From 2a7762b5753ee5f5ff36c6a81f5cd96292764d00 Mon Sep 17 00:00:00 2001 From: Swapnil Mishra Date: Fri, 24 Apr 2020 16:54:13 +0000 Subject: [PATCH 1/5] nature submission mirror with refactoring --- base.r | 221 +- covariate-size-effects.r | 2 +- data/COVID-19-up-to-date.csv | 22725 +++++++++++++++++++-------------- data/COVID-19-up-to-date.rds | Bin 50718 -> 64956 bytes data/regions.csv | 15 + make-table.r | 2 +- plot-3-panel.r | 21 +- plot-forecast.r | 9 +- stan-models/base.stan | 67 +- utils/process-covariates.r | 132 + utils/read-data.r | 42 + 11 files changed, 13080 insertions(+), 10156 deletions(-) create mode 100644 data/regions.csv create mode 100644 utils/process-covariates.r create mode 100644 utils/read-data.r diff --git a/base.r b/base.r index 569c9603..3eb25ea7 100644 --- a/base.r +++ b/base.r @@ -7,22 +7,8 @@ library(tidyr) library(EnvStats) library(optparse) -countries <- c( - "Denmark", - "Italy", - "Germany", - "Spain", - "United_Kingdom", - "France", - "Norway", - "Belgium", - "Austria", - "Sweden", - "Switzerland", - "Greece", - "Portugal", - "Netherlands" -) +source('utils/read-data.r') +source('utils/process-covariates.r') # Commandline options and parsing parser <- OptionParser() @@ -54,7 +40,7 @@ if(length(cmdoptions$args) == 0) { } else { StanModel = cmdoptions$args[1] } - + print(sprintf("Running %s",StanModel)) if(DEBUG) { print("Running in DEBUG mode") @@ -62,190 +48,36 @@ if(DEBUG) { print("Running in FULL mode") } -## Reading all data -d=readRDS('data/COVID-19-up-to-date.rds') - -## Ensure that output directories exist -dir.create("results/", showWarnings = FALSE, recursive = TRUE) -dir.create("figures/", showWarnings = FALSE, recursive = TRUE) -dir.create("web/", showWarnings = FALSE, recursive = TRUE) - -## get IFR and population from same file -ifr.by.country = read.csv("data/popt_ifr.csv") -ifr.by.country$country = as.character(ifr.by.country[,2]) -ifr.by.country$country[ifr.by.country$country == "United Kingdom"] = "United_Kingdom" +# Read which countires to use +countries <- read.csv('data/regions.csv', stringsAsFactors = FALSE) +# Read deaths data for regions +d <- read_obs_data(countries) +# Read ifr +ifr.by.country <- read_ifr_data() -serial.interval = read.csv("data/serial_interval.csv") -covariates = read.csv('data/interventions.csv', stringsAsFactors = FALSE) -names_covariates = c('Schools + Universities','Self-isolating if ill', 'Public events', 'Lockdown', 'Social distancing encouraged') -covariates <- covariates %>% - filter((Type %in% names_covariates)) -covariates <- covariates[,c(1,2,4)] -covariates <- spread(covariates, Type, Date.effective) -names(covariates) <- c('Country','lockdown', 'public_events', 'schools_universities','self_isolating_if_ill', 'social_distancing_encouraged') -covariates <- covariates[c('Country','schools_universities', 'self_isolating_if_ill', 'public_events', 'lockdown', 'social_distancing_encouraged')] -covariates$schools_universities <- as.Date(covariates$schools_universities, format = "%d.%m.%Y") -covariates$lockdown <- as.Date(covariates$lockdown, format = "%d.%m.%Y") -covariates$public_events <- as.Date(covariates$public_events, format = "%d.%m.%Y") -covariates$self_isolating_if_ill <- as.Date(covariates$self_isolating_if_ill, format = "%d.%m.%Y") -covariates$social_distancing_encouraged <- as.Date(covariates$social_distancing_encouraged, format = "%d.%m.