.6-3.6h-7.2c-1.998 0-3.6 1.602-3.6 3.6v3.6H9.6c-1.998 0-3.582 1.602-3.582 3.6L6 37.6c0 1.998 1.602 3.6 3.6 3.6h28.8c1.998 0 3.6-1.602 3.6-3.6V17.8c0-1.998-1.602-3.6-3.6-3.6zm-10.8 0h-7.2v-3.6h7.2v3.6z"></path></svg>
461 |                   </use>
462 |                   <title>Jobs</title>
463 |                 </svg>
464 |                 <span class="link-icon__text">Jobs</span></a>
465 |             </li>
466 |           </ul>
467 |         </div>
468 |         <div class="page-footer__main__contacts cell cell--wide-1of4">
469 |           <p class="screenreaders-only">Follow us on:</p>
470 |           <ul class="social-list list-reset links-reset page-footer__social">
471 |             <li>
472 |               <a href="https://www.facebook.com/unimelb" class="social-list__link"><svg width="22" height="22" aria-label="Facebook" focusable="false" role="img" class="social-list__icon">
473 |                   <use xlink:href="#icon-facebook">
474 |                     <svg id="icon-facebook" viewBox="0 0 48 48"><path d="M41.792382 4H6.207618C4.98810765 4 4 4.98810764 4 6.207618v35.584764C4 43.0115926 4.98810764 44 6.207618 44h19.1575682V28.5099553H20.15248v-6.0368536h5.2127062V18.021147c0-5.1665455 3.1555599-7.9797973 7.7644308-7.9797973 2.207618 0 4.1051504.1644099 4.658029.2378473v5.3992971l-3.196475.0014987c-2.5066131 0-2.9918994 1.1910346-2.9918994 2.9388446v3.8542643h5.9779538l-.7784364 6.0368536h-5.1995174V44H41.792382C43.0115926 44 44 43.0115926 44 41.792382V6.207618C44 4.98810765 43.0115926 4 41.792382 4"></path></svg>
475 |                   </use>
476 |                   <title>Facebook</title>
477 |                 </svg>
478 |                 <span class="screenreaders-only">Facebook</span></a>
479 |             </li>
480 |             <li>
481 |               <a href="https://twitter.com/unimelb" class="social-list__link"><svg width="22" height="22" aria-label="Twitter" focusable="false" role="img" class="social-list__icon">
482 |                   <use xlink:href="#icon-twitter">
483 |                     <svg id="icon-twitter" viewBox="0 0 48 48"><path d="M39.9 16.1v1.1c0 10.8-8.3 23.3-23.3 23.3-4.6 0-9-1.3-12.6-3.7.7.1 1.3.1 2 .1 3.8 0 7.4-1.3 10.2-3.5-3.6-.1-6.6-2.4-7.7-5.7.5.1 1 .1 1.5.1.7 0 1.5-.1 2.2-.3-3.8-.8-6.6-4.1-6.6-8v-.1c1.1.6 2.4 1 3.7 1-2.2-1.5-3.7-4-3.7-6.8 0-1.5.4-2.9 1.1-4.1 4 5 10.1 8.2 16.9 8.6-.1-.6-.2-1.2-.2-1.9 0-4.5 3.7-8.2 8.2-8.2 2.4 0 4.5 1 6 2.6 1.9-.4 3.6-1 5.2-2-.6 1.9-1.9 3.5-3.6 4.5 1.7-.2 3.2-.6 4.7-1.3-1 1.6-2.4 3.1-4 4.3z"></path></svg>
484 |                   </use>
485 |                   <title>Twitter</title>
486 |                 </svg>
487 |                 <span class="screenreaders-only">Twitter</span></a>
488 |             </li>
489 |             <li>
490 |               <a href="https://www.linkedin.com/school/university-of-melbourne/" class="social-list__link"><svg width="22" height="22" aria-label="LinkedIn" focusable="false" role="img" class="social-list__icon">
491 |                   <use xlink:href="#icon-linkedin">
492 |                     <svg id="icon-linkedin" viewBox="0 0 48 48"><path d="M44 36.5c0 4.1-3.4 7.5-7.5 7.5h-25C7.4 44 4 40.6 4 36.5v-25C4 7.4 7.4 4 11.5 4h25c4.1 0 7.5 3.4 7.5 7.5v25zM13.2 10.7c-2.1 0-3.4 1.4-3.4 3.1 0 1.7 1.3 3.1 3.3 3.1 2.1 0 3.4-1.4 3.4-3.1.1-1.7-1.2-3.1-3.3-3.1zm3 26.8V19.4h-6v18.1h6zm21.6 0V27.1c0-5.5-3-8.1-6.9-8.1-3.2 0-4.7 1.8-5.4 3h.1v-2.6h-6s.1 1.7 0 18.1h6V27.4c0-.5 0-1.1.2-1.5.4-1.1 1.4-2.2 3.1-2.2 2.2 0 3 1.6 3 4.1v9.7h5.9z"></path></svg>
493 |                   </use>
494 |                   <title>Linkedin</title>
495 |                 </svg>
496 |                 <span class="screenreaders-only">LinkedIn</span></a>
497 |             </li>
498 |           </ul>
499 |           <dl class="page-footer__contact list-def list-def--wrap">
