├── .DS_Store ├── LICENSE ├── README.md ├── Rmd ├── resources │ ├── bmj.csl │ └── library.bib ├── social_distancing_wuhan_report.Rmd └── social_distancing_wuhan_report.html ├── codes ├── .DS_Store ├── 0_master.r ├── 1_loadData.r ├── 2_simOutbreak_ncov_SEIR.r ├── 2_simOutbreak_ncov_SEIcIscR.r ├── function_modelSEIcIscR.r └── function_postprocessing.r ├── data ├── .DS_Store ├── china_pop_age_structure.csv ├── contacts.Rdata ├── contacts_china.rdata ├── out_R0.csv ├── out_date.csv ├── wuhan_pop_case_dist.csv └── wuhanpop.csv ├── outputs ├── .DS_Store ├── SEIR │ └── .DS_Store └── SEIcIscR │ └── .DS_Store └── plots ├── fig_1.png ├── fig_3.png └── fig_4.png /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kieshaprem/covid19-agestructureSEIR-wuhan-social-distancing/092dded2cc45967f665a3f0d3f3e41f6e5a3f6b6/.DS_Store -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # The effect of control strategies that reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study" 2 | 3 | *Contributors: Kiesha Prem †, Yang Liu †, Timothy W Russell, Adam J Kucharski, Rosalind M Eggo, Nicholas Davies, Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group, Mark Jit, Petra Klepac* 4 | 5 | *Correspondence to: kiesha.prem@lshtm.ac.uk* 6 | 7 | *† authors contributed equally* 8 | 9 | *The full paper accompanying this report can be found [here:](https://www.thelancet.com/journals/lanpub/article/PIIS2468-2667(20)30073-6/fulltext#sec1)* 10 | 11 | 12 | ## Aim 13 | 14 | To assess the impact of a range of control measures that reduce social mixing on data from the COVID-19 outbreak in Wuhan, China. Using the best estimates for the epidemiological parameters, we simulate the effects of the control measures using an age-structured, location-specific transmission model. The model uses data on contact patterns. Specifically, we adjust these patterns according to how they change under certain control measures. We then simulate our transmission model under these different scenarios and assess the impact. 15 | 16 | ## Introduction 17 | 18 | The SARS-CoV-2, a novel coronavirus, emerged in the City of Wuhan, Hubei Province, China, in early December 2019.(1,2) Since then, the local and national governments have taken unprecedented measures in response to the COVID-19 outbreak caused by SARS-CoV-2.(3) Exit screening of passengers was shortly followed by travel restrictions in Wuhan on 23rd January 2020, halting all means of unauthorised travel into and out of the city. Similar control measures were extended to the entire province of Hubei by 26th January 2020.(3) Non-pharmaceutical social distancing interventions such as extended school closure and workplace distancing were introduced to reduce the impact of the COVID-19 outbreak in Wuhan.(4) Within the city, schools remained closed; Lunar New Year holidays were extended so that people stayed away from their workplaces; the local government promoted social distancing and encouraged residents to avoid crowded places. These measures are known to have greatly changed the age-specific mixing patterns within the population in previous outbreak response efforts for other respiratory infectious diseases.(5,6) While travel restrictions undoubtedly had a role in reducing the exportations of infections outside of Wuhan, and delayed the onset of outbreaks in other regions,(7,8) changes in the mixing patterns affected the trajectory of the outbreak within Wuhan itself. In order to estimate the effects of social distancing measures on the progression of the COVID-19 epidemic, we look at Wuhan, hoping to provide some insights for the rest of the world. 19 | 20 | To examine how these changes in population mixing have affected the outbreak progression in Wuhan, we used synthetic location-specific contact patterns in Wuhan and adapted it in the presence of school closures, extended workplace closures, reduction in mixing in the general community. Using these matrices and the latest estimates of the epidemiological parameters of the Wuhan outbreak,(1,9,14–16) we simulated the ongoing trajectory of an outbreak in Wuhan using an age-structured susceptible-exposed-infected-removed (SEIR) model(17,18) for several social distancing measures. 21 | 22 | 23 | ## Discussion 24 | This study describes a mathematical model that quantifies the potential impacts of social distancing policies, relying on Wuhan as a case study. Epidemiological investigations conducted during the WHO-China Joint Mission on COVID-19 found many infections clustered around the households.(35) Extreme social distancing measures, including school closures, workplace closures, and avoiding any public gatherings all at once, can push the transmission to households leading to increased clustering in household cases.(5) As households are not explicitly included in the model, we do not consider heterogeneity and clustering of household transmission. Distinguishing between repeated and new contacts is important for disease propagation in contact network models,(36,37) more sophisticated methods accounting for temporal presence within the household(38) would be needed to characterise higher degrees of contact. Our compartmental model does not capture individual-level heterogeneity in contacts, which could be important in super-spreading events, particularly early on in an epidemic. In combination with nosocomial infections, the risk of COVID-19 infections is potentially amplified in close contacts of confirmed cases and healthcare workers. However, the compartmental model we presented is not equipped to explicitly consider transmission within health care institutions and households. More complex models, such as individual-based models with familial and healthcare structures, should be explored. Further research on the nosocomial infection risks among healthcare workers and patients have been identified as one of the research gaps to be prioritised in the next few months by the WHO. 25 | 26 | While the precise estimates of the impact of interventions may vary by country and indeed different estimates of key parameters, our model highlights the usefulness of social distancing interventions as well as the need to carefully calibrate their lifting to avoid second and subsequent waves of a COVID-19 epidemic. Non-Hubei China, and other east or southeast Asian regions have managed to avert a major outbreak locally and delayed the peak without resorting to Hubei’s extreme measures.(39) Policymakers are advised to reapportion their resources to focus on mitigating the impact of their potentially soon-do-be overwhelmed health system.(40) 27 | -------------------------------------------------------------------------------- /Rmd/resources/bmj.csl: -------------------------------------------------------------------------------- 1 | 2 | 182 | -------------------------------------------------------------------------------- /Rmd/resources/library.bib: -------------------------------------------------------------------------------- 1 | @article{kucharski2020early, 2 | title={Early dynamics of transmission and control of COVID-19: a mathematical modelling study}, 3 | author={Kucharski, Adam J and Russell, Timothy W and Diamond, Charlie and Liu, Yang and Edmunds, John and Funk, Sebastian and Eggo, Rosalind M and CMMID nCoV working group and others}, 4 | journal={medRxiv}, 5 | year={2020}, 6 | publisher={Cold Spring Harbor Laboratory Press} 7 | } 8 | 9 | @article{kucharski2014case, 10 | title={Case fatality rate for Ebola virus disease in west Africa}, 11 | author={Kucharski, Adam J and Edmunds, W John}, 12 | journal={The Lancet}, 13 | volume={384}, 14 | number={9950}, 15 | pages={1260}, 16 | year={2014}, 17 | publisher={Elsevier} 18 | } 19 | 20 | 21 | @article{linton2020incubation, 22 | title={Incubation Period and Other Epidemiological Characteristics of 2019 Novel Coronavirus Infections with Right Truncation: A Statistical Analysis of Publicly Available Case Data}, 23 | author={Linton, Natalie M and Kobayashi, Tetsuro and Yang, Yichi and Hayashi, Katsuma and Akhmetzhanov, Andrei R and Jung, Sung-mok and Yuan, Baoyin and Kinoshita, Ryo and Nishiura, Hiroshi}, 24 | journal={Journal of Clinical Medicine}, 25 | volume={9}, 26 | number={2}, 27 | pages={538}, 28 | year={2020}, 29 | publisher={Multidisciplinary Digital Publishing Institute} 30 | } 31 | 32 | 33 | @article{nishiura2009early, 34 | title={Early epidemiological assessment of the virulence of emerging infectious diseases: a case study of an influenza pandemic}, 35 | author={Nishiura, Hiroshi and Klinkenberg, Don and Roberts, Mick and Heesterbeek, Johan AP}, 36 | journal={PLoS One}, 37 | volume={4}, 38 | number={8}, 39 | year={2009}, 40 | publisher={Public Library of Science} 41 | } 42 | -------------------------------------------------------------------------------- /Rmd/social_distancing_wuhan_report.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "The effect of control strategies that reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study" 3 | output: 4 | html_document: 5 | html_fragment 6 | bibliography: resources/library.bib 7 | csl: resources/bmj.csl 8 | --- 9 | 10 | ```{r setup, include=FALSE} 11 | knitr::opts_chunk$set(echo = FALSE) 12 | ``` 13 | 14 | ```{r settings} 15 | ## Report date 16 | target_date <- Sys.Date() - 1 17 | ``` 18 | 19 | *Contributors: Kiesha Prem †, Yang Liu †, Timothy W Russell, Adam J Kucharski, Rosalind M Eggo, Nicholas Davies, Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group, Mark Jit, Petra Klepac* 20 | 21 | *Correspondence to: kiesha.prem@lshtm.ac.uk* 22 | 23 | *† authors contributed equally* 24 | 25 | *The full paper accompanying this report can be found as a preprint [here:](https://www.medrxiv.org/content/10.1101/2020.03.09.20033050v1)* 26 | 27 | 28 | ## Aim 29 | 30 | To assess the impact of a range of control measures that reduce social mixing on data from the COVID-19 outbreak in Wuhan, China. Using the best estimates for the epidemiological parameters, we simulate the effects of the control measures using an age-structured, location-specific transmission model. The model uses data on contact patterns. Specifically, we adjust these patterns according to how they change under certain control measures. We then simulate our transmission model under these different scenarios and assess the impact. 31 | 32 | ## Introduction 33 | 34 | The SARS-CoV-2, a novel coronavirus, emerged in the City of Wuhan, Hubei Province, China, in early December 2019.(1,2) Since then, the local and national governments have taken unprecedented measures in response to the COVID-19 outbreak caused by SARS-CoV-2.(3) Exit screening of passengers was shortly followed by travel restrictions in Wuhan on 23rd January 2020, halting all means of unauthorised travel into and out of the city. Similar control measures were extended to the entire province of Hubei by 26th January 2020.(3) Non-pharmaceutical social distancing interventions such as extended school closure and workplace distancing were introduced to reduce the impact of the COVID-19 outbreak in Wuhan.(4) Within the city, schools remained closed; Lunar New Year holidays were extended so that people stayed away from their workplaces; the local government promoted social distancing and encouraged residents to avoid crowded places. These measures are known to have greatly changed the age-specific mixing patterns within the population in previous outbreak response efforts for other respiratory infectious diseases.(5,6) While travel restrictions undoubtedly had a role in reducing the exportations of infections outside of Wuhan, and delayed the onset of outbreaks in other regions,(7,8) changes in the mixing patterns affected the trajectory of the outbreak within Wuhan itself. In order to estimate the effects of social distancing measures on the progression of the COVID-19 epidemic, we look at Wuhan, hoping to provide some insights for the rest of the world. 