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multi_city_voi.R
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#' Main script to run ITHIM Global in sampling mode
#'
#' Script to run ITHIM Global using input parameter distributions. Outputs the health impacts associated with transport in a given city
#' via an air pollution, physical activity and injury pathway. Also performs a Value of Information analysis to calculate the the
#' expected values of partially perfect information, i.e the reduction in standard deviation in the outcome were we to know
#' an input parameter or several interdependent input parameters exactly.
#'
#' The ITHIM Global main sampling script works as follows:
#'
#' - the following variables need to be defined before running the script:
#' - the name(s) of the city or cities for which the model is to be run
#' - the number of samples to be drawn from the input parameters distributions which
#' equals the number of model runs
#' - whether a VoI analysis is to be performed, if yes, also set the following parameters:
#' - define the list of outcomes for which the VoI analysis is to be performed
#' - define whether you want to run the VoI analysis split by sex
#' - define whether you want to run the VoI analysis split by sex and age
#' - define whether you want to calculate the sum of the outcomes of interest and whether
#' whether to include this sum in the VoI analysis
#' - The input parameter file name containing the global and local input parameters and their distribution parameters
#' - define the output_version
#' - define whether to write the key aspect of the model run to the OutputVersionControl.txt file which
#' documents the key aspects of the model run (timestamp, author name, cities for which model was run,
#' input parameter file name, output version number, number of samples, and any comments. If yes, also define
#' - author name
#' - any comments that are to be written to the file
#' - The scenarios need defining by:
#' - updating the character defining which scenario script is to be called
#' - giving the reference scenario against which all other scenarios are compared,
#' this reference scenario needs to be the scenario name which corresponds to the current input parameter files
#' - giving the percentage increase in each mode for the BOGOTA (GLOBAL, LATAM, and AFRICA_INDIA) scenarios
#'
#' - the remainder of the code does not need to be changed:
#'
#' - local and global input parameters from the input parameter spreadsheet are read in and put into the correct format needed
#' for the model run
#'
#' - The\code{\link{run_ithim_setup()}} script is called which prepares the input data needed for the health impact assessment
#' and samples for the input parameter distributions
#'
#' - The \code{\link{run_ithim()}} script is called which performs the health impact assessment NSAMPLE times
#'
#' - The \code{\link{extract_data_for_voi()}} function is called which gets the data into the correct
#' format for plotting and the VoI analysis
#'
#' - Plots are created for all cities and each city individually showing the total YLL outcomes and their
#' 95% confidence intervals for each scenario ('results/voi/city_yll_',output_version,'.pdf')
#'
#' - Plots are created for each city individually showing the total YLL outcomes for the entire population and
#' both sexes and their 95% confidence intervals for each scenario ('results/voi/city_yll_sex',output_version,'.pdf')
#'
#' - One plot is created showing the change in total YLL per person relative to the baseline summed
#' across all cities ('results/voi/combined_yll_pp','_',output_version,'.pdf')
#'
#' - if required the VoI analysis is started:
#' - EVPPI values for the different input parameters of the total population outcomes are calculated for
#' each city ('results/voi/evppi_',output_version,".csv")
#' - EVPPI values are plotted for each city ('results/voi/evppi_',output_version,".pdf")
#' - if required the VoI analysis by sex is started:
#' - EVPPI values for the different input parameters of the total population outcomes by sex are
#' calculated for each city ('results/voi/evppi_sex_',output_version,".csv")
#' - EVPPI values are plotted for each city ('results/voi/evppi_sex_',output_version,".pdf")
#' - if required the VoI analysis by sex and age group is started:
#' - EVPPI values for the different input parameters of the total population outcomes by sex and age group are
#' calculated for each city (results/voi/evppi_agesex_',output_version,".csv")
#' - EVPPI values are plotted for each city and outcome ('results/voi/evppi_agesex_',output_version,".pdf")
#'
#' - The OutputVersionControl.txt file is updated if needed
#'
#'
#'
library(ithimr)
library(earth)
library(RColorBrewer)
library(plotrix)
library(foreach)
library(future)
plan(multisession)
library(doRNG)
library(future.apply)
library(voi) #install_github("chjackson/voi")
library(readxl)
library(rlist)
library(janitor)
if (!require("drpa",character.only = TRUE)) {
print('Installing "drpa" package...')