%Y") -## using covariates as dates we want -covariates$schools_universities[covariates$schools_universities > covariates$lockdown] <- covariates$lockdown[covariates$schools_universities > covariates$lockdown] -covariates$public_events[covariates$public_events > covariates$lockdown] <- covariates$lockdown[covariates$public_events > covariates$lockdown] -covariates$social_distancing_encouraged[covariates$social_distancing_encouraged > covariates$lockdown] <- covariates$lockdown[covariates$social_distancing_encouraged > covariates$lockdown] -covariates$self_isolating_if_ill[covariates$self_isolating_if_ill > covariates$lockdown] <- covariates$lockdown[covariates$self_isolating_if_ill > covariates$lockdown] +# Read interventions +interventions <- read_interventions(countries) -forecast = 0 +N2 <- 100 # increase if you need more forecast -N2 = 120 # increase if you need more forecast - -dates = list() -reported_cases = list() -# Pads serial interval with 0 if N2 is greater than the length of the serial -# interval array -if (N2 > length(serial.interval$fit)) { - pad_serial.interval <- data.frame( - "X"=(length(serial.interval$fit)+1):N2, - "fit"=rep(0.0, max(N2-length(serial.interval$fit), 0 )) - ) - serial.interval = rbind(serial.interval, pad_serial.interval) -} -stan_data = list(M=length(countries),N=NULL,covariate1=NULL,covariate2=NULL,covariate3=NULL,covariate4=NULL,covariate5=NULL,covariate6=NULL,deaths=NULL,f=NULL, - N0=6,cases=NULL,SI=serial.interval$fit[1:N2], - EpidemicStart = NULL, pop = NULL) # N0 = 6 to make it consistent with Rayleigh -deaths_by_country = list() - -# various distributions required for modeling -mean1 = 5.1; cv1 = 0.86; # infection to onset -mean2 = 18.8; cv2 = 0.45 # onset to death -x1 = rgammaAlt(1e7,mean1,cv1) # infection-to-onset distribution -x2 = rgammaAlt(1e7,mean2,cv2) # onset-to-death distribution - -ecdf.saved = ecdf(x1+x2) - -for(Country in countries) { - IFR=ifr.by.country$ifr[ifr.by.country$country == Country] - - covariates1 <- covariates[covariates$Country == Country, c(2,3,4,5,6)] - - d1_pop = ifr.by.country[ifr.by.country$country==Country,] - d1=d[d$Countries.and.territories==Country,c(1,5,6,7)] - d1$date = as.Date(d1$DateRep,format='%d/%m/%Y') - d1$t = decimal_date(d1$date) - d1=d1[order(d1$t),] - - date_min <- dmy('31/12/2019') - if (as.Date(d1$DateRep[1], format='%d/%m/%Y') > as.Date(date_min, format='%d/%m/%Y')){ - print(paste(Country,'In padding')) - pad_days <- as.Date(d1$DateRep[1], format='%d/%m/%Y') - date_min - pad_dates <- date_min + days(1:pad_days[[1]]-1) - padded_data <- data.frame("Countries.and.territories" = rep(Country, pad_days), - "DateRep" = format(pad_dates, '%d/%m/%Y'), - "t" = decimal_date(as.Date(pad_dates,format='%d/%m/%Y')), - "date" = as.Date(pad_dates,format='%d/%m/%Y'), - "Cases" = as.integer(rep(0, pad_days)), - "Deaths" = as.integer(rep(0, pad_days)), - stringsAsFactors=F) - - d1 <- bind_rows(padded_data, d1) - } - index = which(d1$Cases>0)[1] - index1 = which(cumsum(d1$Deaths)>=10)[1] # also 5 - index2 = index1-30 - - print(sprintf("First non-zero cases is on day %d, and 30 days before 10 deaths is day %d",index,index2)) - d1=d1[index2:nrow(d1),] - stan_data$EpidemicStart = c(stan_data$EpidemicStart,index1+1-index2) - stan_data$pop = c(stan_data$pop, d1_pop$popt) - - - for (ii in 1:ncol(covariates1)) { - covariate = names(covariates1)[ii] - d1[covariate] <- (as.Date(d1$DateRep, format='%d/%m/%Y') >= as.Date(covariates1[1,covariate]))*1 # should this be > or >=? - } - - dates[[Country]] = d1$date - # hazard estimation - N = length(d1$Cases) - print(sprintf("%s has %d days of data",Country,N)) - forecast = N2 - N - if(forecast < 0) { - print(sprintf("%s: %d", Country, N)) - print("ERROR!!!! increasing N2") - N2 = N - forecast = N2 - N - } - - # IFR is the overall probability of dying given infection - convolution = function(u) (IFR * ecdf.saved(u)) - - f = rep(0,N2) # f is the probability of dying on day i given infection - f[1] = (convolution(1.5) - convolution(0)) - for(i in 2:N2) { - f[i] = (convolution(i+.5) - convolution(i-.5)) - } - - reported_cases[[Country]] = as.vector(as.numeric(d1$Cases)) - deaths=c(as.vector(as.numeric(d1$Deaths)),rep(-1,forecast)) - cases=c(as.vector(as.numeric(d1$Cases)),rep(-1,forecast)) - deaths_by_country[[Country]] = as.vector(as.numeric(d1$Deaths)) - covariates2 <- as.data.frame(d1[, colnames(covariates1)]) - # x=1:(N+forecast) - covariates2[N:(N+forecast),] <- covariates2[N,] - - ## append data - stan_data$N = c(stan_data$N,N) - # stan_data$x = cbind(stan_data$x,x) - stan_data$covariate1 = cbind(stan_data$covariate1,covariates2[,1]) - stan_data$covariate2 = cbind(stan_data$covariate2,covariates2[,2]) - stan_data$covariate3 = cbind(stan_data$covariate3,covariates2[,3]) - stan_data$covariate4 = cbind(stan_data$covariate4,covariates2[,4]) - stan_data$covariate5 = cbind(stan_data$covariate5,covariates2[,4]) - stan_data$covariate6 = cbind(stan_data$covariate6,covariates2[,5]) - stan_data$f = cbind(stan_data$f,f) - stan_data$deaths = cbind(stan_data$deaths,deaths) - stan_data$cases = cbind(stan_data$cases,cases) - - stan_data$N2=N2 - stan_data$x=1:N2 - if(length(stan_data$N) == 1) { - stan_data$N = as.array(stan_data$N) - } -} - -# create the `any intervention` covariate -stan_data$covariate4 = 1*as.data.frame((stan_data$covariate1+ - stan_data$covariate2+ - stan_data$covariate3+ - stan_data$covariate5+ - stan_data$covariate6) >= 1) - -if(DEBUG) { - for(i in 1:length(countries)) { - write.csv( - data.frame(date=dates[[i]], - `school closure`=stan_data$covariate1[1:stan_data$N[i],i], - `self isolating if ill`=stan_data$covariate2[1:stan_data$N[i],i], - `public events`=stan_data$covariate3[1:stan_data$N[i],i], - `government makes any intervention`=stan_data$covariate4[1:stan_data$N[i],i], - `lockdown`=stan_data$covariate5[1:stan_data$N[i],i], - `social distancing encouraged`=stan_data$covariate6[1:stan_data$N[i],i]), - file=sprintf("results/%s-check-dates.csv",countries[i]),row.names=F) - } -} +processed_data <- process_covariates(countries = countries, interventions = interventions, + d = d , ifr.by.country = ifr.by.country, N2 = N2) +stan_data = processed_data$stan_data +dates = processed_data$dates +deaths_by_country = processed_data$deaths_by_country +reported_cases = processed_data$reported_cases options(mc.cores = parallel::detectCores()) rstan_options(auto_write = TRUE) m = stan_model(paste0('stan-models/',StanModel,'.stan')) - if(DEBUG) { fit = sampling(m,data=stan_data,iter=40,warmup=20,chains=2) } else if (FULL) { - fit = sampling(m,data=stan_data,iter=4000,warmup=2000,chains=4,thin=4,control = list(adapt_delta = 0.95, max_treedepth = 10)) + fit = sampling(m,data=stan_data,iter=1800,warmup=1000,chains=5,thin=1,control = list(adapt_delta = 0.95, max_treedepth = 15)) } else { - fit = sampling(m,data=stan_data,iter=200,warmup=100,chains=4,thin=4,control = list(adapt_delta = 0.95, max_treedepth = 10)) -} + fit = sampling(m,data=stan_data,iter=1000,warmup=500,chains=4,thin=1,control = list(adapt_delta = 0.95, max_treedepth = 10)) +} out = rstan::extract(fit) prediction = out$prediction @@ -257,9 +89,13 @@ if(JOBID == "") JOBID = as.character(abs(round(rnorm(1) * 1000000))) print(sprintf("Jobid = %s",JOBID)) -save.image(paste0('results/',StanModel,'-',JOBID,'.Rdata')) +countries <- countries$Regions +save(fit,prediction,dates,reported_cases,deaths_by_country,countries,estimated.deaths,estimated.deaths.cf,out,file=paste0('results/',StanModel,'-',JOBID,'-stanfit.Rdata')) -save(fit,prediction,dates,reported_cases,deaths_by_country,countries,estimated.deaths,estimated.deaths.cf,out,covariates,file=paste0('results/',StanModel,'-',JOBID,'-stanfit.Rdata')) +## Ensure that output directories exist +dir.create("results/", showWarnings = FALSE, recursive = TRUE) +dir.create("figures/", showWarnings = FALSE, recursive = TRUE) +dir.create("web/", showWarnings = FALSE, recursive = TRUE) library(bayesplot) filename <- paste0(StanModel,'-',JOBID) @@ -298,7 +134,8 @@ if(make_table_error != 0){ stop(sprintf("Generation of alpha covar table failed! Code: %d", make_table_error)) } + verify_result_error <- system(paste0("Rscript web-verify-output.r ", filename,'.Rdata'),intern=FALSE) if(verify_result_error != 0){ stop(sprintf("Verification of web output failed! Code: %d", verify_result_error)) -} +} \ No newline at end of file diff --git a/covariate-size-effects.r b/covariate-size-effects.r index 567796f2..9ff1a034 100644 --- a/covariate-size-effects.r +++ b/covariate-size-effects.r @@ -15,7 +15,7 @@ plot_labels <- c("School Closure", "Self Isolation", "Public Events", "First Intervention", - "Lockdown", 'Social distancing') + "Lockdown", 'Social distancing \n encouraged') colnames(alpha) = plot_labels first.intervention = alpha[,c(1,2,3,5,6)] + alpha[,4] data1 = mcmc_intervals_data(first.intervention,prob=.95,transformation=function(x) 1-exp(-x),point_est="mean") diff --git a/data/COVID-19-up-to-date.csv b/data/COVID-19-up-to-date.csv index eade526a..138186f2 100644 --- a/data/COVID-19-up-to-date.csv +++ b/data/COVID-19-up-to-date.csv @@ -1,9923 +1,12802 @@ -dateRep,day,month,year,cases,deaths,countriesAndTerritories,geoId,countryterritoryCode,popData2018 -10/04/2020,10,4,2020,61,1,Afghanistan,AF,AFG,37172386 -09/04/2020,9,4,2020,56,3,Afghanistan,AF,AFG,37172386 -08/04/2020,8,4,2020,30,4,Afghanistan,AF,AFG,37172386 -07/04/2020,7,4,2020,38,0,Afghanistan,AF,AFG,37172386 -06/04/2020,6,4,2020,29,2,Afghanistan,AF,AFG,37172386 -05/04/2020,5,4,2020,35,1,Afghanistan,AF,AFG,37172386 -04/04/2020,4,4,2020,0,0,Afghanistan,AF,AFG,37172386 -03/04/2020,3,4,2020,43,0,Afghanistan,AF,AFG,37172386 -02/04/2020,2,4,2020,26,0,Afghanistan,AF,AFG,37172386 -01/04/2020,1,4,2020,25,0,Afghanistan,AF,AFG,37172386 -31/03/2020,31,3,2020,27,0,Afghanistan,AF,AFG,37172386 -30/03/2020,30,3,2020,8,1,Afghanistan,AF,AFG,37172386 -29/03/2020,29,3,2020,15,1,Afghanistan,AF,AFG,37172386 -28/03/2020,28,3,2020,16,1,Afghanistan,AF,AFG,37172386 -27/03/2020,27,3,2020,0,0,Afghanistan,AF,AFG,37172386 -26/03/2020,26,3,2020,33,0,Afghanistan,AF,AFG,37172386 -25/03/2020,25,3,2020,2,0,Afghanistan,AF,AFG,37172386 -24/03/2020,24,3,2020,6,1,Afghanistan,AF,AFG,37172386 -23/03/2020,23,3,2020,10,0,Afghanistan,AF,AFG,37172386 -22/03/2020,22,3,2020,0,0,Afghanistan,AF,AFG,37172386 -21/03/2020,21,3,2020,2,0,Afghanistan,AF,AFG,37172386 -20/03/2020,20,3,2020,0,0,Afghanistan,AF,AFG,37172386 -19/03/2020,19,3,2020,0,0,Afghanistan,AF,AFG,37172386 -18/03/2020,18,3,2020,1,0,Afghanistan,AF,AFG,37172386 -17/03/2020,17,3,2020,5,0,Afghanistan,AF,AFG,37172386 -16/03/2020,16,3,2020,6,0,Afghanistan,AF,AFG,37172386 -15/03/2020,15,3,2020,3,0,Afghanistan,AF,AFG,37172386 -11/03/2020,11,3,2020,3,0,Afghanistan,AF,AFG,37172386 -08/03/2020,8,3,2020,3,0,Afghanistan,AF,AFG,37172386 -02/03/2020,2,3,2020,0,0,Afghanistan,AF,AFG,37172386 -01/03/2020,1,3,2020,0,0,Afghanistan,AF,AFG,37172386 -29/02/2020,29,2,2020,0,0,Afghanistan,AF,AFG,37172386 -28/02/2020,28,2,2020,0,0,Afghanistan,AF,AFG,37172386 -27/02/2020,27,2,2020,0,0,Afghanistan,AF,AFG,37172386 -26/02/2020,26,2,2020,0,0,Afghanistan,AF,AFG,37172386 -25/02/2020,25,2,2020,1,0,Afghanistan,AF,AFG,37172386 -24/02/2020,24,2,2020,0,0,Afghanistan,AF,AFG,37172386 -23/02/2020,23,2,2020,0,0,Afghanistan,AF,AFG,37172386 -22/02/2020,22,2,2020,0,0,Afghanistan,AF,AFG,37172386 -21/02/2020,21,2,2020,0,0,Afghanistan,AF,AFG,37172386 -20/02/2020,20,2,2020,0,0,Afghanistan,AF,AFG,37172386 -19/02/2020,19,2,2020,0,0,Afghanistan,AF,AFG,37172386 -18/02/2020,18,2,2020,0,0,Afghanistan,AF,AFG,37172386 -17/02/2020,17,2,2020,0,0,Afghanistan,AF,AFG,37172386 -16/02/2020,16,2,2020,0,0,Afghanistan,AF,AFG,37172386 -15/02/2020,15,2,2020,0,0,Afghanistan,AF,AFG,37172386 -14/02/2020,14,2,2020,0,0,Afghanistan,AF,AFG,37172386 -13/02/2020,13,2,2020,0,0,Afghanistan,AF,AFG,37172386 -12/02/2020,12,2,2020,0,0,Afghanistan,AF,AFG,37172386 -11/02/2020,11,2,2020,0,0,Afghanistan,AF,AFG,37172386 -10/02/2020,10,2,2020,0,0,Afghanistan,AF,AFG,37172386 -09/02/2020,9,2,2020,0,0,Afghanistan,AF,AFG,37172386 -08/02/2020,8,2,2020,0,0,Afghanistan,AF,AFG,37172386 -07/02/2020,7,2,2020,0,0,Afghanistan,AF,AFG,37172386 -06/02/2020,6,2,2020,0,0,Afghanistan,AF,AFG,37172386 -05/02/2020,5,2,2020,0,0,Afghanistan,AF,AFG,37172386 -04/02/2020,4,2,2020,0,0,Afghanistan,AF,AFG,37172386 -03/02/2020,3,2,2020,0,0,Afghanistan,AF,AFG,37172386 -02/02/2020,2,2,2020,0,0,Afghanistan,AF,AFG,37172386 -01/02/2020,1,2,2020,0,0,Afghanistan,AF,AFG,37172386 -31/01/2020,31,1,2020,0,0,Afghanistan,AF,AFG,37172386 -30/01/2020,30,1,2020,0,0,Afghanistan,AF,AFG,37172386 -29/01/2020,29,1,2020,0,0,Afghanistan,AF,AFG,37172386 -28/01/2020,28,1,2020,0,0,Afghanistan,AF,AFG,37172386 -27/01/2020,27,1,2020,0,0,Afghanistan,AF,AFG,37172386 -26/01/2020,26,1,2020,0,0,Afghanistan,AF,AFG,37172386 -25/01/2020,25,1,2020,0,0,Afghanistan,AF,AFG,37172386 -24/01/2020,24,1,2020,0,0,Afghanistan,AF,AFG,37172386 -23/01/2020,23,1,2020,0,0,Afghanistan,AF,AFG,37172386 -22/01/2020,22,1,2020,0,0,Afghanistan,AF,AFG,37172386 -21/01/2020,21,1,2020,0,0,Afghanistan,AF,AFG,37172386 -20/01/2020,20,1,2020,0,0,Afghanistan,AF,AFG,37172386 -19/01/2020,19,1,2020,0,0,Afghanistan,AF,AFG,37172386 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zGoixv54!KV%{3W&S}RY?W&L^6|GH!T_N&TJAXj;wV8&3T#6Q1+&ksc4r^J)%cC5#ud;97u{;yg!>cDF95bwLlU&sD#&k-*;nrLsR1cT|00pI1 zw+)W{`QdY9r@pyJK5LKPTbk4XAEB>0~?M8DL=eCkcB%z{_)n@z0w(ZgJ_)Ygwd1p8}qFjm2#|*>#wv_RjHN{zkG?Tbi9YA@Ikf z2?V|GVgTu6o%i@C88YVTt0WPCFvh;rft!W*`?aN8c$^~!$v*C*?$F%%ENoIz3*rc%uy!3s3?c>D$xa^~tdiCr4 zIU!v&6GmTmc+P*fVHMl4jU@FhkxQZ^gQ?0lUPX*HT*LBzBcPgNSP?18=fC62$)9c@ zzuR1_?3c*1j!Wdho~Axmc{gWoF4WGI)fXatv2JjP+cLD&Sv3f?x+OaL M; // number of countries + int P; // number of covariates int N0; // number of days for which to impute infections int N[M]; // days of observed data for country m. each entry must be <= N2 int N2; // days of observed data + # of days to forecast int cases[N2,M]; // reported cases int deaths[N2, M]; // reported deaths -- the rows with i > N contain -1 and should be ignored matrix[N2, M] f; // h * s - matrix[N2, M] covariate1; - matrix[N2, M] covariate2; - matrix[N2, M] covariate3; - matrix[N2, M] covariate4; - matrix[N2, M] covariate5; - matrix[N2, M] covariate6; + matrix[N2, P] X[M]; // features matrix int EpidemicStart[M]; real pop[M]; real SI[N2]; // fixed pre-calculated SI using emprical data from Neil } +transformed data { + vector[N2] SI_rev; // SI in reverse order + vector[N2] f_rev[M]; // f in reversed order + + for(i in 1:N2) + SI_rev[i] = SI[N2-i+1]; + + for(m in 1:M){ + for(i in 1:N2) { + f_rev[m, i] = f[N2-i+1,m]; + } + } +} + + parameters { real mu[M]; // intercept for Rt - real alpha_hier[6]; // sudo parameter for the hier term for alpha + real alpha_hier[P]; // sudo parameter for the hier term for alpha + real gamma; + vector[M] lockdown; real kappa; real y[M]; real phi; @@ -28,7 +41,7 @@ parameters { } transformed parameters { - real alpha[6]; + vector[P] alpha; matrix[N2, M] prediction = rep_matrix(0,N2,M); matrix[N2, M] E_deaths = rep_matrix(0,N2,M); matrix[N2, M] Rt = rep_matrix(0,N2,M); @@ -36,34 +49,24 @@ transformed parameters { { matrix[N2,M] cumm_sum = rep_matrix(0,N2,M); - for(i in 1:6){ + for(i in 1:P){ alpha[i] = alpha_hier[i] - ( log(1.05) / 6.0 ); } for (m in 1:M){ - for (i in 2:N0){ - cumm_sum[i,m] = cumm_sum[i-1,m] + y[m]; - } prediction[1:N0,m] = rep_vector(y[m],N0); // learn the number of cases in the first N0 days + cumm_sum[2:N0,m] = cumulative_sum(prediction[2:N0,m]); - Rt[,m] = mu[m] * exp( covariate1[,m] * (-alpha[1]) + covariate2[,m] * (-alpha[2]) + - covariate3[,m] * (-alpha[3]) + covariate4[,m] * (-alpha[4]) + covariate5[,m] * (-alpha[5]) + - covariate6[,m] * (-alpha[6]) ); - Rt_adj[1:N0,m] = Rt[1:N0,m]; + Rt[,m] = mu[m] * exp(-X[m] * alpha - X[m][,5] * lockdown[m]); + Rt_adj[1:N0,m] = Rt[1:N0,m]; for (i in (N0+1):N2) { - real convolution=0; - for(j in 1:(i-1)) { - convolution += prediction[j, m] * SI[i-j]; - } + real convolution = dot_product(sub_col(prediction, 1, m, i-1), tail(SI_rev, i-1)); cumm_sum[i,m] = cumm_sum[i-1,m] + prediction[i-1,m]; Rt_adj[i,m] = ((pop[m]-cumm_sum[i,m]) / pop[m]) * Rt[i,m]; prediction[i, m] = Rt_adj[i,m] * convolution; } - E_deaths[1, m]= 1e-15 * prediction[1,m]; for (i in 2:N2){ - for(j in 1:(i-1)){ - E_deaths[i,m] += prediction[j,m] * f[i-j,m] * ifr_noise[m]; - } + E_deaths[i,m] = ifr_noise[m] * dot_product(sub_col(prediction, 1, m, i-1), tail(f_rev[m], i-1)); } } } @@ -73,6 +76,8 @@ model { for (m in 1:M){ y[m] ~ exponential(1/tau); } + gamma ~ normal(0,.2); + lockdown ~ normal(0,gamma); phi ~ normal(0,5); kappa ~ normal(0,0.5); mu ~ normal(3.28, kappa); // citation: https://academic.oup.com/jtm/article/27/2/taaa021/5735319 @@ -95,19 +100,13 @@ generated quantities { } prediction0[1:N0,m] = rep_vector(y[m],N0); for (i in (N0+1):N2) { - real convolution0 = 0; - for(j in 1:(i-1)) { - convolution0 += prediction0[j, m] * SI[i-j]; - } + real convolution0 = dot_product(sub_col(prediction0, 1, m, i-1), tail(SI_rev, i-1)); cumm_sum0[i,m] = cumm_sum0[i-1,m] + prediction0[i-1,m]; - prediction0[i, m] = ((pop[m]-cumm_sum0[i,m]) / pop[m]) * mu[m] * convolution0; + prediction0[i, m] = ((pop[m]-cumm_sum0[i,m]) / pop[m]) * mu[m] * convolution0; } - - E_deaths0[1, m] = uniform_rng(1e-16, 1e-15); + E_deaths0[1, m]= 1e-15 * prediction0[1,m]; for (i in 2:N2){ - for(j in 1:(i-1)){ - E_deaths0[i,m] += prediction0[j,m] * f[i-j,m] * ifr_noise[m]; - } + E_deaths0[i,m] = ifr_noise[m] * dot_product(sub_col(prediction0, 1, m, i-1), tail(f_rev[m], i-1)); } } } diff --git a/utils/process-covariates.r b/utils/process-covariates.r new file mode 100644 index 00000000..e52fe3dd --- /dev/null +++ b/utils/process-covariates.r @@ -0,0 +1,132 @@ +library(rstan) +library(data.table) +library(lubridate) +library(gdata) +library(dplyr) +library(tidyr) +library(EnvStats) +library(scales) +library(stringr) +library(abind) + +process_covariates <- function(countries, interventions, d, ifr.by.country,N2){ + serial.interval = read.csv("data/serial_interval.csv") + # Pads serial interval with 0 if N2 is greater than the length of the serial + # interval array + if (N2 > length(serial.interval$fit)) { + pad_serial.interval <- data.frame( + "X"=(length(serial.interval$fit)+1):N2, + "fit"=rep(1e-17, max(N2-length(serial.interval$fit), 0 )) + ) + serial.interval = rbind(serial.interval, pad_serial.interval) + } + # various distributions required for modeling + mean1 <- 5.1; cv1 <- 0.86; # infection to onset + mean2 <- 17.8; cv2 <- 0.45 # onset to death + x1 <- rgammaAlt(1e6,mean1,cv1) # infection-to-onset distribution + x2 <- rgammaAlt(1e6,mean2,cv2) # onset-to-death distribution + + ecdf.saved <- ecdf(x1+x2) + forecast <- 0 + dates <- list() + reported_cases <- list() + stan_data <- list(M=length(countries$Regions),N=NULL,deaths=NULL,f=NULL, + N0=6,cases=NULL,SI=serial.interval$fit[1:N2],features=NULL, + EpidemicStart = NULL, pop = NULL) + deaths_by_country <- list() + covariate_list <- list() + k=1 + # going over each region + for (Country in countries$Regions){ + IFR <- ifr.by.country$ifr[ifr.by.country$country == Country] + region_pop <- ifr.by.country[ifr.by.country$country==Country,] + region <- d[d$Country==Country,] + region$DateRep <-region$DateRep + region <-region[order(as.Date(region$DateRep)),] # ensure date ordering + + # padding in raw data backwards ex. portugal + date_min <- dmy('31/12/2019') + if (region$DateRep[1] > date_min){ + print(paste(Country,'In padding ECDC data')) + pad_days <-region$DateRep[1] - date_min + pad_dates <- date_min + days(1:pad_days[[1]]-1) + padded_data <- data.frame("Country" = rep(Country, pad_days), + "DateRep" = pad_dates, + "Cases" = as.integer(rep(0, pad_days)), + "Deaths" = as.integer(rep(0, pad_days)), + stringsAsFactors=F) + + region <- bind_rows(padded_data,region) + } + index = which(region$Cases>0)[1] + index1 = which(cumsum(region$Deaths)>=10)[1] # also 5 + index2 = index1-30 + + print(sprintf("First non-zero cases is on day %d, and 30 days before 10 deaths is day %d",index,index2)) + region=region[index2:nrow(region),] + stan_data$EpidemicStart = c(stan_data$EpidemicStart,index1+1-index2) + stan_data$pop = c(stan_data$pop,region_pop$popt) + # NPI interventionss are being used + interventions_region <- interventions[interventions$Country == Country, c(2,3,4,5,6)] # school, self-isolation, public, lockdown, social-distancing + for (ii in 1:ncol(interventions_region)) { + covariate = names(interventions_region)[ii] + region[covariate] <- (region$DateRep >= interventions_region[1,covariate])*1 # should this be > or >=? + } + + dates[[Country]] =region$DateRep + # hazard estimation + N = length(region$Cases) + print(sprintf("%s has %d days of data",Country,N)) + forecast = N2 - N + if(forecast < 0) { + print(sprintf("%s: %d", Country, N)) + print("ERROR!!!! increasing N2") + N2 = N + forecast = N2 - N + } + + # IFR is the overall probability of dying given infection + convolution = function(u) (IFR * ecdf.saved(u)) + + f = rep(0,N2) # f is the probability of dying on day i given infection + f[1] = (convolution(1.5) - convolution(0)) + for(i in 2:N2) { + f[i] = (convolution(i+.5) - convolution(i-.5)) + } + reported_cases[[Country]] = as.vector(as.numeric(region$Cases)) + deaths=c(as.vector(as.numeric(region$Deaths)),rep(-1,forecast)) + cases=c(as.vector(as.numeric(region$Cases)),rep(-1,forecast)) + deaths_by_country[[Country]] = as.vector(as.numeric(region$Deaths)) + region_intervention <- as.data.frame(region[, colnames(interventions_region)]) + region_intervention[N:(N+forecast),] <- region_intervention[N,] + school =region_intervention[,1] + selfIsolation =region_intervention[,2] + publicEvents = region_intervention[,3] + lockdown = region_intervention[,4] + socialDistancing = region_intervention[,5] + firstIntervention = 1*((school+ selfIsolation+ publicEvents+ lockdown + socialDistancing) >= 1) + ## append data + stan_data$N = c(stan_data$N,N) + # stan_data$x = cbind(stan_data$x,x) + stan_data$f = cbind(stan_data$f,f) + stan_data$deaths = cbind(stan_data$deaths,deaths) + stan_data$cases = cbind(stan_data$cases,cases) + + stan_data$N2=N2 + stan_data$x=1:N2 + if(length(stan_data$N) == 1) { + stan_data$N = as.array(stan_data$N) + } + df_features = data.frame('school' = school, 'selfIsolation' = selfIsolation, 'publicEvents' = publicEvents, + 'firstIntervention' = firstIntervention, 'lockdown' = lockdown, 'socialDistancing' = socialDistancing) + features <- as.matrix(df_features) + covariate_list[[k]] <- features + k <- k+1 + } + stan_data$P = dim(features)[2] + stan_data$X = array(NA, dim = c(stan_data$M , stan_data$N2 ,stan_data$P )) + for (i in 1:stan_data$M){ + stan_data$X[i,,] = covariate_list[[i]] + } + return(list("stan_data" = stan_data, "dates" = dates, "reported_cases"=reported_cases, "deaths_by_country" = deaths_by_country)) +} \ No newline at end of file diff --git a/utils/read-data.r b/utils/read-data.r new file mode 100644 index 00000000..b8531310 --- /dev/null +++ b/utils/read-data.r @@ -0,0 +1,42 @@ +library(tidyr) +library(lubridate) +library(stringr) +library(dplyr) + +read_obs_data <- function(countries){ + # Read the deaths and cases data + d <- readRDS('data/COVID-19-up-to-date.rds') + colnames(d)[colnames(d) == "Countries.and.territories"] <- "Country" + d <-d[d$Country %in% countries$Regions, c(1,5,6,7)] + d$DateRep <- as.Date(d$DateRep, format = '%d/%m/%Y') + return(d) +} + +read_ifr_data <- function(){ + ifr.by.country <- read.csv("data/popt_ifr.csv") + ifr.by.country$country <- as.character(ifr.by.country[,2]) + ifr.by.country$country[ifr.by.country$country == "United Kingdom"] <- "United_Kingdom" + return(ifr.by.country) + +} + +read_interventions <- function(countries){ + interventions = read.csv('data/interventions.csv', stringsAsFactors = FALSE) + names_interventions = c('Schools + Universities','Self-isolating if ill', 'Public events', 'Lockdown', 'Social distancing encouraged') + interventions <- interventions[interventions$Type %in% names_interventions,] + interventions <- interventions[,c(1,2,4)] + interventions <- spread(interventions, Type, Date.effective) + names(interventions) <- c('Country','lockdown', 'public_events', 'schools_universities','self_isolating_if_ill', 'social_distancing_encouraged') + interventions <- interventions[c('Country','schools_universities', 'self_isolating_if_ill', 'public_events', 'lockdown', 'social_distancing_encouraged')] + interventions$schools_universities <- as.Date(interventions$schools_universities, format = "%d.