35 | 36 | To examine how these changes in population mixing have affected the outbreak progression in Wuhan, we used synthetic location-specific contact patterns in Wuhan and adapted it in the presence of school closures, extended workplace closures, reduction in mixing in the general community. Using these matrices and the latest estimates of the epidemiological parameters of the Wuhan outbreak,(1,9,14–16) we simulated the ongoing trajectory of an outbreak in Wuhan using an age-structured susceptible-exposed-infected-removed (SEIR) model(17,18) for several social distancing measures. 37 | 38 | ## Methods 39 | 40 | ### SEIR Model 41 | 42 | We simulated the outbreak in Wuhan using a deterministic stage-structured SEIR model over one year period, during which the modelled outbreak peters out. An implication of this approach is that all demographic changes in the population (i.e., births, deaths, and ageing) are ignored. 43 | 44 | For a given age group , epidemic transitions can be described by: 45 | 46 | $$ S_{i, t+1} = S_{i, t} - \beta S_{i, t} \sum_{j = 1}^n C_{i, j} I_{j, t}^c - \alpha \beta S_{i, t} \sum_{j = 1}^n C_{i, j} I_{j, t}^{SC} $$ 47 | 48 | $$ E_{i, t+1} = \beta \sum_{j = 1}^n C_{i, j} I_{j, t}^C + \alpha \beta S_{i, t} \sum_{j = 1}^n C_{i, j} I_{j, t}^{SC} - (1 - \kappa) E_{i, t}$$ 49 | 50 | $$ I_{i, t+1}^C = \rho_i \kappa E_{i, t} + (1 - \gamma)I_{j, t}^C$$ 51 | 52 | $$ I_{j, t+1}^{SC} = (1 - \rho_i) \kappa E_{i, t} + (1 - \gamma)I_{j, t}^C$$ 53 | 54 | $$ R_{i, t+1} = R_{i, t} + \gamma I_{j, t + 1}^C + \gamma I_{j, t + 1}^{SC} $$ 55 | 56 | Where $\beta$ is the transmission rate (scaled to the right value of $R_0$), $C_{i,j}$ describe the contacts of age group $j$ made by age group $i$, $\kappa = 1 - \exp(\frac{1}{d_L})$ is the daily probability of exposed individual becoming infectious (with $d_t$ being the average incubation period), and $\gamma = 1 - \exp(\frac{1}{d_I})$ is the daily probability that an infected individual recovers when the average duration of infection is $D_I$. We further incorporated contributions of asymptomatic and subclinical cases, $1 - \rho$ denotes the probability of an infected cases being asymptomatic or subclinical. We assumed that younger individuals are more likely to be asymptomatic (or subclinical) and less infectious (proportion of infectiousness compared to $I^C, \alpha$ ). 57 | 58 | 59 | | Parameter | Values | References | 60 | |-----------|--------|------------| 61 | | Basic reproduction number, $R_o$ | 2·2 (1·6–3·0) 1 | Kucharski and colleagues 1, 4 | 62 | | Average incubation period, $d_L$ | 6·4 days | Backer and colleagues 1,5 | 63 | | Average duration of infection, $d_I$ | 3 or 7 days | Woelfel and colleagues22| 64 | | Initial number of infected, $I_O$ |200 or 2000 |Abbott and colleagues 16 | 65 | |Pr(infected case is clinical), $\rho_i$ |0 or 0·4, for | Bi and colleagues20 | 66 | | |0 or 0·8 for | Davies and colleagues21 | 67 | |Pr(infection acquired from subclinical), $\alpha$ | 0·25 | Liu and colleagues 19 | 68 | 69 | 70 | 71 | ```{r fig_1, fig.align='center', fig.cap="_**Figure 1. Age-structured SEIR model and details of the modelled social distancing interventions.** The age-specific mixing patterns of individuals in age group alter their likelihood of being exposed to the virus given a certain number of infectives in the population. Younger individuals are more likely to be asymptomatic and less infectious, i.e. subclinical (SC). When $\\rho_i = 0$ for all $i$, the model simplifies to a standard SEIR. The force of infection $\\phi_{i, t}$ is given by $1 - (\\beta \\sum_j C_{i,j} I_{j,t}^C + \\alpha \\beta \\sum_j C_{i,j} I_{j, t}^{SC})$_", out.width = '80%'} 72 | knitr::include_graphics("../plots/fig_1.png") 73 | ``` 74 | 75 | ## Results 76 | 77 | Our simulations show that the control measures aimed at reducing the social mixing in the population can be effective in reducing the magnitude and delaying the peak of the outbreak. Figure 3 shows the effects of different control measures among individuals aged 55–60 and 10–15 years old. The standard school winter break and holidays for the Lunar New Year would have had little effect on progression of the outbreak had the school and workplace re-opened as normal. 78 | 79 | ```{r fig_3, echo=FALSE, fig.align='center', fig.cap="_**Figure 3. Effects of different intervention strategies on the cumulative incidence and new cases per day among individuals aged 55–60 (A to D) and 10–15 (E to H) from late 2019 to end-2020.** Under two scenarios children being equally infectious and children being less infectious, the effects of social distancing measures were investigated. Theoretical no intervention (black line), school break and LNY (blue line) and intense control measures that are relaxed in a staggered fashion at the beginning of March (red line), and intense control measures that are relaxed in a staggered fashion at the beginning of April (orange line). Shading indicates the timing of the school holidays, Lunar New Year (dark vertical line), intense control measures (dark grey), and staggered return to work followed by school opening (lighter grey)._", out.width = '80%'} 80 | knitr::include_graphics("../plots/fig_3.png") 81 | ``` 82 | 83 | We presented the median cumulative incidence (Fig 4A), incident case per day (Fig 4B) and age-specific incidence per day (Fig 4C–G) of the 200 simulated outbreaks. The 25th and 75th percentile outbreaks are represented by the shaded area in the cumulative incidence. The intense control measures of prolonged school closure and work holidays reduced the final size (Fig 4A) and peak incidence, while also delaying the peak of the outbreak (Fig 4B). Our model suggests that the effects of these social distancing strategies vary across age categories, the reduction in incidence is highest among school children and older individuals and lowest among working-aged adults (Fig 4C–G and Fig 5). 84 | 85 | 86 | ```{r fig_4, echo=FALSE, fig.align='center', fig.cap="_**Figure 4. Effects of the different social distancing measures on the cumulative incidence (A) and new cases per day (B), and age-specific incidence per day (C to G) from late 2019 to end-2020.** The median cumulative incidence, incident cases per day and age-specific incidence per day are represented as solid lines. The 25th and 75th percentile outbreaks are represented by the shaded area in the cumulative incidence. Theoretical no intervention (black line), school break and Lunar New Year (blue line) and intense control measures that are relaxed in a staggered fashion at the beginning of March (red line), and intense control measured that are relaxed in a staggered fashion at the beginning of April (orange line). Shading indicates the timing of the school holidays, Lunar New Year weekend (dark vertical line), intense control measures (dark grey), and staggered return to work followed by school opening (lighter grey)._", out.width = '80%'} 87 | knitr::include_graphics("../plots/fig_4.png") 88 | ``` 89 | 90 | These measures were most effective if staggered return to work was at the beginning of April; this reduced the median number of infections by more than 92% (interquartile range: 66–97%) and 24% (interquartile range: 13–90%) in mid-2020 and end-2020, respectively, (Fig 5 and Fig S4) should the disease have a longer duration of infectiousness, and reduced the magnitude and delayed the peak incidence across all age categories (Fig 4C–E) which can have further beneficial impact by relieving the pressure on the healthcare system in the immediate few months. Uncertainty in $R_o$ values has a large impact on the timing of the peak and the final size of the outbreak (Fig 4A). 91 | 92 | ## Discussion 93 | 94 | This study describes a mathematical model that quantifies the potential impacts of social distancing policies, relying on Wuhan as a case study. Epidemiological investigations conducted during the WHO-China Joint Mission on COVID-19 found many infections clustered around the households.(35) Extreme social distancing measures, including school closures, workplace closures, and avoiding any public gatherings all at once, can push the transmission to households leading to increased clustering in household cases.(5) As households are not explicitly included in the model, we do not consider heterogeneity and clustering of household transmission. Distinguishing between repeated and new contacts is important for disease propagation in contact network models,(36,37) more sophisticated methods accounting for temporal presence within the household(38) would be needed to characterise higher degrees of contact. Our compartmental model does not capture individual-level heterogeneity in contacts, which could be important in super-spreading events, particularly early on in an epidemic. In combination with nosocomial infections, the risk of COVID-19 infections is potentially amplified in close contacts of confirmed cases and healthcare workers. However, the compartmental model we presented is not equipped to explicitly consider transmission within health care institutions and households. More complex models, such as individual-based models with familial and healthcare structures, should be explored. Further research on the nosocomial infection risks among healthcare workers and patients have been identified as one of the research gaps to be prioritised in the next few months by the WHO. 95 | 96 | While the precise estimates of the impact of interventions may vary by country and indeed different estimates of key parameters, our model highlights the usefulness of social distancing interventions as well as the need to carefully calibrate their lifting to avoid second and subsequent waves of a COVID-19 epidemic. Non-Hubei China, and other east or southeast Asian regions have managed to avert a major outbreak locally and delayed the peak without resorting to Hubei’s extreme measures.(39) Policymakers are advised to reapportion their resources to focus on mitigating the impact of their potentially soon-do-be overwhelmed health system.