remotes::install_github("meta-analyses/drpa")
library(drpa)
print("")
}
rm(list=ls())
#
# cities <- c('belo_horizonte', 'bogota', 'buenos_aires',
# 'cali', 'medellin', 'mexico_city', 'montevideo',
# 'santiago', 'sao_paulo', 'accra', 'bangalore', 'cape_town','delhi',
# 'vizag', 'kisumu', 'nairobi', 'port_louis')
# cities <- c('antofagasta', 'arica', 'belo_horizonte', 'bogota', 'buenos_aires',
# 'cali', 'copiapo', 'coquimbo_laserena', 'gran_valparaiso',
# 'iquique_altohospicio', 'medellin', 'mexico_city', 'montevideo',
# 'osorno', 'puerto_montt', 'san_antonio',
# 'santiago', 'sao_paulo', 'temuco_padrelascasas', 'valdivia',
# 'accra', 'bangalore', 'cape_town','delhi', 'vizag', 'kisumu', 'nairobi', 'port_louis')
cities <- c('bogota')
# number of times input values are sampled from each input parameter distribution
nsamples <- 2000
input_parameter_file <- "InputParameters_v42.0.xlsx"
## Get the current repo sha
gitArgs <- c("rev-parse", "--short", "HEAD", ">", file.path("repo_sha"))
# Use shell command for Windows as it's failing with system2 for Windows (giving status 128)
if (.Platform$OS.type == "windows"){
shell(paste(append("git", gitArgs), collapse = " "), wait = T)
} else {
system2("git", gitArgs, wait = T)
}
# repo_sha <- as.character(readLines(file.path("repo_sha")))
#output_version <- paste0(repo_sha, "_test_run") # gives the version number of the output documents, independent of the input parameter file name
output_version <- 'bogota_2000samples3_v42.0'
voi_analysis <- T # set to T if want to run VoI analysis and to F otherwise
# flag whether to run VOI analysis split gender
voi_gender <- T # set to T if want to include split and to F otherwise
# flag whether to run VOI analysis split by age and gender
voi_age_gender <- T # set to T if want to include split and to F otherwise
# list of potential values for the outcome_voi_list
# 'pa_ap_all_cause', 'pa_ap_IHD', 'pa_total_cancer', 'pa_ap_lung_cancer', 'ap_COPD',
# 'pa_ap_stroke', 'pa_ap_T2D', 'ap_LRI', 'pa_breast_cancer', 'pa_colon_cancer', 'pa_endo_cancer',
# 'pa_liver_cancer', 'pa_ap_CVD', 'pa_total_dementia', 'pa_myeloma', 'pa_Parkinson',
# 'pa_head_neck_cancer', 'pa_stomach_cancer', 'inj'
# outcome_voi_list <- c('pa_ap_all_cause', 'pa_ap_IHD', 'pa_total_cancer', 'pa_ap_lung_cancer', 'ap_COPD',
# 'pa_ap_stroke', 'pa_ap_T2D', 'ap_LRI', 'pa_breast_cancer', 'pa_colon_cancer', 'pa_endo_cancer',
# 'pa_liver_cancer', 'pa_ap_CVD', 'pa_total_dementia', 'pa_myeloma', 'pa_Parkinson',
# 'pa_head_neck_cancer', 'pa_stomach_cancer', 'inj',
# 'level1', 'level2', 'level3')
outcome_voi_list <- c('pa_ap_all_cause', 'inj', 'level1')
# records the main aspects of an ithim run in the OutputVersionControl.txt document
# text file records timestamp of run, author name, cities the script is run for,
# the input parameter file version used, the output version,
# the number of samples (which is 1 in constant mode), the path to any other input files,
# any comments and the runtime of the code
write_output_control = T # whether you want to save the model run specifics or not
author <- "AKS"
comment <- "Added CO2 emission sampling"
# scenario definition
scenario_name <- "BOGOTA"
reference_scenario <- 'Baseline'
scenario_increase <- 0.05 # increase for each mode in each scenario
compute_mode <- 'sample' # sample from the given input parameter distributions
# disease levels
# different levels
level1 <-c('pa_ap_all_cause','inj')
level2 <- c('pa_total_cancer','pa_ap_CVD','ap_respiratory','inj')
level3 <- c('pa_ap_IHD','pa_ap_lung_cancer','ap_COPD','pa_ap_stroke','pa_ap_T2D','ap_LRI',
'pa_breast_cancer','pa_colon_cancer','pa_endo_cancer','pa_liver_cancer','pa_total_dementia',
'pa_myeloma','pa_Parkinson','pa_head_neck_cancer', 'pa_stomach_cancer',
'pa_myeloid_leukemia','inj')
############################### No need to change the following ##################################
# keep record when code started:
starttime <- Sys.time()
all_inputs <- read_excel(input_parameter_file, sheet = "all_city_parameter_inputs")
all_inputs[is.na(all_inputs)] <- ""
all_inputs <- as.data.frame(all_inputs)
#all_inputs <- read.csv('all_city_parameter_inputs.csv',stringsAsFactors = F) # read in parameter list
# get input parameters into correct format
parameter_names <- all_inputs$parameter