%m.%Y") + interventions$lockdown <- as.Date(interventions$lockdown, format = "%d.%m.%Y") + interventions$public_events <- as.Date(interventions$public_events, format = "%d.%m.%Y") + interventions$self_isolating_if_ill <- as.Date(interventions$self_isolating_if_ill, format = "%d.%m.%Y") + interventions$social_distancing_encouraged <- as.Date(interventions$social_distancing_encouraged, format = "%d.%m.%Y") + ## using interventions as dates we want + interventions$schools_universities[interventions$schools_universities > interventions$lockdown] <- interventions$lockdown[interventions$schools_universities > interventions$lockdown] + interventions$public_events[interventions$public_events > interventions$lockdown] <- interventions$lockdown[interventions$public_events > interventions$lockdown] + interventions$social_distancing_encouraged[interventions$social_distancing_encouraged > interventions$lockdown] <- interventions$lockdown[interventions$social_distancing_encouraged > interventions$lockdown] + interventions$self_isolating_if_ill[interventions$self_isolating_if_ill > interventions$lockdown] <- interventions$lockdown[interventions$self_isolating_if_ill > interventions$lockdown] + return(interventions) +} From 8fa9232a06b813ee86a47f314a6b12c0b0de099d Mon Sep 17 00:00:00 2001 From: Swapnil Mishra Date: Fri, 24 Apr 2020 18:01:36 +0100 Subject: [PATCH 2/5] Update environment.yml --- environment.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/environment.yml b/environment.yml index 2abc95a9..33314fda 100644 --- a/environment.yml +++ b/environment.yml @@ -10,5 +10,6 @@ dependencies: - r-dplyr - r-rstan - r-optparse + - r-abind channels: - conda-forge From df982f4d00b40299ffd8cbaa5142bdccda281e5b Mon Sep 17 00:00:00 2001 From: Swapnil Mishra Date: Fri, 24 Apr 2020 18:09:32 +0100 Subject: [PATCH 3/5] Update environment.yml --- environment.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/environment.yml b/environment.yml index 33314fda..af6db83e 100644 --- a/environment.yml +++ b/environment.yml @@ -11,5 +11,7 @@ dependencies: - r-rstan - r-optparse - r-abind + - r-svglite + - r-jsonlite channels: - conda-forge From a67238f9bb933e9f640c3e013b51ea7fcfd375c0 Mon Sep 17 00:00:00 2001 From: Swapnil Mishra Date: Fri, 24 Apr 2020 18:20:10 +0100 Subject: [PATCH 4/5] Update base.r --- base.r | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/base.r b/base.r index 3eb25ea7..ad5752ed 100644 --- a/base.r +++ b/base.r @@ -96,6 +96,7 @@ save(fit,prediction,dates,reported_cases,deaths_by_country,countries,estimated.d dir.create("results/", showWarnings = FALSE, recursive = TRUE) dir.create("figures/", showWarnings = FALSE, recursive = TRUE) dir.create("web/", showWarnings = FALSE, recursive = TRUE) +dir.create("web/data", showWarnings = FALSE, recursive = TRUE) library(bayesplot) filename <- paste0(StanModel,'-',JOBID) @@ -138,4 +139,4 @@ if(make_table_error != 0){ verify_result_error <- system(paste0("Rscript web-verify-output.r ", filename,'.Rdata'),intern=FALSE) if(verify_result_error != 0){ stop(sprintf("Verification of web output failed! Code: %d", verify_result_error)) -} \ No newline at end of file +} From d41c6011e49d1ac9748f26f52a238e208998b7b8 Mon Sep 17 00:00:00 2001 From: Swapnil Mishra Date: Fri, 24 Apr 2020 18:27:33 +0100 Subject: [PATCH 5/5] Update base.r --- base.r | 1 + 1 file changed, 1 insertion(+) diff --git a/base.r b/base.r index ad5752ed..0ab824c9 100644 --- a/base.r +++ b/base.r @@ -90,6 +90,7 @@ if(JOBID == "") print(sprintf("Jobid = %s",JOBID)) countries <- countries$Regions +save.image(paste0('results/',StanModel,'-',JOBID,'.Rdata')) save(fit,prediction,dates,reported_cases,deaths_by_country,countries,estimated.deaths,estimated.deaths.cf,out,file=paste0('results/',StanModel,'-',JOBID,'-stanfit.Rdata')) ## Ensure that output directories exist