(40) 97 | 98 | 99 | ## References 100 | 101 | For cited articles and referenced figures (other than Figures 1, 3 and 4 which appear here, please see the preprint of the accepted paper [here:](https://www.medrxiv.org/content/10.1101/2020.03.09.20033050v1)) -------------------------------------------------------------------------------- /codes/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kieshaprem/covid19-agestructureSEIR-wuhan-social-distancing/092dded2cc45967f665a3f0d3f3e41f6e5a3f6b6/codes/.DS_Store -------------------------------------------------------------------------------- /codes/0_master.r: -------------------------------------------------------------------------------- 1 | # load relevant the data files 2 | source('codes/1_loadData.r') 3 | 4 | # source the age-structured SEIcIscR model functions 5 | source('codes/function_modelSEIcIscR.r') 6 | 7 | # source the age-structured SEIcIscR model functions 8 | source('codes/function_postprocessing.r') 9 | 10 | # simulate N oubtreaks 11 | source('codes/2_simOutbreak_ncov_SEIR.r') 12 | source('codes/2_simOutbreak_ncov_SEIcIscR.r') 13 | 14 | 15 | 16 | 17 | 18 | -------------------------------------------------------------------------------- /codes/1_loadData.r: -------------------------------------------------------------------------------- 1 | require(data.table) 2 | require(Matrix) 3 | require(matrixcalc) 4 | 5 | ## load data: Wuhan's population age structure and Contact matrices 6 | 7 | loadPopData = TRUE 8 | loadContactMatrices = TRUE 9 | loadCaseData =TRUE 10 | loadR0posterior =TRUE 11 | 12 | 13 | ## load data: Population age structure (currently using China's) and Contact matrices 14 | 15 | # 1) population data 16 | if(loadPopData) 17 | { 18 | wuhanpop = read.csv('data/wuhanpop.csv',as.is = TRUE) 19 | } 20 | 21 | # 2) (projected) contact matrices 22 | ### Acknowlegments: codes from Petra Klepac (petrakle) 23 | normalize.contact.matrices <- function(C, popv, make.sym = F){ 24 | # FUN normalize them so that 25 | # the matrix with all the contacts is normalized so that its dominant eigenvalue is 1 26 | # and other matrices keep their contributions 27 | if (make.sym){ 28 | Csym <- lapply(C, function(x, popv) (x + t(x)*((popv)%*%t(1/popv)))/2, popv) # make sure contacts are reciprocal 29 | } else { 30 | Csym <- C # if make.sym = F leave it as is 31 | } 32 | eig1 <- Re(eigen(Csym["all"]$all)$value[1]) # save dominant eigenvalue of the matrix with all contacts 33 | 34 | # divide all matrices by the real part of the dominant matrix of all of the contacts 35 | # that way all the rescaled matrices still sum to C_all = C_work + C_home + C_school + C_other 36 | Cnorm <- lapply(Csym, function(x,eig1) x/eig1, eig1) 37 | 38 | return(Cnorm) 39 | } 40 | 41 | # Synthetic contact matrices for China from Prem, Cook and Jit (2017) https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005697 42 | if(loadContactMatrices) 43 | { 44 | load(paste0('data/contacts_china.rdata')) 45 | contacts <- contacts_china # normalize.contact.matrices(contacts_china,wuhanpop$popage, make.sym=T) 46 | rm(contacts_china) 47 | } 48 | 49 | # case age distribution 50 | if(loadCaseData) 51 | { 52 | wuhancaseraw = read.csv('data/wuhan_pop_case_dist.csv',as.is = TRUE) 53 | caseage = rep(wuhancaseraw$wuhan,each=2)/2 54 | wuhancase = data.frame(agegroup = 1:16, caseage = c(caseage[1:15],sum(caseage[16:20]))) 55 | rm(wuhancaseraw,caseage) 56 | } 57 | 58 | if(loadR0posterior) 59 | { 60 | # --- read in R0 posterior 61 | R0_plot <-read.csv(paste0("data/out_R0.csv")) 62 | R0_dates <- read.csv(paste0('data/out_date.csv')) 63 | start_date <- as.Date(R0_dates[1,1]) # first case 64 | end_date <- as.Date(R0_dates[nrow(R0_dates),1]) # period to forecast ahead 65 | date_range <- seq.Date(start_date,end_date,1) 66 | 67 | # extract all estimates from 01.01.2020 - 23.01.2020 68 | R0_posterior <- R0_plot[which(date_range == as.Date("2020-01-01") ):which(date_range == as.Date("2020-01-23")),] 69 | range(R0_posterior) 70 | r0posterior = as.vector((unlist(R0_posterior))) 71 | par(mfrow=c(2,1)) 72 | R0_dense = (density((r0posterior))) 73 | plot(x = R0_dense$x,y=R0_dense$y,type='l',xlab='R0',ylab='Density',lwd=2) 74 | R0_dense = (density(log(r0posterior))) 75 | plot(x = R0_dense$x,y=R0_dense$y,type='l',xlab='ln(R0)',ylab='Density',lwd=2) 76 | 77 | 78 | rm(R0_dense,R0_plot,R0_posterior,date_range,end_date,start_date) 79 | 80 | } 81 | 82 | 83 | rm(loadContactMatrices,loadPopData,loadR0posterior,loadCaseData) 84 | -------------------------------------------------------------------------------- /codes/2_simOutbreak_ncov_SEIR.r: -------------------------------------------------------------------------------- 1 | ## To simulate n_simSEIR outbreaks 2 | 3 | nsim = 200 4 | 5 | set.seed(123) 6 | r0postCrI = r0posterior 7 | # hist(r0postCrI) 8 | # summary(r0postCrI) 9 | R0est = sample(x = r0postCrI,size = nsim) 10 | # print(R0est) 11 | 12 | ## To simulate n_simSEIR outbreaks: duration of infection = 3 days, initial infected n=~200 infected 13 | epi_doNothingDurInf3 = vector('list',nsim) 14 | epi_baseDurInf3 = vector('list',nsim) 15 | epi_marchDurInf3 = vector('list',nsim) 16 | epi_aprilDurInf3 = vector('list',nsim) 17 | start = Sys.time() 18 | durInfSim = 3 19 | initialI = 0.0002 20 | for(sim in 1:nsim) 21 | { 22 | epi_doNothingDurInf3[[sim]] = simulateOutbreakSEIR(R0t =R0est[sim] ,rho = rep(0.5,3660),dateStartSchoolClosure = as.Date('2019-11-01'), 23 | dateStartIntenseIntervention = as.Date('2019-11-01'), dateEndIntenseIntervention = as.Date('2019-11-01'), 24 | pWorkOpen = c(1,1,1,1),numWeekStagger = c(0,0,0),pInfected=initialI,durInf = durInfSim) 25 | epi_baseDurInf3[[sim]] = simulateOutbreakSEIR(R0t =R0est[sim] ,rho = rep(0.5,3660),dateEndIntenseIntervention = as.Date('2020-01-31'),pWorkOpen = c(0.1,0.75,1,1), 26 | numWeekStagger = c(10/7,10/7,10/7),pInfected=initialI,durInf = durInfSim) 27 | epi_marchDurInf3[[sim]] = simulateOutbreakSEIR(R0t =R0est[sim] ,rho = rep(0.5,3660),dateEndIntenseIntervention = as.Date('2020-03-01'), 28 | pInfected=initialI,durInf = durInfSim) 29 | epi_aprilDurInf3[[sim]] = simulateOutbreakSEIR(R0t =R0est[sim] ,rho = rep(0.5,3660),dateEndIntenseIntervention = as.Date('2020-04-01'), 30 | pInfected=initialI,durInf = durInfSim) 31 | if(sim%%10==0) print(paste0('Done with simulation ',sim)) 32 | } 33 | end = Sys.time() 34 | print(end-start) 35 | 36 | covid_SDurInf3 = list() 37 | covid_SDurInf3[[1]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_doNothingDurInf3) 38 | covid_SDurInf3[[2]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_baseDurInf3) 39 | covid_SDurInf3[[3]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_marchDurInf3) 40 | covid_SDurInf3[[4]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_aprilDurInf3) 41 | 42 | covid_IDurInf3 = list() 43 | covid_IDurInf3[[1]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_doNothingDurInf3) 44 | covid_IDurInf3[[2]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_baseDurInf3) 45 | covid_IDurInf3[[3]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_marchDurInf3) 46 | covid_IDurInf3[[4]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_aprilDurInf3) 47 | 48 | peaktime_DurInf3 = list() 49 | peaktime_DurInf3[[1]] = summarisePeakTimePeakSize(SIMS = epi_doNothingDurInf3) 50 | peaktime_DurInf3[[2]] = summarisePeakTimePeakSize(SIMS = epi_baseDurInf3) 51 | peaktime_DurInf3[[3]] = summarisePeakTimePeakSize(SIMS = epi_marchDurInf3) 52 | peaktime_DurInf3[[4]] = summarisePeakTimePeakSize(SIMS = epi_aprilDurInf3) 53 | 54 | covid_DurInf3 = list() 55 | covid_DurInf3[[1]] = summariseSimulations_mid(CI = 50,SIMS = epi_doNothingDurInf3) 56 | covid_DurInf3[[2]] = summariseSimulations_mid(CI = 50,SIMS = epi_baseDurInf3) 57 | covid_DurInf3[[3]] = summariseSimulations_mid(CI = 50,SIMS = epi_marchDurInf3) 58 | covid_DurInf3[[4]] = summariseSimulations_mid(CI = 50,SIMS = epi_aprilDurInf3) 59 | 60 | AGEcovid_IDurInf3 = list() 61 | AGEcovid_IDurInf3[[1]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_doNothingDurInf3) 62 | AGEcovid_IDurInf3[[2]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_baseDurInf3) 63 | AGEcovid_IDurInf3[[3]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_marchDurInf3) 64 | AGEcovid_IDurInf3[[4]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_aprilDurInf3) 65 | 66 | epiFirstSimDurInf3 = list(epi_doNothingDurInf3 = epi_doNothingDurInf3[[1]], 67 | epi_baseDurInf3= epi_baseDurInf3[[1]], 68 | epi_marchDurInf3 = epi_marchDurInf3[[1]], 69 | epi_aprilDurInf3 = epi_aprilDurInf3[[1]]) 70 | 71 | 72 | 73 | ## To simulate n_simSEIR outbreaks: duration of infection = 7 days, initial infected n=~200 infected 74 | 75 | epi_doNothingDurInf7 = vector('list',nsim) 76 | epi_baseDurInf7 = vector('list',nsim) 77 | epi_marchDurInf7 = vector('list',nsim) 78 | epi_aprilDurInf7 = vector('list',nsim) 79 | start = Sys.time() 80 | durInfSim = 7 81 | initialI = 0.0002 82 | for(sim in 1:nsim) 83 | { 84 | epi_doNothingDurInf7[[sim]] = simulateOutbreakSEIR(R0t =R0est[sim] ,rho = rep(0.5,3660),dateStartSchoolClosure = as.Date('2019-11-01'), 85 | dateStartIntenseIntervention = as.Date('2019-11-01'), dateEndIntenseIntervention = as.Date('2019-11-01'), 86 | pWorkOpen = c(1,1,1,1),numWeekStagger = c(0,0,0,0),pInfected=initialI,durInf = durInfSim) 87 | epi_baseDurInf7[[sim]] = simulateOutbreakSEIR(R0t =R0est[sim] ,rho = rep(0.5,3660),dateEndIntenseIntervention = as.Date('2020-01-31'),pWorkOpen = c(0.1,0.75,1,1), 88 | numWeekStagger = c(10/7,10/7,10/7),pInfected=initialI,durInf = durInfSim) 89 | epi_marchDurInf7[[sim]] = simulateOutbreakSEIR(R0t =R0est[sim] ,rho = rep(0.5,3660),dateEndIntenseIntervention = as.Date('2020-03-01'), 90 | pInfected=initialI,durInf = durInfSim) 91 | epi_aprilDurInf7[[sim]] = simulateOutbreakSEIR(R0t =R0est[sim] ,rho = rep(0.5,3660),dateEndIntenseIntervention = as.Date('2020-04-01'), 92 | pInfected=initialI,durInf = durInfSim) 93 | if(sim%%10==0) print(paste0('Done with simulation ',sim)) 94 | } 95 | end = Sys.time() 96 | print(end-start) 97 | 98 | covid_SDurInf7 = list() 99 | covid_SDurInf7[[1]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_doNothingDurInf7) 100 | covid_SDurInf7[[2]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_baseDurInf7) 101 | covid_SDurInf7[[3]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_marchDurInf7) 102 | covid_SDurInf7[[4]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_aprilDurInf7) 103 | 104 | covid_IDurInf7 = list() 105 | covid_IDurInf7[[1]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_doNothingDurInf7) 106 | covid_IDurInf7[[2]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_baseDurInf7) 107 | covid_IDurInf7[[3]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_marchDurInf7) 108 | covid_IDurInf7[[4]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_aprilDurInf7) 109 | 110 | peaktime_DurInf7 = list() 111 | peaktime_DurInf7[[1]] = summarisePeakTimePeakSize(SIMS = epi_doNothingDurInf7) 112 | peaktime_DurInf7[[2]] = summarisePeakTimePeakSize(SIMS = epi_baseDurInf7) 113 | peaktime_DurInf7[[3]] = summarisePeakTimePeakSize(SIMS = epi_marchDurInf7) 114 | peaktime_DurInf7[[4]] = summarisePeakTimePeakSize(SIMS = epi_aprilDurInf7) 115 | 116 | covid_DurInf7 = list() 117 | covid_DurInf7[[1]] = summariseSimulations_mid(CI = 50,SIMS = epi_doNothingDurInf7) 118 | covid_DurInf7[[2]] = summariseSimulations_mid(CI = 50,SIMS = epi_baseDurInf7) 119 | covid_DurInf7[[3]] = summariseSimulations_mid(CI = 50,SIMS = epi_marchDurInf7) 120 | covid_DurInf7[[4]] = summariseSimulations_mid(CI = 50,SIMS = epi_aprilDurInf7) 121 | 122 | AGEcovid_IDurInf7 = list() 123 | AGEcovid_IDurInf7[[1]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_doNothingDurInf7) 124 | AGEcovid_IDurInf7[[2]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_baseDurInf7) 125 | AGEcovid_IDurInf7[[3]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_marchDurInf7) 126 | AGEcovid_IDurInf7[[4]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_aprilDurInf7) 127 | 128 | epiFirstSimDurInf7 = list(epi_doNothingDurInf7 = epi_doNothingDurInf7[[1]], 129 | epi_baseDurInf7= epi_baseDurInf7[[1]], 130 | epi_marchDurInf7 = epi_marchDurInf7[[1]], 131 | epi_aprilDurInf7 = epi_aprilDurInf7[[1]]) 132 | 133 | save(covid_IDurInf3,file = 'outputs/SEIR/covid_IDurInf3.rdata') 134 | save(covid_SDurInf3,file = 'outputs/SEIR/covid_SDurInf3.rdata') 135 | save(covid_IDurInf7,file = 'outputs/SEIR/covid_IDurInf7.rdata') 136 | save(covid_SDurInf7,file = 'outputs/SEIR/covid_SDurInf7.rdata') 137 | 138 | save(peaktime_DurInf3,file = 'outputs/SEIR/peaktime_DurInf3.rdata') 139 | save(peaktime_DurInf7,file = 'outputs/SEIR/peaktime_DurInf7.rdata') 140 | 141 | save(covid_DurInf3,file ='outputs/SEIR/covid_DurInf3.rdata') 142 | save(covid_DurInf7,file ='outputs/SEIR/covid_DurInf7.rdata') 143 | 144 | save(AGEcovid_IDurInf3,file ='outputs/SEIR/AGEcovid_IDurInf3.rdata') 145 | save(AGEcovid_IDurInf7,file ='outputs/SEIR/AGEcovid_IDurInf7.rdata') 146 | 147 | save(epiFirstSimDurInf3,file ='outputs/SEIR/epiFirstSimDurInf3.rdata') 148 | save(epiFirstSimDurInf7,file ='outputs/SEIR/epiFirstSimDurInf7.rdata') 149 | 150 | rm(epi_doNothingDurInf3,epi_baseDurInf3,epi_marchDurInf3,epi_aprilDurInf3) 151 | rm(epi_doNothingDurInf7,epi_baseDurInf7,epi_marchDurInf7,epi_aprilDurInf7) 152 | 153 | 154 | ## To simulate n_simSEIR outbreaks: duration of infection = 3 days, initial infected n=~2000 infected 155 | epi_doNothingDurInf3I2000 = vector('list',nsim) 156 | epi_baseDurInf3I2000 = vector('list',nsim) 157 | epi_marchDurInf3I2000 = vector('list',nsim) 158 | epi_aprilDurInf3I2000 = vector('list',nsim) 