500 |             <dt>Phone:</dt>
501 |             <dd>13 MELB
502 |               <small>
503 |                 (<a href="tel:136352" rel="nofollow">13 6352</a>)
504 |               </small>
505 |             </dd>
506 |             <dt>International:</dt>
507 |             <dd>
508 |               <a href="tel:+61390355511" rel="nofollow">+61 3 9035 5511</a>
509 |             </dd>
510 |             <dt>ABN:</dt>
511 |             <dd>84 002 705 224</dd>
512 |             <dt>CRICOS Provider Code:</dt>
513 |             <dd>
514 |               00116K
515 |               <small>
516 |                 (<a href="https://services.unimelb.edu.au/international/visas">visa information</a>)
517 |               </small>
518 |             </dd>
519 |           </dl>
520 |         </div>
521 |       </div>
522 |       <div class="page-footer__legal links-reset">
523 |         <a href="https://safety.unimelb.edu.au/emergency">Emergency information</a>
524 |         <span class="page-footer__legal__sep">|</span>
525 |         <a href="https://unimelb.edu.au/disclaimer">Disclaimer and copyright</a>
526 |         <span class="page-footer__legal__sep">|</span>
527 |         <a href="https://www.unimelb.edu.au/accessibility">Accessibility</a>
528 |         <span class="page-footer__legal__sep">|</span>
529 |         <a href="https://unimelb.edu.au/privacy">Privacy</a>
530 |       </div>
531 |     </div>
532 |   </div>
533 |   <div class="page-footer__bottom">
534 |     <div class="page-footer__inner max">
535 |       <div class="grid">
536 |         <div class="cell cell--wide-3of4">
537 |           <div class="toggleblock--mobile toggleblock">
538 |             <div id="ui-toggle-footer-sitemap" aria-controls="ui-toggle-footer-sitemap-panel" tabindex="0" class="toggleblock__default">
539 |               <h2>Site Map</h2>
540 |             </div>
541 |             <div id="ui-toggle-footer-sitemap-panel" aria-labelledby="ui-toggle-footer-sitemap" tabindex="-1" aria-hidden="true" role="region" class="toggleblock__hidden">
542 |               <div class="grid">
543 |                 <div class="cell cell--tab-1of2 cell--desk-1of3">
544 |                   <a href="https://study.unimelb.edu.au/" class="page-footer__sitemap-heading" tabindex="-1">Study at Melbourne</a>
545 |                   <ul class="page-footer__sitemap-pages page-footer__sitemap-pages--minor list-reset list-chevrons">
546 |                     <li>
547 |                       <a href="https://study.unimelb.edu.au/find" class="link-reset" tabindex="-1">Find a course</a>
548 |                     </li>
549 |                     <li>
550 |                       <a href="https://study.unimelb.edu.au/how-to-apply" class="link-reset" tabindex="-1">Admissions, fees &amp; applications</a>
551 |                     </li>
552 |                     <li>
553 |                       <a href="https://study.unimelb.edu.au/your-experience/getting-ready-for-study/information-for-international-students" class="link-reset" tabindex="-1">International students</a>
554 |                     </li>
555 |                     <li>
556 |                       <a href="https://www.unimelb.edu.au/campustour" class="link-reset" tabindex="-1">Campus tour</a>
557 |                     </li>
558 |                     <li>
559 |                       <a href="https://study.unimelb.edu.au/connect-with-us/" class="link-reset" tabindex="-1">Connect with us</a>
560 |                     </li>
561 |                     <li>
562 |                       <a href="https://study.unimelb.edu.au/your-experience/accommodation/" class="link-reset" tabindex="-1">Accommodation</a>
563 |                     </li>
564 |                   </ul>
565 |                 </div>
566 |                 <div class="cell cell--tab-1of2 cell--desk-1of3">
