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base-Italy.r
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library(rstan)
library(data.table)
library(lubridate)
library(gdata)
library(dplyr)
library(tidyr)
library(EnvStats)
library(scales)
library(stringr)
library(abind)
library(optparse)
library(ggplot2)
library(ggrepel)
library(gtable)
library(zoo)
source('Italy/code/utils/read-data-subnational.r')
source('Italy/code/utils/process-covariates-italy.r')
# Commandline options and parsing
parser <- OptionParser()
parser <- add_option(parser, c("-D", "--debug"), action="store_true",
help="Perform a debug run of the model")
parser <- add_option(parser, c("-F", "--full"), action="store_true",
help="Perform a full run of the model")
cmdoptions <- parse_args(parser, args = commandArgs(trailingOnly = TRUE), positional_arguments = TRUE)
# Default run parameters for the model
# Sys.setenv(DEBUG = "TRUE") # to run in debug mode
if(is.null(cmdoptions$options$debug)) {
DEBUG = Sys.getenv("DEBUG") == "TRUE"
} else {
DEBUG = cmdoptions$options$debug
}
# Sys.setenv(FULL = "TRUE") # to run in full mode
if(is.null(cmdoptions$options$full)) {
FULL = Sys.getenv("FULL") == "TRUE"
} else {
FULL = cmdoptions$options$full
}
if(DEBUG && FULL) {
stop("Setting both debug and full run modes at once is invalid")
}
if(length(cmdoptions$args) == 0) {
StanModel = 'base-italy'
} else {
StanModel = cmdoptions$args[1]
}
args = cmdoptions$args
# if using rstudio change this, 1: stan-file, 2: mobility data to use, 3:interventions to use
# 4:formula to use for full pool, 5:formula for partial pool
# debug and full are either commandline or sys variable
# these are the intervention names
#"schools_universities" "public_events""lockdown" "social_distancing_encouraged" "self_isolating_if_ill"
# these are the mobility names
# "grocery" "parks" "residential" "retail" "transit" "workplace" "averageMobility"
if(length(args) == 0) {
args = c('base-italy', 'google', 'interventions',
'~ -1 + residential + transit + averageMobility',
'~ -1 + residential + transit + averageMobility'
)
}
StanModel = args[1]
cat(sprintf("Running:\nStanModel = %s\nMobility = %s\nInterventions = %s\nFixed effects:%s\nRandom effects:%s\nDebug: %s\n",
StanModel,args[2],args[3], args[4],args[5], DEBUG))
# Read deaths data for regions
d <- read_obs_data()
regions<-unique(as.factor(d$country))
# Read ifr
ifr.by.country <- read_ifr_data(unique(d$country))
ifr.by.country <- ifr.by.country[1:22,]
# Read google mobility, apple mobility, interventions, stringency
google_mobility <- read_google_mobility("Italy")
mobility<-google_mobility[which(google_mobility$country!="Italy"),]
# Read interventions
interventions <- read_interventions()
interventions<-interventions[which(interventions$Country!="Italy"),]
# Table 1 and top 7
regions_sum <- d %>% group_by(country) %>% summarise(Deaths=sum(Deaths)) %>%
inner_join(ifr.by.country) %>% mutate(deathsPer1000=Deaths/popt) %>%
arrange(desc(deathsPer1000))
regions_sum <- regions_sum[,-which(colnames(regions_sum) %in% c("X"))]
regions_sum$ifr<-signif(regions_sum$ifr*100,2)
regions_sum$deathsPer1000 <- signif(regions_sum$deathsPer1000*1000,2)
top_7 <- regions_sum[1:7,]
forecast <- 7 # increaseto get correct number of days to simulate
# Maximum number of days to simulate
N2 <- (max(d$DateRep) - min(d$DateRep) + 1 + forecast)[[1]]
formula = as.formula(args[4])
formula_partial = as.formula(args[5])
processed_data <- process_covariates(regions = regions, mobility = mobility, intervention = interventions,
d = d , ifr.by.country = ifr.by.country, N2 = N2, formula = formula, formula_partial = formula_partial)
stan_data <- processed_data$stan_data
dates <- processed_data$dates
reported_deaths <- processed_data$deaths_by_country
reported_cases <- processed_data$reported_cases
options(mc.cores = parallel::detectCores())
rstan_options(auto_write = TRUE)