159 | start = Sys.time() 160 | durInfSim = 3 161 | initialI = 0.002 162 | for(sim in 1:nsim) 163 | { 164 | epi_doNothingDurInf3I2000[[sim]] = simulateOutbreakSEIR(R0t =R0est[sim] ,rho = rep(0.5,3660),dateStartSchoolClosure = as.Date('2019-11-01'), 165 | dateStartIntenseIntervention = as.Date('2019-11-01'), dateEndIntenseIntervention = as.Date('2019-11-01'), 166 | pWorkOpen = c(1,1,1,1),numWeekStagger = c(0,0,0),pInfected=initialI,durInf = durInfSim) 167 | epi_baseDurInf3I2000[[sim]] = simulateOutbreakSEIR(R0t =R0est[sim] ,rho = rep(0.5,3660),dateEndIntenseIntervention = as.Date('2020-01-31'),pWorkOpen = c(0.1,0.75,1,1), 168 | numWeekStagger = c(10/7,10/7,10/7),pInfected=initialI,durInf = durInfSim) 169 | epi_marchDurInf3I2000[[sim]] = simulateOutbreakSEIR(R0t =R0est[sim] ,rho = rep(0.5,3660),dateEndIntenseIntervention = as.Date('2020-03-01'), 170 | pInfected=initialI,durInf = durInfSim) 171 | epi_aprilDurInf3I2000[[sim]] = simulateOutbreakSEIR(R0t =R0est[sim] ,rho = rep(0.5,3660),dateEndIntenseIntervention = as.Date('2020-04-01'), 172 | pInfected=initialI,durInf = durInfSim) 173 | if(sim%%10==0) print(paste0('Done with simulation ',sim)) 174 | } 175 | end = Sys.time() 176 | print(end-start) 177 | 178 | covid_SDurInf3I2000 = list() 179 | covid_SDurInf3I2000[[1]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_doNothingDurInf3I2000) 180 | covid_SDurInf3I2000[[2]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_baseDurInf3I2000) 181 | covid_SDurInf3I2000[[3]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_marchDurInf3I2000) 182 | covid_SDurInf3I2000[[4]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_aprilDurInf3I2000) 183 | 184 | covid_IDurInf3I2000 = list() 185 | covid_IDurInf3I2000[[1]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_doNothingDurInf3I2000) 186 | covid_IDurInf3I2000[[2]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_baseDurInf3I2000) 187 | covid_IDurInf3I2000[[3]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_marchDurInf3I2000) 188 | covid_IDurInf3I2000[[4]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_aprilDurInf3I2000) 189 | 190 | peaktime_DurInf3I2000 = list() 191 | peaktime_DurInf3I2000[[1]] = summarisePeakTimePeakSize(SIMS = epi_doNothingDurInf3I2000) 192 | peaktime_DurInf3I2000[[2]] = summarisePeakTimePeakSize(SIMS = epi_baseDurInf3I2000) 193 | peaktime_DurInf3I2000[[3]] = summarisePeakTimePeakSize(SIMS = epi_marchDurInf3I2000) 194 | peaktime_DurInf3I2000[[4]] = summarisePeakTimePeakSize(SIMS = epi_aprilDurInf3I2000) 195 | 196 | covid_DurInf3I2000 = list() 197 | covid_DurInf3I2000[[1]] = summariseSimulations_mid(CI = 50,SIMS = epi_doNothingDurInf3I2000) 198 | covid_DurInf3I2000[[2]] = summariseSimulations_mid(CI = 50,SIMS = epi_baseDurInf3I2000) 199 | covid_DurInf3I2000[[3]] = summariseSimulations_mid(CI = 50,SIMS = epi_marchDurInf3I2000) 200 | covid_DurInf3I2000[[4]] = summariseSimulations_mid(CI = 50,SIMS = epi_aprilDurInf3I2000) 201 | 202 | AGEcovid_IDurInf3I2000 = list() 203 | AGEcovid_IDurInf3I2000[[1]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_doNothingDurInf3I2000) 204 | AGEcovid_IDurInf3I2000[[2]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_baseDurInf3I2000) 205 | AGEcovid_IDurInf3I2000[[3]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_marchDurInf3I2000) 206 | AGEcovid_IDurInf3I2000[[4]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_aprilDurInf3I2000) 207 | 208 | epiFirstSimDurInf3I2000 = list(epi_doNothingDurInf3I2000 = epi_doNothingDurInf3I2000[[1]], 209 | epi_baseDurInf3I2000= epi_baseDurInf3I2000[[1]], 210 | epi_marchDurInf3I2000 = epi_marchDurInf3I2000[[1]], 211 | epi_aprilDurInf3I2000 = epi_aprilDurInf3I2000[[1]]) 212 | 213 | 214 | 215 | ## To simulate n_simSEIR outbreaks: duration of infection = 7 days, initial infected n=~2000 infected 216 | 217 | epi_doNothingDurInf7I2000 = vector('list',nsim) 218 | epi_baseDurInf7I2000 = vector('list',nsim) 219 | epi_marchDurInf7I2000 = vector('list',nsim) 220 | epi_aprilDurInf7I2000 = vector('list',nsim) 221 | start = Sys.time() 222 | durInfSim = 7 223 | initialI = 0.002 224 | for(sim in 1:nsim) 225 | { 226 | epi_doNothingDurInf7I2000[[sim]] = simulateOutbreakSEIR(R0t =R0est[sim] ,rho = rep(0.5,3660),dateStartSchoolClosure = as.Date('2019-11-01'), 227 | dateStartIntenseIntervention = as.Date('2019-11-01'), dateEndIntenseIntervention = as.Date('2019-11-01'), 228 | pWorkOpen = c(1,1,1,1),numWeekStagger = c(0,0,0),pInfected=initialI,durInf = durInfSim) 229 | epi_baseDurInf7I2000[[sim]] = simulateOutbreakSEIR(R0t =R0est[sim] ,rho = rep(0.5,3660),dateEndIntenseIntervention = as.Date('2020-01-31'),pWorkOpen = c(0.1,0.75,1,1), 230 | numWeekStagger = c(10/7,10/7,10/7),pInfected=initialI,durInf = durInfSim) 231 | epi_marchDurInf7I2000[[sim]] = simulateOutbreakSEIR(R0t =R0est[sim] ,rho = rep(0.5,3660),dateEndIntenseIntervention = as.Date('2020-03-01'), 232 | pInfected=initialI,durInf = durInfSim) 233 | epi_aprilDurInf7I2000[[sim]] = simulateOutbreakSEIR(R0t =R0est[sim] ,rho = rep(0.5,3660),dateEndIntenseIntervention = as.Date('2020-04-01'), 234 | pInfected=initialI,durInf = durInfSim) 235 | if(sim%%10==0) print(paste0('Done with simulation ',sim)) 236 | } 237 | end = Sys.time() 238 | print(end-start) 239 | 240 | covid_SDurInf7I2000 = list() 241 | covid_SDurInf7I2000[[1]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_doNothingDurInf7I2000) 242 | covid_SDurInf7I2000[[2]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_baseDurInf7I2000) 243 | covid_SDurInf7I2000[[3]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_marchDurInf7I2000) 244 | covid_SDurInf7I2000[[4]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_aprilDurInf7I2000) 245 | 246 | covid_IDurInf7I2000 = list() 247 | covid_IDurInf7I2000[[1]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_doNothingDurInf7I2000) 248 | covid_IDurInf7I2000[[2]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_baseDurInf7I2000) 249 | covid_IDurInf7I2000[[3]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_marchDurInf7I2000) 250 | covid_IDurInf7I2000[[4]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_aprilDurInf7I2000) 251 | 252 | peaktime_DurInf7I2000 = list() 253 | peaktime_DurInf7I2000[[1]] = summarisePeakTimePeakSize(SIMS = epi_doNothingDurInf7I2000) 254 | peaktime_DurInf7I2000[[2]] = summarisePeakTimePeakSize(SIMS = epi_baseDurInf7I2000) 255 | peaktime_DurInf7I2000[[3]] = summarisePeakTimePeakSize(SIMS = epi_marchDurInf7I2000) 256 | peaktime_DurInf7I2000[[4]] = summarisePeakTimePeakSize(SIMS = epi_aprilDurInf7I2000) 257 | 258 | covid_DurInf7I2000 = list() 259 | covid_DurInf7I2000[[1]] = summariseSimulations_mid(CI = 50,SIMS = epi_doNothingDurInf7I2000) 260 | covid_DurInf7I2000[[2]] = summariseSimulations_mid(CI = 50,SIMS = epi_baseDurInf7I2000) 261 | covid_DurInf7I2000[[3]] = summariseSimulations_mid(CI = 50,SIMS = epi_marchDurInf7I2000) 262 | covid_DurInf7I2000[[4]] = summariseSimulations_mid(CI = 50,SIMS = epi_aprilDurInf7I2000) 263 | 264 | AGEcovid_IDurInf7I2000 = list() 265 | AGEcovid_IDurInf7I2000[[1]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_doNothingDurInf7I2000) 266 | AGEcovid_IDurInf7I2000[[2]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_baseDurInf7I2000) 267 | AGEcovid_IDurInf7I2000[[3]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_marchDurInf7I2000) 268 | AGEcovid_IDurInf7I2000[[4]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_aprilDurInf7I2000) 269 | 270 | epiFirstSimDurInf7I2000 = list(epi_doNothingDurInf7I2000 = epi_doNothingDurInf7I2000[[1]], 271 | epi_baseDurInf7I2000= epi_baseDurInf7I2000[[1]], 272 | epi_marchDurInf7I2000 = epi_marchDurInf7I2000[[1]], 273 | epi_aprilDurInf7I2000 = epi_aprilDurInf7I2000[[1]]) 274 | 275 | save(covid_IDurInf3I2000,file = 'outputs/SEIR/covid_IDurInf3I2000.rdata') 276 | save(covid_SDurInf3I2000,file = 'outputs/SEIR/covid_SDurInf3I2000.rdata') 277 | save(covid_IDurInf7I2000,file = 'outputs/SEIR/covid_IDurInf7I2000.rdata') 278 | save(covid_SDurInf7I2000,file = 'outputs/SEIR/covid_SDurInf7I2000.rdata') 279 | 280 | save(peaktime_DurInf3I2000,file = 'outputs/SEIR/peaktime_DurInf3I2000.rdata') 281 | save(peaktime_DurInf7I2000,file = 'outputs/SEIR/peaktime_DurInf7I2000.rdata') 282 | 283 | save(covid_DurInf3I2000,file ='outputs/SEIR/covid_DurInf3I2000.rdata') 284 | save(covid_DurInf7I2000,file ='outputs/SEIR/covid_DurInf7I2000.rdata') 285 | 286 | save(AGEcovid_IDurInf3I2000,file ='outputs/SEIR/AGEcovid_IDurInf3I2000.rdata') 287 | save(AGEcovid_IDurInf7I2000,file ='outputs/SEIR/AGEcovid_IDurInf7I2000.rdata') 288 | 289 | save(epiFirstSimDurInf3I2000,file ='outputs/SEIR/epiFirstSimDurInf3I2000.rdata') 290 | save(epiFirstSimDurInf7I2000,file ='outputs/SEIR/epiFirstSimDurInf7I2000.rdata') 291 | 292 | rm(epi_doNothingDurInf3I2000,epi_baseDurInf3I2000,epi_marchDurInf3I2000,epi_aprilDurInf3I2000) 293 | rm(epi_doNothingDurInf7I2000,epi_baseDurInf7I2000,epi_marchDurInf7I2000,epi_aprilDurInf7I2000) 294 | 295 | 296 | 297 | 298 | 299 | 300 | 301 | 302 | 303 | -------------------------------------------------------------------------------- /codes/2_simOutbreak_ncov_SEIcIscR.r: -------------------------------------------------------------------------------- 1 | ## To simulate n_simSEIcIscR outbreaks 2 | 3 | nsim = 200 4 | 5 | set.seed(123) 6 | r0postCrI = r0posterior 7 | # hist(r0postCrI) 8 | # summary(r0postCrI) 9 | R0est = sample(x = r0postCrI,size = nsim) 10 | # print(R0est) 11 | 12 | ## To simulate n_sim SEIcIscR outbreaks: duration of infection = 3 days, initial infected n=~200 infected 13 | epi_doNothingDurInf3 = vector('list',nsim) 14 | epi_baseDurInf3 = vector('list',nsim) 15 | epi_marchDurInf3 = vector('list',nsim) 16 | epi_aprilDurInf3 = vector('list',nsim) 17 | start = Sys.time() 18 | durInfSim = 3 19 | initialI = 0.0002 20 | for(sim in 1:nsim) 21 | { 22 | epi_doNothingDurInf3[[sim]] = simulateOutbreakSEIcIscR(R0t =R0est[sim] ,dateStartSchoolClosure = as.Date('2019-11-01'), 23 | dateStartIntenseIntervention = as.Date('2019-11-01'), dateEndIntenseIntervention = as.Date('2019-11-01'), 24 | pWorkOpen = c(1,1,1,1),numWeekStagger = c(0,0,0),pInfected=initialI,durInf = durInfSim) 25 | epi_baseDurInf3[[sim]] = simulateOutbreakSEIcIscR(R0t =R0est[sim] ,dateEndIntenseIntervention = as.Date('2020-01-31'),pWorkOpen = c(0.1,0.75,1,1), 26 | numWeekStagger = c(10/7,10/7,10/7),pInfected=initialI,durInf = durInfSim) 27 | epi_marchDurInf3[[sim]] = simulateOutbreakSEIcIscR(R0t =R0est[sim] ,dateEndIntenseIntervention = as.Date('2020-03-01'), 28 | pInfected=initialI,durInf = durInfSim) 29 | epi_aprilDurInf3[[sim]] = simulateOutbreakSEIcIscR(R0t =R0est[sim] ,dateEndIntenseIntervention = as.Date('2020-04-01'), 30 | pInfected=initialI,durInf = durInfSim) 31 | if(sim%%10==0) print(paste0('Done with simulation ',sim)) 32 | } 33 | end = Sys.time() 34 | print(end-start) 35 | 36 | covid_SDurInf3sc = list() 37 | covid_SDurInf3sc[[1]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_doNothingDurInf3) 38 | covid_SDurInf3sc[[2]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_baseDurInf3) 39 | covid_SDurInf3sc[[3]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_marchDurInf3) 40 | covid_SDurInf3sc[[4]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_aprilDurInf3) 41 | 42 | covid_IDurInf3sc = list() 43 | covid_IDurInf3sc[[1]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_doNothingDurInf3) 44 | covid_IDurInf3sc[[2]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_baseDurInf3) 45 | covid_IDurInf3sc[[3]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_marchDurInf3) 46 | covid_IDurInf3sc[[4]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_aprilDurInf3) 47 | 48 | peaktime_DurInf3sc = list() 49 | peaktime_DurInf3sc[[1]] = summarisePeakTimePeakSize(SIMS = epi_doNothingDurInf3) 50 | peaktime_DurInf3sc[[2]] = summarisePeakTimePeakSize(SIMS = epi_baseDurInf3) 51 | peaktime_DurInf3sc[[3]] = summarisePeakTimePeakSize(SIMS = epi_marchDurInf3) 52 | peaktime_DurInf3sc[[4]] = summarisePeakTimePeakSize(SIMS = epi_aprilDurInf3) 53 | 54 | covid_DurInf3sc = list() 55 | covid_DurInf3sc[[1]] = summariseSimulations_mid(CI = 50,SIMS = epi_doNothingDurInf3) 56 | covid_DurInf3sc[[2]] = summariseSimulations_mid(CI = 50,SIMS = epi_baseDurInf3) 57 | covid_DurInf3sc[[3]] = summariseSimulations_mid(CI = 50,SIMS = epi_marchDurInf3) 58 | covid_DurInf3sc[[4]] = summariseSimulations_mid(CI = 50,SIMS = epi_aprilDurInf3) 59 | 60 | AGEcovid_IDurInf3sc = list() 61 | AGEcovid_IDurInf3sc[[1]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_doNothingDurInf3) 62 | AGEcovid_IDurInf3sc[[2]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_baseDurInf3) 63 | AGEcovid_IDurInf3sc[[3]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_marchDurInf3) 64 | AGEcovid_IDurInf3sc[[4]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_aprilDurInf3) 65 | 66 | epiFirstSimDurInf3sc = list(epi_doNothingDurInf3 = epi_doNothingDurInf3[[1]], 67 | epi_baseDurInf3= epi_baseDurInf3[[1]], 68 | epi_marchDurInf3 = epi_marchDurInf3[[1]], 69 | epi_aprilDurInf3 = epi_aprilDurInf3[[1]]) 70 | 71 | 72 | 73 | ## To simulate n_simSEIcIscR outbreaks: duration of infection = 7 days, initial infected n=~200 infected 74 | 75 | epi_doNothingDurInf7 = vector('list',nsim) 76 | epi_baseDurInf7 = vector('list',nsim) 77 | epi_marchDurInf7 = vector('list',nsim) 78 | epi_aprilDurInf7 = vector('list',nsim) 79 | start = Sys.time() 80 | durInfSim = 7 81 | initialI = 0.0002 82 | for(sim in 1:nsim) 83 | { 84 | epi_doNothingDurInf7[[sim]] = simulateOutbreakSEIcIscR(R0t =R0est[sim] ,dateStartSchoolClosure = as.Date('2019-11-01'), 85 | dateStartIntenseIntervention = as.Date('2019-11-01'), dateEndIntenseIntervention = as.Date('2019-11-01'), 86 | pWorkOpen = c(1,1,1,1),numWeekStagger = c(0,0,0,0),pInfected=initialI,durInf = durInfSim) 87 | epi_baseDurInf7[[sim]] = simulateOutbreakSEIcIscR(R0t =R0est[sim] ,dateEndIntenseIntervention = as.Date('2020-01-31'),pWorkOpen = c(0.1,0.75,1,1), 88 | numWeekStagger = c(10/7,10/7,10/7),pInfected=initialI,durInf = durInfSim) 89 | epi_marchDurInf7[[sim]] = simulateOutbreakSEIcIscR(R0t =R0est[sim] ,dateEndIntenseIntervention = as.Date('2020-03-01'), 90 | pInfected=initialI,durInf = durInfSim) 91 | epi_aprilDurInf7[[sim]] = simulateOutbreakSEIcIscR(R0t =R0est[sim] ,dateEndIntenseIntervention = as.Date('2020-04-01'), 92 | pInfected=initialI,durInf = durInfSim) 93 | if(sim%%10==0) print(paste0('Done with simulation ',sim)) 94 | } 95 | end = Sys.time() 96 | print(end-start) 97 | 98 | covid_SDurInf7sc = list() 99 | covid_SDurInf7sc[[1]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_doNothingDurInf7) 100 | covid_SDurInf7sc[[2]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_baseDurInf7) 101 | covid_SDurInf7sc[[3]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_marchDurInf7) 102 | covid_SDurInf7sc[[4]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_aprilDurInf7) 103 | 104 | covid_IDurInf7sc = list() 105 | covid_IDurInf7sc[[1]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_doNothingDurInf7) 106 | covid_IDurInf7sc[[2]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_baseDurInf7) 107 | covid_IDurInf7sc[[3]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_marchDurInf7) 108 | covid_IDurInf7sc[[4]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_aprilDurInf7) 109 | 110 | peaktime_DurInf7sc = list() 111 | peaktime_DurInf7sc[[1]] = summarisePeakTimePeakSize(SIMS = epi_doNothingDurInf7) 112 | peaktime_DurInf7sc[[2]] = summarisePeakTimePeakSize(SIMS = epi_baseDurInf7) 113 | peaktime_DurInf7sc[[3]] = summarisePeakTimePeakSize(SIMS = epi_marchDurInf7) 114 | peaktime_DurInf7sc[[4]] = summarisePeakTimePeakSize(SIMS = epi_aprilDurInf7) 115 | 116 | covid_DurInf7sc = list() 117 | covid_DurInf7sc[[1]] = summariseSimulations_mid(CI = 50,SIMS = epi_doNothingDurInf7) 118 | covid_DurInf7sc[[2]] = summariseSimulations_mid(CI = 50,SIMS = epi_baseDurInf7) 119 | covid_DurInf7sc[[3]] = summariseSimulations_mid(CI = 50,SIMS = epi_marchDurInf7) 120 | covid_DurInf7sc[[4]] = summariseSimulations_mid(CI = 50,SIMS = epi_aprilDurInf7) 121 | 122 | AGEcovid_IDurInf7sc = list() 123 | AGEcovid_IDurInf7sc[[1]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_doNothingDurInf7) 124 | AGEcovid_IDurInf7sc[[2]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_baseDurInf7) 125 | AGEcovid_IDurInf7sc[[3]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_marchDurInf7) 126 | AGEcovid_IDurInf7sc[[4]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_aprilDurInf7) 127 | 128 | epiFirstSimDurInf7sc = list(epi_doNothingDurInf7 = epi_doNothingDurInf7[[1]], 129 | epi_baseDurInf7= epi_baseDurInf7[[1]], 130 | epi_marchDurInf7 = epi_marchDurInf7[[1]], 131 | epi_aprilDurInf7 = epi_aprilDurInf7[[1]]) 132 | 133 | save(covid_IDurInf3sc,file = 'outputs/SEIcIscR/covid_IDurInf3.rdata') 134 | save(covid_SDurInf3sc,file = 'outputs/SEIcIscR/covid_SDurInf3.rdata') 135 | save(covid_IDurInf7sc,file = 'outputs/SEIcIscR/covid_IDurInf7.rdata') 136 | save(covid_SDurInf7sc,file = 'outputs/SEIcIscR/covid_SDurInf7.rdata') 137 | 138 | save(peaktime_DurInf3sc,file = 'outputs/SEIcIscR/peaktime_DurInf3.rdata') 139 | save(peaktime_DurInf7sc,file = 'outputs/SEIcIscR/peaktime_DurInf7.rdata') 140 | 141 | save(covid_DurInf3sc,file ='outputs/SEIcIscR/covid_DurInf3.rdata') 142 | save(covid_DurInf7sc,file ='outputs/SEIcIscR/covid_DurInf7.rdata') 143 | 144 | save(AGEcovid_IDurInf3sc,file ='outputs/SEIcIscR/AGEcovid_IDurInf3.rdata') 145 | save(AGEcovid_IDurInf7sc,file ='outputs/SEIcIscR/AGEcovid_IDurInf7.rdata') 146 | 147 | save(epiFirstSimDurInf3sc,file ='outputs/SEIcIscR/epiFirstSimDurInf3.rdata') 148 | save(epiFirstSimDurInf7sc,file ='outputs/SEIcIscR/epiFirstSimDurInf7.rdata') 149 | 150 | rm(epi_doNothingDurInf3,epi_baseDurInf3,epi_marchDurInf3,epi_aprilDurInf3) 151 | rm(epi_doNothingDurInf7,epi_baseDurInf7,epi_marchDurInf7,epi_aprilDurInf7) 152 | 153 | 154 | ## To simulate n_simSEIcIscR outbreaks: duration of infection = 3 days, initial infected n=~2000 infected 155 | epi_doNothingDurInf3I2000 = vector('list',nsim) 156 | epi_baseDurInf3I2000 = vector('list',nsim) 157 | epi_marchDurInf3I2000 = vector('list',nsim) 158 | epi_aprilDurInf3I2000 = vector('list',nsim) 159 | start = Sys.time() 160 | durInfSim = 3 161 | initialI = 0.002 162 | for(sim in 1:nsim) 163 | { 164 | epi_doNothingDurInf3I2000[[sim]] = simulateOutbreakSEIcIscR(R0t =R0est[sim] ,dateStartSchoolClosure = as.Date('2019-11-01'), 165 | dateStartIntenseIntervention = as.Date('2019-11-01'), dateEndIntenseIntervention = as.Date('2019-11-01'), 166 | pWorkOpen = c(1,1,1,1),numWeekStagger = c(0,0,0),pInfected=initialI,durInf = durInfSim) 167 | epi_baseDurInf3I2000[[sim]] = simulateOutbreakSEIcIscR(R0t =R0est[sim] ,dateEndIntenseIntervention = as.Date('2020-01-31'),pWorkOpen = c(0.1,0.75,1,1), 168 | numWeekStagger = c(10/7,10/7,10/7),pInfected=initialI,durInf = durInfSim) 169 | epi_marchDurInf3I2000[[sim]] = simulateOutbreakSEIcIscR(R0t =R0est[sim] ,dateEndIntenseIntervention = as.Date('2020-03-01'), 170 | pInfected=initialI,durInf = durInfSim) 171 | epi_aprilDurInf3I2000[[sim]] = simulateOutbreakSEIcIscR(R0t =R0est[sim] ,dateEndIntenseIntervention = as.Date('2020-04-01'), 172 | pInfected=initialI,durInf = durInfSim) 173 | if(sim%%10==0) print(paste0('Done with simulation ',sim)) 174 | } 175 | end = Sys.time() 176 | print(end-start) 177 | 178 | covid_SDurInf3I2000sc = list() 179 | covid_SDurInf3I2000sc[[1]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_doNothingDurInf3I2000) 180 | covid_SDurInf3I2000sc[[2]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_baseDurInf3I2000) 181 | covid_SDurInf3I2000sc[[3]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_marchDurInf3I2000) 182 | covid_SDurInf3I2000sc[[4]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_aprilDurInf3I2000) 183 | 184 | covid_IDurInf3I2000sc = list() 185 | covid_IDurInf3I2000sc[[1]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_doNothingDurInf3I2000) 186 | covid_IDurInf3I2000sc[[2]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_baseDurInf3I2000) 187 | covid_IDurInf3I2000sc[[3]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_marchDurInf3I2000) 188 | covid_IDurInf3I2000sc[[4]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_aprilDurInf3I2000) 189 | 190 | peaktime_DurInf3I2000sc = list() 191 | peaktime_DurInf3I2000sc[[1]] = summarisePeakTimePeakSize(SIMS = epi_doNothingDurInf3I2000) 192 | peaktime_DurInf3I2000sc[[2]] = summarisePeakTimePeakSize(SIMS = epi_baseDurInf3I2000) 193 | peaktime_DurInf3I2000sc[[3]] = summarisePeakTimePeakSize(SIMS = epi_marchDurInf3I2000) 194 | peaktime_DurInf3I2000sc[[4]] = summarisePeakTimePeakSize(SIMS = epi_aprilDurInf3I2000) 195 | 196 | covid_DurInf3I2000sc = list() 197 | covid_DurInf3I2000sc[[1]] = summariseSimulations_mid(CI = 50,SIMS = epi_doNothingDurInf3I2000) 198 | covid_DurInf3I2000sc[[2]] = summariseSimulations_mid(CI = 50,SIMS = epi_baseDurInf3I2000) 199 | covid_DurInf3I2000sc[[3]] = summariseSimulations_mid(CI = 50,SIMS = epi_marchDurInf3I2000) 200 | covid_DurInf3I2000sc[[4]] = summariseSimulations_mid(CI = 50,SIMS = epi_aprilDurInf3I2000) 201 | 202 | AGEcovid_IDurInf3I2000sc = list() 203 | AGEcovid_IDurInf3I2000sc[[1]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_doNothingDurInf3I2000) 204 | AGEcovid_IDurInf3I2000sc[[2]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_baseDurInf3I2000) 205 | AGEcovid_IDurInf3I2000sc[[3]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_marchDurInf3I2000) 206 | AGEcovid_IDurInf3I2000sc[[4]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_aprilDurInf3I2000) 207 | 208 | epiFirstSimDurInf3I2000sc = list(epi_doNothingDurInf3I2000 = epi_doNothingDurInf3I2000[[1]], 209 | epi_baseDurInf3I2000= epi_baseDurInf3I2000[[1]], 210 | epi_marchDurInf3I2000 = epi_marchDurInf3I2000[[1]], 211 | epi_aprilDurInf3I2000 = epi_aprilDurInf3I2000[[1]]) 212 | 213 | 214 | 215 | ## To simulate n_simSEIcIscR outbreaks: duration of infection = 7 days, initial infected n=~2000 infected 216 | 217 | epi_doNothingDurInf7I2000 = vector('list',nsim) 218 | epi_baseDurInf7I2000 = vector('list',nsim) 219 | epi_marchDurInf7I2000 = vector('list',nsim) 220 | epi_aprilDurInf7I2000 = vector('list',nsim) 221 | start = Sys.time() 222 | durInfSim = 7 223 | initialI = 0.002 224 | for(sim in 1:nsim) 225 | { 226 | epi_doNothingDurInf7I2000[[sim]] = simulateOutbreakSEIcIscR(R0t =R0est[sim] ,dateStartSchoolClosure = as.Date('2019-11-01'), 227 | dateStartIntenseIntervention = as.Date('2019-11-01'), dateEndIntenseIntervention = as.Date('2019-11-01'), 228 | pWorkOpen = c(1,1,1,1),numWeekStagger = c(0,0,0),pInfected=initialI,durInf = durInfSim) 229 | epi_baseDurInf7I2000[[sim]] = simulateOutbreakSEIcIscR(R0t =R0est[sim] ,dateEndIntenseIntervention = as.Date('2020-01-31'),pWorkOpen = c(0.1,0.75,1,1), 230 | numWeekStagger = c(10/7,10/7,10/7),pInfected=initialI,durInf = durInfSim) 231 | epi_marchDurInf7I2000[[sim]] = simulateOutbreakSEIcIscR(R0t =R0est[sim] ,dateEndIntenseIntervention = as.Date('2020-03-01'), 232 | pInfected=initialI,durInf = durInfSim) 233 | epi_aprilDurInf7I2000[[sim]] = simulateOutbreakSEIcIscR(R0t =R0est[sim] ,dateEndIntenseIntervention = as.Date('2020-04-01'), 234 | pInfected=initialI,durInf = durInfSim) 235 | if(sim%%10==0) print(paste0('Done with simulation ',sim)) 236 | } 237 | end = Sys.time() 238 | print(end-start) 239 | 240 | covid_SDurInf7I2000sc = list() 241 | covid_SDurInf7I2000sc[[1]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_doNothingDurInf7I2000) 242 | covid_SDurInf7I2000sc[[2]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_baseDurInf7I2000) 243 | covid_SDurInf7I2000sc[[3]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_marchDurInf7I2000) 244 | covid_SDurInf7I2000sc[[4]] = summariseSimulations(VAR = 