567 |                   <a href="https://about.unimelb.edu.au/" class="page-footer__sitemap-heading" tabindex="-1">About us</a>
568 |                   <ul class="page-footer__sitemap-pages page-footer__sitemap-pages--minor list-reset list-chevrons">
569 |                     <li>
570 |                       <a href="https://about.unimelb.edu.au/strategy" class="link-reset" tabindex="-1">Strategy and governance</a>
571 |                     </li>
572 |                     <li>
573 |                       <a href="https://about.unimelb.edu.au/facts-and-figures" class="link-reset" tabindex="-1">Tradition of excellence</a>
574 |                     </li>
575 |                     <li>
576 |                       <a href="https://about.unimelb.edu.au/priorities-and-partnerships/international" class="link-reset" tabindex="-1">International connections</a>
577 |                     </li>
578 |                     <li>
579 |                       <a href="https://maps.unimelb.edu.au" class="link-reset" tabindex="-1">Campuses and facilities</a>
580 |                     </li>
581 |                     <li>
582 |                       <a href="https://about.unimelb.edu.au/strategy/our-structure" class="link-reset" tabindex="-1">Structure and governance</a>
583 |                     </li>
584 |                     <li>
585 |                       <a href="https://about.unimelb.edu.au/news-resources/reports-and-publications" class="link-reset" tabindex="-1">Policy and publications</a>
586 |                     </li>
587 |                     <li>
588 |                       <a href="https://about.unimelb.edu.au/careers" class="link-reset" tabindex="-1">Careers at Melbourne</a>
589 |                     </li>
590 |                     <li>
591 |                       <a href="https://about.unimelb.edu.au/strategy/governance/regulatory-framework/supplying-to-the-university" class="link-reset" tabindex="-1">Supplying to the University</a>
592 |                     </li>
593 |                   </ul>
594 |                 </div>
595 |                 <div class="cell cell--tab-1of2 cell--desk-1of3 cell--tab-clear">
596 |                   <a href="https://www.unimelb.edu.au/contact" class="page-footer__sitemap-heading" tabindex="-1">Contact &amp; Maps</a>
597 |                   <ul class="page-footer__sitemap-pages page-footer__sitemap-pages--minor list-reset list-chevrons">
598 |                     <li>
599 |                       <a href="https://ask.unimelb.edu.au/app/ask" class="link-reset" tabindex="-1">Enquiries</a>
600 |                     </li>
601 |                     <li>
602 |                       <a data-notsecure="" href="http://newsroom.melbourne.edu/" class="link-reset" tabindex="-1">Media</a>
603 |                     </li>
604 |                     <li>
605 |                       <a href="https://findanexpert.unimelb.edu.au/" class="link-reset" tabindex="-1">Find an expert</a>
606 |                     </li>
607 |                     <li>
608 |                       <a href="https://maps.unimelb.edu.au/" class="link-reset" tabindex="-1">Campus maps</a>
609 |                     </li>
610 |                     <li>
611 |                       <a href="https://about.unimelb.edu.au/news-resources/campus-services-and-facilities/transport-and-parking" class="link-reset" tabindex="-1">Traffic, parking &amp; bicycles</a>
612 |                     </li>
613 |                     <li>
614 |                       <a data-notsecure="" href="http://directory.unimelb.edu.au/" class="link-reset" tabindex="-1">Find a staff member</a>
615 |                     </li>
616 |                   </ul>
617 |                 </div>
618 |                 <div class="cell cell--tab-1of2 cell--desk-1of3 cell--desk-clear cell--wide-clear">