m = stan_model(paste0('Italy/code/stan-models/',StanModel,'.stan'))
if(DEBUG) {
fit = sampling(m,data=stan_data,iter=40,warmup=20,chains=2)
} else if (FULL) {
fit = sampling(m,data=stan_data,iter=2000,warmup=1500,chains=4,thin=1,control = list(adapt_delta = 0.95, max_treedepth = 15))
} else {
fit = sampling(m,data=stan_data,iter=600,warmup=300,chains=4,thin=1,control = list(adapt_delta = 0.95, max_treedepth = 10))
}
out <- rstan::extract(fit)
estimated_cases_raw <- out$prediction
estimated_deaths_raw <- out$E_deaths
estimated_deaths_cf <- out$E_deaths0
JOBID = Sys.getenv("PBS_JOBID")
if(JOBID == "")
JOBID = as.character(abs(round(rnorm(1) * 1000000)))
print(sprintf("Jobid = %s",JOBID))
filename <- paste0(StanModel,'-',JOBID)
regions <- unique(d$country)
# This is a hack to get it to save
states = regions
covariate_data = list(interventions, mobility)
save(fit, dates, reported_cases, reported_deaths, regions, states, JOBID ,
estimated_cases_raw, estimated_deaths_raw, estimated_deaths_cf, stan_data, covariate_data,
file=paste0('Italy/results/',StanModel,'-',JOBID,'-stanfit.Rdata'))
source("Italy/code/plotting/make-plots.r")
make_plots_all(paste0('Italy/results/', StanModel, '-', JOBID, '-stanfit.Rdata'),
last_date_data = max(dates[[1]]))
source("Italy/code/utils/make-table.r")
# Prints attackrates to console
make_table(paste0('Italy/results/', StanModel, '-', JOBID, '-stanfit.Rdata'),
date_till_percentage = max(dates[[1]]))
# code for scenarios runs only in full mode
if (FULL){
source("Italy/code/utils/simulate-regional.r")
len_forecast <- 8*7
# Can make plots = TRUE if you want to see 3 panel plots for simulations and rt_plots
simulate_scenarios(JOBID = JOBID, StanModel, plots = TRUE, scenario_type = "increase-mob-current", len_forecast = len_forecast,
subdir='Italy',
simulate_code='Italy/code/stan-models/simulate.stan', mobility_vars=c(1,2,3),
mobility_increase = 40)
simulate_scenarios(JOBID = JOBID, StanModel, plots = TRUE, scenario_type = "increase-mob-current", len_forecast = len_forecast,
subdir='Italy',
simulate_code='Italy/code/stan-models/simulate.stan', mobility_vars=c(1,2,3),
mobility_increase = 20)
simulate_scenarios(JOBID = JOBID, StanModel, plots = TRUE, scenario_type = "constant-mob", len_forecast = len_forecast,
subdir='Italy',
simulate_code='Italy/code/stan-models/simulate.stan', mobility_vars=c(1,2,3),
mobility_increase = 0)
source("Italy/code/utils/make-table.r")
# Prints attack rates to console
scenario_type = "constant-mob"
mobility_increase = 0
make_table_simulation(paste0('Italy/results/sim-', scenario_type, "-", StanModel, '-', len_forecast, '-', mobility_increase, '-', JOBID, '-stanfit.Rdata'),
date_till_percentage = max(dates[[1]]) + len_forecast)
source("Italy/code/plotting/make-scenario-plots-top7.r")
make_scenario_comparison_plots_mobility(JOBID = JOBID, StanModel, len_forecast = len_forecast,
last_date_data = max(dates[[1]]) + len_forecast, baseline = FALSE,
mobility_increase = 20,top=7)
make_scenario_comparison_plots_mobility(JOBID = JOBID, StanModel, len_forecast = len_forecast,
last_date_data = max(dates[[1]]) + len_forecast, baseline = FALSE,
mobility_increase = 40,top=7)
make_scenario_comparison_plots_mobility(JOBID = JOBID, StanModel, len_forecast = len_forecast,
last_date_data = max(dates[[1]]) + len_forecast, baseline = FALSE,
mobility_increase = 20,top=8)
make_scenario_comparison_plots_mobility(JOBID = JOBID, StanModel, len_forecast = len_forecast,
last_date_data = max(dates[[1]]) + len_forecast, baseline = FALSE,
mobility_increase = 40,top=8)
make_scenario_comparison_plots_mobility(JOBID = JOBID, StanModel, len_forecast = len_forecast,
last_date_data = max(dates[[1]]) + len_forecast, baseline = FALSE,
mobility_increase = 20,top=9)
make_scenario_comparison_plots_mobility(JOBID = JOBID, StanModel, len_forecast = len_forecast,
last_date_data = max(dates[[1]]) + len_forecast, baseline = FALSE,
mobility_increase = 40,top=9)
}