'S',CI = 50,SIMS = epi_aprilDurInf7I2000) 245 | 246 | covid_IDurInf7I2000sc = list() 247 | covid_IDurInf7I2000sc[[1]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_doNothingDurInf7I2000) 248 | covid_IDurInf7I2000sc[[2]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_baseDurInf7I2000) 249 | covid_IDurInf7I2000sc[[3]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_marchDurInf7I2000) 250 | covid_IDurInf7I2000sc[[4]] = summariseSimulations(VAR = 'incidence',CI = 50,SIMS = epi_aprilDurInf7I2000) 251 | 252 | peaktime_DurInf7I2000sc = list() 253 | peaktime_DurInf7I2000sc[[1]] = summarisePeakTimePeakSize(SIMS = epi_doNothingDurInf7I2000) 254 | peaktime_DurInf7I2000sc[[2]] = summarisePeakTimePeakSize(SIMS = epi_baseDurInf7I2000) 255 | peaktime_DurInf7I2000sc[[3]] = summarisePeakTimePeakSize(SIMS = epi_marchDurInf7I2000) 256 | peaktime_DurInf7I2000sc[[4]] = summarisePeakTimePeakSize(SIMS = epi_aprilDurInf7I2000) 257 | 258 | covid_DurInf7I2000sc = list() 259 | covid_DurInf7I2000sc[[1]] = summariseSimulations_mid(CI = 50,SIMS = epi_doNothingDurInf7I2000) 260 | covid_DurInf7I2000sc[[2]] = summariseSimulations_mid(CI = 50,SIMS = epi_baseDurInf7I2000) 261 | covid_DurInf7I2000sc[[3]] = summariseSimulations_mid(CI = 50,SIMS = epi_marchDurInf7I2000) 262 | covid_DurInf7I2000sc[[4]] = summariseSimulations_mid(CI = 50,SIMS = epi_aprilDurInf7I2000) 263 | 264 | AGEcovid_IDurInf7I2000sc = list() 265 | AGEcovid_IDurInf7I2000sc[[1]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_doNothingDurInf7I2000) 266 | AGEcovid_IDurInf7I2000sc[[2]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_baseDurInf7I2000) 267 | AGEcovid_IDurInf7I2000sc[[3]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_marchDurInf7I2000) 268 | AGEcovid_IDurInf7I2000sc[[4]] = summariseSimulationsAGE(VAR = 'incidence',CI = 50,SIMS = epi_aprilDurInf7I2000) 269 | 270 | epiFirstSimDurInf7I2000sc = list(epi_doNothingDurInf7I2000 = epi_doNothingDurInf7I2000[[1]], 271 | epi_baseDurInf7I2000= epi_baseDurInf7I2000[[1]], 272 | epi_marchDurInf7I2000 = epi_marchDurInf7I2000[[1]], 273 | epi_aprilDurInf7I2000 = epi_aprilDurInf7I2000[[1]]) 274 | 275 | save(covid_IDurInf3I2000sc,file = 'outputs/SEIcIscR/covid_IDurInf3I2000.rdata') 276 | save(covid_SDurInf3I2000sc,file = 'outputs/SEIcIscR/covid_SDurInf3I2000.rdata') 277 | save(covid_IDurInf7I2000sc,file = 'outputs/SEIcIscR/covid_IDurInf7I2000.rdata') 278 | save(covid_SDurInf7I2000sc,file = 'outputs/SEIcIscR/covid_SDurInf7I2000.rdata') 279 | 280 | save(peaktime_DurInf3I2000sc,file = 'outputs/SEIcIscR/peaktime_DurInf3I2000.rdata') 281 | save(peaktime_DurInf7I2000sc,file = 'outputs/SEIcIscR/peaktime_DurInf7I2000.rdata') 282 | 283 | save(covid_DurInf3I2000sc,file ='outputs/SEIcIscR/covid_DurInf3I2000.rdata') 284 | save(covid_DurInf7I2000sc,file ='outputs/SEIcIscR/covid_DurInf7I2000.rdata') 285 | 286 | save(AGEcovid_IDurInf3I2000sc,file ='outputs/SEIcIscR/AGEcovid_IDurInf3I2000.rdata') 287 | save(AGEcovid_IDurInf7I2000sc,file ='outputs/SEIcIscR/AGEcovid_IDurInf7I2000.rdata') 288 | 289 | save(epiFirstSimDurInf3I2000sc,file ='outputs/SEIcIscR/epiFirstSimDurInf3I2000.rdata') 290 | save(epiFirstSimDurInf7I2000sc,file ='outputs/SEIcIscR/epiFirstSimDurInf7I2000.rdata') 291 | 292 | rm(epi_doNothingDurInf3I2000,epi_baseDurInf3I2000,epi_marchDurInf3I2000,epi_aprilDurInf3I2000) 293 | rm(epi_doNothingDurInf7I2000,epi_baseDurInf7I2000,epi_marchDurInf7I2000,epi_aprilDurInf7I2000) 294 | 295 | 296 | 297 | 298 | 299 | 300 | -------------------------------------------------------------------------------- /codes/function_modelSEIcIscR.r: -------------------------------------------------------------------------------- 1 | ## functions to simulate the SEIR and SEIcIscR outbreak 2 | 3 | ## load data: Population age structure 4 | 5 | loadPopInfo = function(POP) 6 | { 7 | pop = list() 8 | pop$N = sum(POP$popage) # Population size 9 | pop$p_age = POP$propage # Population age structure of China 10 | return(pop) 11 | } 12 | 13 | loadInterventions = function(p_workopen) 14 | { 15 | list( 16 | # constraints under a DO-NOTHING scenario 17 | base =list(home = diag(1,16,16), 18 | work = diag(1,16,16), 19 | school = diag(1,16,16), 20 | others = diag(1,16,16)), 21 | # Wuhan's lockdown--assume from XX Jan to XX Feb 22 | wuhanlockdown = list(home = diag(1,16,16), 23 | work = diag(0.1,16,16), 24 | school = diag(0,16,16), 25 | others = diag(c(rep(0.1,4),rep(0.1,12)))), 26 | # constraints under school closure + some social distancing for school-age going children but 100% workplace 27 | schcloseonly = list(home = diag(c(rep(1,4),rep(1,12))), 28 | work = diag(1,16,16), 29 | school = diag(0,16,16), 30 | others = diag(c(rep(0.5,4),rep(1,12)))), 31 | # constraints under work place distancing only (MAYBE UNREALISTIC, should close schools too) 32 | workplacedistonly = list(home = diag(1,16,16), 33 | work = diag(0.5,16,16), 34 | school = diag(1,16,16), 35 | others = diag(0.1,16,16)) , 36 | 37 | # constraints under work place distancing + schoolclosure 38 | schcloseworkplacedist = list(home = diag(1,16,16), 39 | work = diag(p_workopen,16,16), 40 | school = diag(0,16,16), 41 | others = diag(c(rep(0.1,4),rep(0.1,12)))), 42 | # Post Outbeak, people still cautious 43 | postoutbreak = list(home = diag(1,16,16), 44 | work = diag(1.0,16,16), 45 | school = diag(1.0,16,16), 46 | others = diag(c(rep(1.0,4),rep(1.0,12))))) 47 | 48 | } 49 | 50 | 51 | getbeta = function(R0t,constraints,gamma,p_age,calculate_transmission_probability=1,CONTACTMATRIX = contacts) 52 | { 53 | # 1) R0 54 | # 2) gamma = removal rate 55 | # 3) f = population age proportion 56 | # 4) constraints = a scale matrix contstraint age- and location-specific contact matrices (a linear combination over all locations; TODO to specify carefully based on interventions) 57 | # 5) calculate_transmission_probability if this is 1, then calculate the transmission probability from R0 otherwise, assume it is beta=0.05 58 | # 6) npop = population size 59 | 60 | # constraints for age-specific contacts at home, work, school, others 61 | n = 16 #length(p_age) 62 | constraints_base = list(home = diag(1,n), 63 | work = diag(1,n), 64 | school = diag(1,n), 65 | others = diag(1,n)) # constraints under a DO-NOTHING scenario 66 | 67 | Csym <- lapply(CONTACTMATRIX, function(x, p_age) (x + t(x)*((p_age)%*%t(1/p_age)))/2, p_age) # make sure contacts are reciprocal 68 | CONTACTMATRIX=Csym 69 | C = constraints_base[[1]]%*%CONTACTMATRIX[[1]]+ 70 | constraints_base[[2]]%*%CONTACTMATRIX[[2]]+ 71 | constraints_base[[3]]%*%CONTACTMATRIX[[3]]+ 72 | constraints_base[[4]]%*%CONTACTMATRIX[[4]] 73 | 74 | 75 | if (calculate_transmission_probability==1){ 76 | M = C 77 | for(i in 1:n) 78 | { 79 | for(j in 1:n){ 80 | M[i,j] = C[i,j]*p_age[i]/p_age[j] 81 | } 82 | } 83 | eig = eigen(M) 84 | beta = R0t*gamma/max(Re(eig$values)) # reverse engineer beta from the R0 and gamma 85 | beta = beta 86 | }else{ 87 | beta = 0.025#0.05 88 | } 89 | results = list(beta) 90 | names(results) =c('beta') 91 | return(results) 92 | } 93 | 94 | 95 | # Children as infectious and as susceptible 96 | simulateOutbreakSEIR = function(R0t,rho, R0tpostoutbreak = 1.5,dateEndIntenseIntervention, #date we begin relaxing intense intervention 97 | pWorkOpen = c(0.1,0.25,0.5,0.9), # pWorkOpen: proportion of the work force that is working (will be time-varying) 98 | dateStartSchoolClosure = as.Date('2020-01-15') , # cause winter term break 99 | dateStartIntenseIntervention = as.Date('2020-01-23') , #Intense intervention: starts at Wuhan Lockdown 100 | dateStart = as.Date('2019-11-01'),POP = wuhanpop,numWeekStagger=c(2,4,6),pInfected=0.0002,durInf = 7,contacts_china=contacts) 101 | { 102 | # debug dateStartIntenseIntervention = as.Date('2020-01-23') 103 | # debug dateEndIntenseIntervention = as.Date('2020-03-01') 104 | # debug R0est = rep(2,3660) 105 | # debug rho = rep(0.8,3660) 106 | # debug pWorkOpen = c(0.1,0.25,0.5,1) 107 | 108 | 109 | # Load population information 110 | # pop = loadPopInfo(POP = wuhanpop) 111 | pop = list() 112 | pop$N = sum(POP$popage) 113 | pop$p_age = wuhanpop$propage 114 | N_age = pop$N*pop$p_age # Population age structure (in numbers) 115 | # contacts_china = CONTACTS 116 | 117 | 118 | # Specify epi info 119 | durLat = 6.4; # Mean latent period (days) from Backer, et al (2020) 120 | durInf = durInf; # Mean duration of infectiousness (days) 121 | gamma = 1-exp(-1/durInf); # removal rate 122 | alpha = 1-exp(-1/durLat); # infection rate 123 | dt = 1; # Time step (days) 124 | tmax = 428; # Time horizon (days) 366 days in 2020 cause of leap year 125 | numSteps = tmax/dt; # Total number of simulation time steps 126 | # dateStart = as.Date('2019-12-01') # included as a function argument 127 | dateEnd = dateStart+(tmax-1) 128 | dateStartCNY = as.Date('2020-01-25') 129 | dateEndCNY = as.Date('2020-01-31') 130 | 131 | # Declare the state variables and related variables: 132 | # The values of these variables change over time 133 | S = E = I = R = H = array(0,c(numSteps,length(pop$p_age))) 134 | lambda = incidence = reported = cumulativeIncidence = array(0,c(numSteps,length(pop$p_age))) 135 | time = array(0,numSteps) 136 | 137 | 138 | # Initialise the time-dependent variables, i.e. setting the values of the variables at time 0 139 | E[1,] = 0 140 | I[1,] = pInfected*sum(N_age)/16#rpois(length(N_age),lambda = pInfected*sum(N_age)/16) # 100 # Assign 100 infected person in each age group (TODO RELAX?) 141 | R[1,] = 0 142 | S[1,] = N_age-E[1,]-I[1,]-R[1,] 143 | H[1,] = 0 # Accumulator function for the infected cases (I) that get re 144 | incidence[1,] = 0; 145 | reported[1,] = 0; 146 | time[1] = 0; 147 | 148 | 149 | ## INTERVENTIONS 150 | # School closed 2020-02-10, lockdown (intense intervention) started 2020-01-23, end of intense intervention: user-specified 151 | # note that intense intervention is time-varying control by pWorkOpen: proportion of the work force that is working 152 | # debug pWorkOpen = c(0.1,0.25,0.5,1) 153 | tStartSchoolClosure = as.vector(dateStartSchoolClosure - dateStart)+1 154 | tStartIntenseIntervention = as.vector(dateStartIntenseIntervention - dateStart)+1 # for pw = 0.1 155 | tEndIntenseIntervention = as.vector(dateEndIntenseIntervention - dateStart)+1 # for pw = 0.1 156 | tRelaxIntervention1 = tEndIntenseIntervention + numWeekStagger[1]*7 # for pw = 0.25 157 | tRelaxIntervention2 = tEndIntenseIntervention + numWeekStagger[2]*7 # for pw = 0.5 158 | tRelaxIntervention3 = tEndIntenseIntervention + numWeekStagger[3]*7 # for pw = 1 159 | # tStartEndClosure = as.vector(dateEndSchoolClosure - dateStart)+1 160 | pwork = array(1,numSteps) 161 | pwork[1:tRelaxIntervention3] =c(rep(1,(tStartIntenseIntervention-0)), # dont know there is outbreak 162 | rep(pWorkOpen[1],(tEndIntenseIntervention-tStartIntenseIntervention)), 163 | rep(pWorkOpen[2],(tRelaxIntervention1-tEndIntenseIntervention)), 164 | rep(pWorkOpen[3],(tRelaxIntervention2-tRelaxIntervention1)), 165 | rep(pWorkOpen[4],(tRelaxIntervention3-tRelaxIntervention2))) 166 | R0tpostoutbreak = R0t #overwrites the default reduction in R0 post-outbreak 167 | 168 | beta = getbeta(R0t = R0t,constraints = constraintsIntervention$base,gamma = gamma,p_age = pop$p_age) 169 | if(pWorkOpen[2]<1) beta_postfirstwave = getbeta(R0t = R0tpostoutbreak,constraints = constraintsIntervention$base,gamma = gamma,p_age = pop$p_age) 170 | if(pWorkOpen[2]>=1) beta_postfirstwave = beta#getbeta(R0t = R0t[2],constraints = constraintsIntervention$base,gamma = gamma,p_age = pop$p_age) 171 | for (stepIndex in 1: (numSteps-1)) 172 | { 173 | 174 | # load plausible intervetions 175 | constraintsIntervention = loadInterventions(p_workopen = pwork[stepIndex]) 176 | 177 | ## Age- and location-specific contact rates for the given interventions 178 | 179 | # I0: before school winter break intervention period, use base-case 180 | if(time[stepIndex] < tStartSchoolClosure) 181 | { 182 | CONSTRAINT = constraintsIntervention$base 183 | } 184 | # I1: When school winter break but before lockdown period, use 'schcloseonly' 185 | if(time[stepIndex] >= tStartSchoolClosure & time[stepIndex] < tStartIntenseIntervention) 186 | { 187 | INTERVENTION = "schcloseonly" 188 | CONSTRAINT = constraintsIntervention[[INTERVENTION]] 189 | } 190 | # I2: Wuhan lockdown period, use 'schcloseonly' 191 | if(time[stepIndex] >= tStartIntenseIntervention & time[stepIndex] < tRelaxIntervention3) 192 | { 193 | INTERVENTION = "schcloseworkplacedist" 194 | CONSTRAINT = constraintsIntervention[[INTERVENTION]] 195 | } 196 | # I0: before school winter break intervention period, use base-case 197 | if(time[stepIndex] >= tRelaxIntervention3) 198 | { 199 | if(pWorkOpen[2]<1) CONSTRAINT = constraintsIntervention$postoutbreak 200 | if(pWorkOpen[2]>=1) CONSTRAINT = constraintsIntervention$base 201 | } 202 | # 203 | 204 | C = CONSTRAINT[[1]]%*%contacts_china[[1]]+ 205 | CONSTRAINT[[2]]%*%contacts_china[[2]]+ 206 | CONSTRAINT[[3]]%*%contacts_china[[3]]+ 207 | CONSTRAINT[[4]]%*%contacts_china[[4]] 208 | 209 | # calculate the force of infection 210 | 211 | # beta = getbeta(R0t = R0t[stepIndex],constraints = constraintsIntervention$base,gamma = gamma,p_age = pop$p_age) 212 | 213 | if(time[stepIndex] < tEndIntenseIntervention+0) lambda[stepIndex,] = as.numeric(beta)*(as.matrix(C)%*%as.matrix(I[stepIndex,]/N_age)); 214 | if(time[stepIndex] >= tEndIntenseIntervention+0) lambda[stepIndex,] = as.numeric(beta_postfirstwave)*(as.matrix(C)%*%as.matrix(I[stepIndex,]/N_age)); 215 | # lambda[stepIndex,] = as.numeric(beta)*(as.matrix(C)%*%as.matrix(I[stepIndex,]/N_age)); 216 | # calculate the number of infections and recoveries between time t and t+dt 217 | 218 | numInfection = lambda[stepIndex,]*S[stepIndex,]*dt; # S to E 219 | numInfected = alpha*E[stepIndex,]*dt; # E to I 220 | numRecovery = gamma*I[stepIndex,]*dt; # I to R 221 | numReported = numInfected*rho[stepIndex]; # I to H, but not removed from I (remember this is an accumulator function) 222 | 223 | # SEIR difference equations 224 | S[stepIndex+1,] = S[stepIndex,]-numInfection; 225 | E[stepIndex+1,] = E[stepIndex,]+numInfection-numInfected; 226 | I[stepIndex+1,] = I[stepIndex,]+numInfected-numRecovery; 227 | R[stepIndex+1,] = R[stepIndex,]+numRecovery; 228 | H[stepIndex+1,] = H[stepIndex,]+numReported; 229 | incidence[stepIndex+1,] = numInfected/dt; 230 | reported[stepIndex+1,] = numReported/dt; 231 | time[stepIndex+1] = time[stepIndex]+dt; 232 | 233 | } 234 | output = list(S =S, E = E, I = I, R = R, time = time,lambda=lambda, 235 | incidence = incidence, N_age= N_age, #reported = reported, 236 | R0t = R0t,#rho = rho, 237 | dateStart = dateStart, dateEnd = dateEnd, 238 | dateStartIntenseIntervention = dateStartIntenseIntervention, dateEndIntenseIntervention = dateEndIntenseIntervention, 239 | dateStartSchoolClosure = dateStartSchoolClosure, dateStartCNY = dateStartCNY,dateEndCNY = dateEndCNY) 240 | return(output) 241 | } 242 | 243 | # Children less infectious and as susceptible 244 | simulateOutbreakSEIcIscR = function(R0t,rho=c(rep(0.4,4),rep(0.8,12)), R0tpostoutbreak = 1.5,dateEndIntenseIntervention, #date we begin relaxing intense intervention 245 | pWorkOpen = c(0.1,0.25,0.5,0.9), # pWorkOpen: proportion of the work force that is working (will be time-varying) 246 | dateStartSchoolClosure = as.Date('2020-01-15') , # cause winter term break 247 | dateStartIntenseIntervention = as.Date('2020-01-23') , #Intense intervention: starts at Wuhan Lockdown 248 | dateStart = as.Date('2019-11-01'),POP = wuhanpop,numWeekStagger=c(2,4,6),pInfected=0.0002,durInf = 7,contacts_china=contacts) 249 | { 250 | # debug dateStartIntenseIntervention = as.Date('2020-01-23') 251 | # debug dateEndIntenseIntervention = as.Date('2020-03-01') 252 | # debug R0est = rep(2,3660) 253 | # debug rho = rep(0.8,3660) 254 | # debug pWorkOpen = c(0.1,0.25,0.5,1) 255 | 256 | 257 | # Load population information 258 | # pop = loadPopInfo(POP = wuhanpop) 259 | pop = list() 260 | pop$N = sum(POP$popage) 261 | pop$p_age = wuhanpop$propage 262 | N_age = pop$N*pop$p_age # Population age structure (in numbers) 263 | # contacts_china = CONTACTS 264 | 265 | 266 | # Specify epi info 267 | durLat = 6.4; # Mean latent period (days) from Backer, et al (2020) 268 | durInf = durInf; # Mean duration of infectiousness (days) 269 | gamma = 1-exp(-1/durInf); # removal rate 270 | alpha = 1-exp(-1/durLat); # infection rate 271 | dt = 1; # Time step (days) 272 | tmax = 428; # Time horizon (days) 366 days in 2020 cause of leap year 273 | numSteps = tmax/dt; # Total number of simulation time steps 274 | # dateStart = as.Date('2019-12-01') # included as a function argument 275 | dateEnd = dateStart+(tmax-1) 276 | dateStartCNY = as.Date('2020-01-25') 277 | dateEndCNY = as.Date('2020-01-31') 278 | 279 | # Declare the state variables and related variables: 280 | # The values of these variables change over time 281 | S = E = Isc = Ic = R = array(0,c(numSteps,length(pop$p_age))) 282 | lambda = incidence = subclinical = cumulativeIncidence = array(0,c(numSteps,length(pop$p_age))) 283 | time = array(0,numSteps) 284 | 285 | 286 | # Initialise the time-dependent variables, i.e. setting the values of the variables at time 0 287 | E[1,] = 0 288 | Ic[1,] = pInfected*sum(N_age)/16#rpois(length(N_age),lambda = pInfected*sum(N_age)/16) # 100 # Assign 100 infected person in each age group (TODO RELAX?) 289 | Isc[1,] = 0 290 | R[1,] = 0 291 | S[1,] = N_age-E[1,]-Ic[1,]-Isc[1,]-R[1,] 292 | incidence[1,] = 0; 293 | subclinical[1,] = 0; 294 | time[1] = 0; 295 | 296 | 297 | ## INTERVENTIONS 298 | # School closed 2020-02-10, lockdown (intense intervention) started 2020-01-23, end of intense intervention: user-specified 299 | # note that intense intervention is time-varying control by pWorkOpen: proportion of the work force that is working 300 | # debug pWorkOpen = c(0.1,0.25,0.5,1) 301 | tStartSchoolClosure = as.vector(dateStartSchoolClosure - dateStart)+1 302 | tStartIntenseIntervention = as.vector(dateStartIntenseIntervention - dateStart)+1 # for pw = 0.1 303 | tEndIntenseIntervention = as.vector(dateEndIntenseIntervention - dateStart)+1 # for pw = 0.1 304 | tRelaxIntervention1 = tEndIntenseIntervention + numWeekStagger[1]*7 # for pw = 0.25 305 | tRelaxIntervention2 = tEndIntenseIntervention + numWeekStagger[2]*7 # for pw = 0.5 306 | tRelaxIntervention3 = tEndIntenseIntervention + numWeekStagger[3]*7 # for pw = 1 307 | # tStartEndClosure = as.vector(dateEndSchoolClosure - dateStart)+1 308 | pwork = array(1,numSteps) 309 | pwork[1:tRelaxIntervention3] =c(rep(1,(tStartIntenseIntervention-0)), # dont know there is outbreak 310 | rep(pWorkOpen[1],(tEndIntenseIntervention-tStartIntenseIntervention)), 311 | rep(pWorkOpen[2],(tRelaxIntervention1-tEndIntenseIntervention)), 312 | rep(pWorkOpen[3],(tRelaxIntervention2-tRelaxIntervention1)), 313 | rep(pWorkOpen[4],(tRelaxIntervention3-tRelaxIntervention2))) 314 | R0tpostoutbreak = R0t #overwrites the default reduction in R0 post-outbreak 315 | beta = getbeta(R0t = R0t,constraints = constraintsIntervention$base,gamma = gamma,p_age = pop$p_age) 316 | if(pWorkOpen[2]<1) beta_postfirstwave = getbeta(R0t = R0tpostoutbreak,constraints = constraintsIntervention$base,gamma = gamma,p_age = pop$p_age) 317 | if(pWorkOpen[2]>=1) beta_postfirstwave = beta#getbeta(R0t = R0t[2],constraints = constraintsIntervention$base,gamma = gamma,p_age = pop$p_age) 318 | for (stepIndex in 1: (numSteps-1)) 319 | { 320 | 321 | # load plausible intervetions 322 | constraintsIntervention = loadInterventions(p_workopen = pwork[stepIndex]) 323 | 324 | ## Age- and location-specific contact rates for the given interventions 325 | 326 | # I0: before school winter break intervention period, use base-case 327 | if(time[stepIndex] < tStartSchoolClosure) 328 | { 329 | CONSTRAINT = constraintsIntervention$base 330 | } 331 | # I1: When school winter break but before lockdown period, use 'schcloseonly' 332 | if(time[stepIndex] >= tStartSchoolClosure & time[stepIndex] < tStartIntenseIntervention) 333 | { 334 | INTERVENTION = "schcloseonly" 335 | CONSTRAINT = constraintsIntervention[[INTERVENTION]] 336 | } 337 | # I2: Intense intervention 338 | if(time[stepIndex] >= tStartIntenseIntervention & time[stepIndex] < tRelaxIntervention3) 339 | { 340 | INTERVENTION = "schcloseworkplacedist" 341 | CONSTRAINT = constraintsIntervention[[INTERVENTION]] 342 | } 343 | # I3: post outbreak 344 | if(time[stepIndex] >= tRelaxIntervention3) 345 | { 346 | CONSTRAINT = constraintsIntervention$postoutbreak 347 | } 348 | # 349 | 350 | C = CONSTRAINT[[1]]%*%contacts_china[[1]]+ 351 | CONSTRAINT[[2]]%*%contacts_china[[2]]+ 352 | CONSTRAINT[[3]]%*%contacts_china[[3]]+ 353 | CONSTRAINT[[4]]%*%contacts_china[[4]] 354 | 355 | # calculate the force of infection 356 | 357 | # beta = getbeta(R0t = R0t[stepIndex],constraints = constraintsIntervention$base,gamma = gamma,p_age = pop$p_age) 358 | if(time[stepIndex] < tEndIntenseIntervention+0) lambda[stepIndex,] = as.numeric(beta)*(as.matrix(C)%*%(as.matrix(Ic[stepIndex,]/N_age) + 0.25*as.matrix(Isc[stepIndex,]/N_age))); 359 | if(time[stepIndex] >= tEndIntenseIntervention+0)lambda[stepIndex,] = as.numeric(beta_postfirstwave)*(as.matrix(C)%*%(as.matrix(Ic[stepIndex,]/N_age) + 0.25*as.matrix(Isc[stepIndex,]/N_age))); 360 | # calculate the number of infections and recoveries between time t and t+dt 361 | 362 | numStoE = lambda[stepIndex,]*S[stepIndex,]*dt; # S to E 363 | numEtoIc = alpha*rho*E[stepIndex,]*dt; # E to Ic 364 | numEtoIsc = alpha*(1-rho)*E[stepIndex,]*dt; # E to Isc 365 | numIctoR = gamma*Ic[stepIndex,]*dt; # Ic to R 366 | numIsctoR = gamma*Isc[stepIndex,]*dt; # Isc to R 367 | 368 | # Difference equations 369 | S[stepIndex+1,] = S[stepIndex,]-numStoE; 370 | E[stepIndex+1,] = E[stepIndex,]+numStoE-numEtoIc-numEtoIsc; 371 | Ic[stepIndex+1,] = Ic[stepIndex,]+numEtoIc-numIctoR; 372 | Isc[stepIndex+1,] = Isc[stepIndex,]+numEtoIsc-numIsctoR; 373 | R[stepIndex+1,] = R[stepIndex,]+numIctoR+numIsctoR; 374 | 375 | incidence[stepIndex+1,] = numEtoIc/dt; 376 | subclinical[stepIndex+1,] = numEtoIsc/dt; 377 | time[stepIndex+1] = time[stepIndex]+dt; 378 | 379 | 380 | } 381 | output = list(S = S, E = E, Ic = Ic, Isc = Isc, R = R, time = time, lambda=lambda, 382 | incidence = incidence, N_age= N_age, subclinical = subclinical, 383 | R0t = R0t,#rho = rho, 384 | dateStart = dateStart, dateEnd = dateEnd, 385 | dateStartIntenseIntervention = dateStartIntenseIntervention, dateEndIntenseIntervention = dateEndIntenseIntervention, 386 | dateStartSchoolClosure = dateStartSchoolClosure, dateStartCNY = dateStartCNY,dateEndCNY = dateEndCNY) 387 | return(output) 388 | } 389 | 390 | 391 | CHECKMODEL = FALSE 392 | 393 | if(CHECKMODEL) 394 | { 395 | # Quick checks: Simulate an outbreak for sanity checks 396 | set.seed(666) 397 | 398 | 399 | # test for an R0 value of 2.2 400 | R0est = 2.2 401 | # R0est = sample(x = r0posterior,size = 100) 402 | 403 | nsim = 1 404 | epi_doNothing = vector('list',nsim) 405 | epi_base = vector('list',nsim) 406 | epi_march = vector('list',nsim) 407 | epi_april = vector('list',nsim) 408 | 409 | for(sim in 1:nsim) 410 | { 411 | epi_doNothing[[sim]] = simulateOutbreakSEIR(R0t =R0est,rho = rep(0.5,3660),dateStartSchoolClosure = as.Date('2019-12-01'), 412 | dateStartIntenseIntervention =as.Date('2019-12-01'), 413 | dateEndIntenseIntervention = as.Date('2019-12-01'), 414 | pWorkOpen = c(1,1,1,1),numWeekStagger = c(0,0,0,0),durInf = 7) 415 | epi_base[[sim]] = simulateOutbreakSEIR(R0t =R0est ,rho = rep(0.5,3660),dateEndIntenseIntervention = as.Date('2020-01-27'), 416 | pWorkOpen = c(0.5,1,1,1),numWeekStagger = c(0,0,0,0),durInf = 7) 417 | epi_march[[sim]] = simulateOutbreakSEIR(R0t =R0est,rho = rep(0.5,3660), dateEndIntenseIntervention = as.Date('2020-03-01'),durInf = 7) 418 | epi_april[[sim]] = simulateOutbreakSEIR(R0t =R0est,rho = rep(0.5,3660), dateEndIntenseIntervention = as.Date('2020-04-01'),durInf = 7) 419 | # epi_doNothing[[sim]] = simulateOutbreakSEIcIscR(R0t =R0est,dateStartSchoolClosure = as.Date('2019-12-01'), 420 | # dateStartIntenseIntervention =as.Date('2019-12-01'), 421 | # dateEndIntenseIntervention = as.Date('2019-12-01'), 422 | # pWorkOpen = c(1,1,1,1),numWeekStagger = c(0,0,0,0),durInf = 14) 423 | # epi_base[[sim]] = simulateOutbreakSEIcIscR(R0t =R0est ,dateEndIntenseIntervention = as.Date('2020-01-27'), 424 | # pWorkOpen = c(0.5,1,1,1),numWeekStagger = c(0,0,0,0),durInf = 14) 425 | # epi_march[[sim]] = simulateOutbreakSEIcIscR(R0t =R0est, dateEndIntenseIntervention = as.Date('2020-03-01'),durInf = 14) 426 | # epi_april[[sim]] = simulateOutbreakSEIcIscR(R0t =R0est, dateEndIntenseIntervention = as.Date('2020-04-01'),durInf = 14) 427 | } 428 | par(mfrow=c(2,1)) 429 | 430 | # incidence over time 431 | agegp =3 432 | plot(epi_doNothing[[1]]$time, epi_doNothing[[1]]$incidence[,agegp], type='l', lwd=2, 433 | main=paste0("Incidence for age [",(agegp-1)*5,',',agegp*5,')'), 434 | xlab="Time(days)", ylab="Daily no. of infections"); 435 | lines(x=epi_base[[1]]$time,y=epi_base[[1]]$incidence[,agegp],lwd=2,col='grey40') 436 | lines(x=epi_march[[1]]$time,y=epi_march[[1]]$incidence[,agegp],lwd=2,col='steelblue') 437 | lines(x=epi_april[[1]]$time,y=epi_april[[1]]$incidence[,agegp],lwd=2,col='tomato',lty='dashed') 438 | 439 | # cumulative incidence over time 440 | plot(epi_doNothing[[1]]$time, (epi_doNothing[[1]]$N_age[agegp]-epi_doNothing[[1]]$S[,agegp])/epi_doNothing[[1]]$N_age[agegp], lwd=2,type='l', 441 | main=paste0("Cum incidence for age [",(agegp-1)*5,',',agegp*5,')'), 442 | xlab="Time(days)", ylab="Cum incidence",ylim = c(0,1)); 443 | lines(epi_base[[1]]$time, (epi_base[[1]]$N_age[agegp]-epi_base[[1]]$S[,agegp])/epi_base[[1]]$N_age[agegp],lwd=2,col='grey40') 444 | lines(epi_march[[1]]$time, (epi_march[[1]]$N_age[agegp]-epi_march[[1]]$S[,agegp])/epi_march[[1]]$N_age[agegp],lwd=2,col='steelblue') 445 | lines(epi_april[[1]]$time, (epi_april[[1]]$N_age[agegp]-epi_april[[1]]$S[,agegp])/epi_april[[1]]$N_age[agegp],lwd=2,col='tomato',lty='dashed') 446 | legend(0.25, 0.98, legend=c("Do Nothing", "Base","Lockdown->March","Lockdown->April"), 447 | col=c("black", "grey40","steelblue",'tomato'), bty='n',lty=c(1,1,1,1),lwd=c(2,2,2,2), cex=0.7) 448 | 449 | # incidence over time 450 | agegp =13 451 | plot(epi_doNothing[[1]]$time, epi_doNothing[[1]]$incidence[,agegp], type='l', lwd=2, 452 | main=paste0("Incidence for age [",(agegp-1)*5,',',agegp*5,')'), 453 | xlab="Time(days)", ylab="Daily no. of infections"); 454 | lines(x=epi_base[[1]]$time,y=epi_base[[1]]$incidence[,agegp],lwd=2,col='grey40') 455 | lines(x=epi_march[[1]]$time,y=epi_march[[1]]$incidence[,agegp],lwd=2,col='steelblue') 456 | lines(x=epi_april[[1]]$time,y=epi_april[[1]]$incidence[,agegp],lwd=2,col='tomato',lty='dashed') 457 | 458 | # cumulative incidence over time 459 | plot(epi_doNothing[[1]]$time, (epi_doNothing[[1]]$N_age[agegp]-epi_doNothing[[1]]$S[,agegp])/epi_doNothing[[1]]$N_age[agegp], lwd=2,type='l', 460 | main=paste0("Cum incidence for age [",(agegp-1)*5,',',agegp*5,')'), 461 | xlab="Time(days)", ylab="Cum incidence",ylim = c(0,1)); 462 | lines(epi_base[[1]]$time, (epi_base[[1]]$N_age[agegp]-epi_base[[1]]$S[,agegp])/epi_base[[1]]$N_age[agegp],lwd=2,col='grey40') 463 | lines(epi_march[[1]]$time, (epi_march[[1]]$N_age[agegp]-epi_march[[1]]$S[,agegp])/epi_march[[1]]$N_age[agegp],lwd=2,col='steelblue') 464 | lines(epi_april[[1]]$time, (epi_april[[1]]$N_age[agegp]-epi_april[[1]]$S[,agegp])/epi_april[[1]]$N_age[agegp],lwd=2,col='tomato',lty='dashed') 465 | legend(0.25, 0.98, legend=c("Do Nothing", "Base","Lockdown->March","Lockdown->April"), 466 | col=c("black", "grey40","steelblue",'tomato'), bty='n',lty=c(1,1,1,1),lwd=c(2,2,2,2), cex=0.7) 467 | 468 | } 469 | -------------------------------------------------------------------------------- /codes/function_postprocessing.r: -------------------------------------------------------------------------------- 1 | summariseSimulations = function(VAR,CI,SIMS) 2 | { 3 | temp = lapply(SIMS,FUN = function(x) rowSums(x[[VAR]])) 4 | temp1 = do.call(cbind.data.frame, temp) 5 | var_p_median = apply(temp1,1,function(x) quantile(x,0.5)) 6 | var_p_lci = apply(temp1,1,function(x) quantile(x,(1-CI/100)/2)) 7 | var_p_uci = apply(temp1,1,function(x) quantile(x,1-(1-CI/100)/2)) 8 | SUMMARY = list(median = var_p_median,lci = var_p_lci,uci=var_p_uci) 9 | rm(temp,temp1,var_p_median,var_p_lci,var_p_uci) 10 | 11 | 12 | results = list(summary=SUMMARY, Sim1 = SIMS[[1]]) 13 | return(results) 14 | 15 | } 16 | 17 | summarisePeakTimePeakSize= function(VAR = 'incidence',SIMS) 18 | { 19 | time = SIMS[[1]]$time 20 | temp = lapply(SIMS,FUN = function(x) rowSums(x[[VAR]])) 21 | temp1 = do.call(cbind.data.frame, temp) 22 | peaksize = as.numeric(apply(temp1,2,max)) 23 | peaktime = time[as.numeric(apply(temp1,2,function(x) which.max(x)))] 24 | results = list(time = time, peaktime = peaktime,peaksize = peaksize) 25 | return(results) 26 | } 27 | 28 | 29 | summariseSimulations_mid = function(CI,SIMS) 30 | { 31 | temp = lapply(SIMS,FUN = function(x) rowSums(x[['S']])) 32 | temp1 = do.call(cbind.data.frame, temp) 33 | i = which.min(abs(as.numeric(temp1[nrow(temp1),])-as.numeric(quantile(temp1[nrow(temp1),],0.5)))) 34 | j = which.min(abs(as.numeric(temp1[nrow(temp1),])-as.numeric(quantile(temp1[nrow(temp1),],0.25)))) 35 | k = which.min(abs(as.numeric(temp1[nrow(temp1),])-as.numeric(quantile(temp1[nrow(temp1),],0.75)))) 36 | 37 | S = data.frame(med = temp[[i]],lci = temp[[k]],uci = temp[[j]],time = SIMS[[1]]$time) 38 | S_age = list(med = SIMS[[i]]$S,lci = SIMS[[k]]$S,uci = SIMS[[j]]$S) 39 | inc = list(med = SIMS[[i]]$incidence,lci = SIMS[[k]]$incidence,uci = SIMS[[j]]$incidence) 40 | time = SIMS[[1]]$time 41 | N_age = SIMS[[1]]$N_age 42 | results = list(S=S,inc = inc, time = time,N_age=N_age,S_age = S_age) 43 | return(results) 44 | } 45 | 46 | summariseSimulationsAGE = function(VAR,CI,SIMS) 47 | { 48 | var_p_median = var_p_lci = var_p_uci = array(NA,c(length(SIMS[[1]]$time),16)) 49 | for(age in 1:16) 50 | { 51 | temp = lapply(SIMS,FUN = function(x) (x[[VAR]][,age])) 52 | temp1 = do.call(cbind.data.frame, temp) 53 | var_p_median[,age] = apply(temp1,1,function(x) quantile(x,0.5)) 54 | var_p_lci[,age] = apply(temp1,1,function(x) quantile(x,(1-CI/100)/2)) 55 | var_p_uci[,age] = apply(temp1,1,function(x) quantile(x,1-(1-CI/100)/2)) 56 | rm(temp,temp1) 57 | } 58 | 59 | SUMMARY = list(median = var_p_median,lci = var_p_lci,uci=var_p_uci) 60 | rm(var_p_median,var_p_lci,var_p_uci) 61 | 62 | 63 | results = list(summary=SUMMARY, Sim1 = SIMS[[1]]) 64 | return(results) 65 | 66 | } -------------------------------------------------------------------------------- /data/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kieshaprem/covid19-agestructureSEIR-wuhan-social-distancing/092dded2cc45967f665a3f0d3f3e41f6e5a3f6b6/data/.DS_Store -------------------------------------------------------------------------------- /data/china_pop_age_structure.csv: -------------------------------------------------------------------------------- 1 | agegroup,males,propmales,females,propfemales,popage,propage 2 | 1,44456332.00,0.53,39476105.00,0.47,83932437.00,0.06 3 | 2,46320144.00,0.53,40415039.00,0.47,86735183.00,0.06 4 | 3,45349923.00,0.54,38912828.00,0.46,84262751.00,0.06 5 | 4,44103122.00,0.54,38238737.00,0.46,82341859.00,0.06 6 | 5,46273865.00,0.53,40884302.00,0.47,87158167.00,0.06 7 | 6,51522843.00,0.53,46466160.00,0.47,97989003.00,0.07 8 | 7,66443228.00,0.52,62295742.00,0.48,128738970.00,0.09 9 | 8,51345507.00,0.51,48745948.00,0.49,100091455.00,0.07 10 | 9,49289359.00,0.51,46984787.00,0.49,96274146.00,0.07 11 | 10,61173349.00,0.51,58664268.00,0.49,119837617.00,0.08 12 | 11,62348020.00,0.51,61097362.00,0.49,123445382.00,0.09 13 | 12,49958045.00,0.51,48782446.00,0.49,98740491.00,0.07 14 | 13,38917285.00,0.50,38596854.00,0.50,77514139.00,0.05 15 | 14,36526788.00,0.49,37622978.00,0.51,74149766.00,0.05 16 | 15,21425163.00,0.48,23524526.00,0.52,44949689.00,0.03 17 | 16,12207276.00,0.46,14337340.00,0.54,26544616.00,0.02 18 | 17,6883629.00,0.43,9297788.00,0.57,16181417.00,0.01 19 | 18,2843084.00,0.38,4738693.00,0.63,7581777.00,0.01 20 | 19,731228.00,0.32,1573796.00,0.68,2305024.00,0.00 21 | 20,116377.00,0.24,358816.00,0.76,475193.00,0.00 22 | 21,12773.00,0.17,61919.00,0.83,74692.00,0.00 -------------------------------------------------------------------------------- /data/contacts.Rdata: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kieshaprem/covid19-agestructureSEIR-wuhan-social-distancing/092dded2cc45967f665a3f0d3f3e41f6e5a3f6b6/data/contacts.Rdata -------------------------------------------------------------------------------- /data/contacts_china.rdata: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kieshaprem/covid19-agestructureSEIR-wuhan-social-distancing/092dded2cc45967f665a3f0d3f3e41f6e5a3f6b6/data/contacts_china.rdata -------------------------------------------------------------------------------- /data/out_date.csv: -------------------------------------------------------------------------------- 1 | value 2 | 2019-11-22 3 | 2019-11-23 4 | 2019-11-24 5 | 2019-11-25 6 | 2019-11-26 7 | 2019-11-27 8 | 2019-11-28 9 | 2019-11-29 10 | 2019-11-30 11 | 2019-12-01 12 | 2019-12-02 13 | 2019-12-03 14 | 2019-12-04 15 | 2019-12-05 16 | 2019-12-06 17 | 2019-12-07 18 | 2019-12-08 19 | 2019-12-09 20 | 2019-12-10 21 | 2019-12-11 22 | 2019-12-12 23 | 2019-12-13 24 | 2019-12-14 25 | 2019-12-15 26 | 2019-12-16 27 | 2019-12-17 28 | 2019-12-18 29 | 2019-12-19 30 | 2019-12-20 31 | 2019-12-21 32 | 2019-12-22 33 | 2019-12-23 34 | 2019-12-24 35 | 2019-12-25 36 | 2019-12-26 37 | 2019-12-27 38 | 2019-12-28 39 | 2019-12-29 40 | 2019-12-30 41 | 2019-12-31 42 | 2020-01-01 43 | 2020-01-02 44 | 2020-01-03 45 | 2020-01-04 46 | 2020-01-05 47 | 2020-01-06 48 | 2020-01-07 49 | 2020-01-08 50 | 2020-01-09 51 | 2020-01-10 52 | 2020-01-11 53 | 2020-01-12 54 | 2020-01-13 55 | 2020-01-14 56 | 2020-01-15 57 | 2020-01-16 58 | 2020-01-17 59 | 2020-01-18 60 | 2020-01-19 61 | 2020-01-20 62 | 2020-01-21 63 | 2020-01-22 64 | 2020-01-23 65 | 2020-01-24 66 | 2020-01-25 67 | 2020-01-26 68 | 2020-01-27 69 | 2020-01-28 70 | 2020-01-29 71 | 2020-01-30 72 | 2020-01-31 73 | 2020-02-01 74 | 2020-02-02 75 | 2020-02-03 76 | 2020-02-04 77 | 2020-02-05 78 | 2020-02-06 79 | 2020-02-07 80 | 2020-02-08 81 | 2020-02-09 82 | 2020-02-10 83 | 2020-02-11 84 | -------------------------------------------------------------------------------- /data/wuhan_pop_case_dist.csv: -------------------------------------------------------------------------------- 1 | bin_start,bin_end,national,wuhan 2 | 0,9,0.006,0.002 3 | 10,19,0.014,0.003 4 | 20,29,0.118,0.064 5 | 30,39,0.206,0.163 6 | 40,49,0.205,0.148 7 | 50,59,0.214,0.221 8 | 60,69,0.152,0.231 9 | 70,79,0.066,0.123 10 | 80,89,0.018,0.042 11 | 90,99,0.001,0.004 -------------------------------------------------------------------------------- /data/wuhanpop.csv: -------------------------------------------------------------------------------- 1 | "agegroup","popage","propage" 2 | 1,407903,0.0378873697311958 3 | 2,282264,0.0262176069551002 4 | 3,817453,0.0759277182292731 5 | 4,1430272,0.132848358752392 6 | 5,896025,0.0832257435306794 7 | 6,842295,0.0782351247422489 8 | 7,701197,0.0651294792963163 9 | 8,864953,0.0803396741654437 10 | 9,1049332,0.0974654009771321 11 | 10,431113,0.040043190726533 12 | 11,691750,0.0642520109230741 13 | 12,726228,0.0674544407497539 14 | 13,568724,0.0528249521651093 15 | 14,352535,0.0327446081254296 16 | 15,273800,0.0254314428489161 17 | 16,430356,0.0399728780814029 18 | -------------------------------------------------------------------------------- /outputs/.DS_Store: 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