619 |                   <a href="https://www.alumni.unimelb.edu.au/" class="page-footer__sitemap-heading" tabindex="-1">Alumni &amp; friends</a>
620 |                   <ul class="page-footer__sitemap-pages page-footer__sitemap-pages--minor list-reset list-chevrons">
621 |                     <li>
622 |                       <a href="https://www.alumni.unimelb.edu.au/benefits" class="link-reset" tabindex="-1">Benefits &amp; services</a>
623 |                     </li>
624 |                     <li>
625 |                       <a href="https://www.alumni.unimelb.edu.au/give/" class="link-reset" tabindex="-1">Give</a>
626 |                     </li>
627 |                     <li>
628 |                       <a href="https://www.alumni.unimelb.edu.au/volunteer" class="link-reset" tabindex="-1">Volunteer</a>
629 |                     </li>
630 |                     <li>
631 |                       <a href="https://mag.alumni.unimelb.edu.au/?sl=1" class="link-reset" tabindex="-1">3010: alumni magazine</a>
632 |                     </li>
633 |                   </ul>
634 |                 </div>
635 |                 <div class="cell cell--tab-1of2 cell--desk-1of3 cell--tab-clear">
636 |                   <a href="https://research.unimelb.edu.au/" class="page-footer__sitemap-heading" tabindex="-1">Research</a>
637 |                   <ul class="page-footer__sitemap-pages page-footer__sitemap-pages--minor list-reset list-chevrons">
638 |                     <li>
639 |                       <a href="https://research.unimelb.edu.au/melbourne-research-institutes" class="link-reset" tabindex="-1">Research institutes</a>
640 |                     </li>
641 |                     <li>
642 |                       <a href="https://findanexpert.unimelb.edu.au/" class="link-reset" tabindex="-1">Find an expert or supervisor</a>
643 |                     </li>
644 |                     <li>
645 |                       <a href="https://gradresearch.unimelb.edu.au/" class="link-reset" tabindex="-1">Graduate researchers</a>
646 |                     </li>
647 |                     <li>
648 |                       <a href="https://pursuit.unimelb.edu.au/" class="link-reset" tabindex="-1">Pursuit: our research showcase</a>
649 |                     </li>
650 |                   </ul>
651 |                 </div>
652 |                 <div class="cell cell--tab-1of2 cell--desk-1of3">
653 |                   <a href="https://engagement.unimelb.edu.au/" class="page-footer__sitemap-heading" tabindex="-1">Engagement</a>
654 |                   <ul class="page-footer__sitemap-pages page-footer__sitemap-pages--minor list-reset list-chevrons">
655 |                     <li>
656 |                       <a href="https://events.unimelb.edu.au/" class="link-reset" tabindex="-1">Events</a>
657 |                     </li>
658 |                     <li>
659 |                       <a data-notsecure="" href="http://www.sport.unimelb.edu.au/fitnesscentres" class="link-reset" tabindex="-1">Sports facilities</a>
660 |                     </li>
661 |                     <li>
662 |                       <a href="https://shop.unimelb.edu.au/" class="link-reset" tabindex="-1">Shop</a>
663 |                     </li>
664 |                   </ul>
665 |                 </div>
666 |               </div>
667 |               <a class="toggleblock__footer">Close panel</a>
668 |             </div>
669 |           </div>
670 |         </div>
671 |         <div class="page-footer__main__ack cell cell--wide-1of4">
672 |           <a href="https://unimelb.edu.au/reconciliation" class="page-footer__ack link-reset">We acknowledge and pay respect to the Traditional Owners of the
673 |             lands upon which our campuses are situated.</a>
674 |         </div>
675 |       </div>
676 |     </div>
677 |   </div>
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