parameter_starts <- which(parameter_names!='')
parameter_stops <- c(parameter_starts[-1] - 1, nrow(all_inputs))
parameter_names <- parameter_names[parameter_names!='']
parameter_list <- list()
for(i in 1:length(parameter_names)){
parameter_list[[parameter_names[i]]] <- list()
parameter_index <- which(all_inputs$parameter==parameter_names[i])
if(all_inputs[parameter_index,2]=='') {
parameter_list[[parameter_names[i]]] <- lapply(cities,function(x) {
city_index <- which(colnames(all_inputs)==x)
val <- all_inputs[parameter_index,city_index]
ifelse(val%in%c('T','F'),val,ifelse(is.numeric(val), as.numeric(val), as.character(val)))
})
names(parameter_list[[parameter_names[i]]]) <- cities
}else if(all_inputs[parameter_index,2]=='constant'){
if (compute_mode != 'sample'){
indices <- 0
parameter_list[[parameter_names[i]]] <- lapply(cities,function(x) {
city_index <- which(colnames(all_inputs)==x)
val <- all_inputs[parameter_index+indices,city_index]
ifelse(val=='',0,as.numeric(val))
})
}
if(compute_mode=='sample'){ # if sampling from distribution, check that distribution parameters exist
parameter_list[[parameter_names[i]]] <- lapply(cities,function(x) {
indices <- 1:2
city_index <- which(colnames(all_inputs)==x)
val <- all_inputs[parameter_index+indices,city_index]
if (val[1] == '' & val[2]==''){ # if no distribution parameters given in input file, read in constant value instead
indices <-0
city_index <- which(colnames(all_inputs)==x)
val <- all_inputs[parameter_index+indices,city_index]}
val <- as.numeric(val)
})
}
names(parameter_list[[parameter_names[i]]]) <- cities
}else{
parameter_list[[parameter_names[i]]] <- lapply(cities,function(x) {
city_index <- which(colnames(all_inputs)==x)
if(any(all_inputs[parameter_starts[i]:parameter_stops[i],city_index]!='')){
sublist_indices <- which(all_inputs[parameter_starts[i]:parameter_stops[i],city_index]!='')
thing <- as.list(as.numeric(c(all_inputs[parameter_starts[i]:parameter_stops[i],city_index])[sublist_indices]))
names(thing) <- c(all_inputs[parameter_starts[i]:parameter_stops[i],2])[sublist_indices]
thing
}
}
)
names(parameter_list[[parameter_names[i]]]) <- cities
}
}
list2env(parameter_list, environment())
# read in global parameters
all_global_inputs <- read_excel(input_parameter_file, sheet = "all_global_parameter_inputs")
all_global_inputs[is.na(all_global_inputs)] <- ""
all_global_inputs <- as.data.frame(all_global_inputs)
# get input parameters into correct format
global_parameter_names <- all_global_inputs$parameter
global_parameter_starts <- which(global_parameter_names!='')
global_parameter_stops <- c(global_parameter_starts[-1] - 1, nrow(all_global_inputs))
global_parameter_names <- global_parameter_names[global_parameter_names!='']
global_parameter_list <- list()
for(i in 1:length(global_parameter_names)){
global_parameter_list[[global_parameter_names[i]]] <- list()
global_parameter_index <- which(all_global_inputs$parameter==global_parameter_names[i])
if(all_global_inputs[global_parameter_index,2]=='') {
global_parameter_list[[global_parameter_names[i]]] <- all_global_inputs[global_parameter_index,'global']
}else if(all_global_inputs[global_parameter_index,2]=='constant'){
if (compute_mode != 'sample'){
global_parameter_list[[global_parameter_names[i]]] <- ifelse(all_global_inputs[global_parameter_index,'global']=='',
0,as.numeric(all_global_inputs[global_parameter_index,'global']))
}
else if(compute_mode=='sample'){ # if sampling from distribution, check that distribution parameters exist
indices <- 1:2
val <- all_global_inputs[global_parameter_index+indices,'global']
if (val[1] == '' & val[2]==''){ # if no distribution parameters given in input file, read in constant value instead
val <- all_global_inputs[global_parameter_index,'global']}
val <- as.numeric(val)
global_parameter_list[[global_parameter_names[i]]] <- val
}
}
}
list2env(global_parameter_list, environment())
dist_cat <- unlist(strsplit(gsub(" ", "", dist_cat, fixed = TRUE), "\\,"))
min_age <- as.numeric(min_age)
max_age <- as.numeric(max_age)
################################### Start running the actual analysis
## with uncertainty
## comparison across cities
setting_parameters <- c("PM_CONC_BASE","BACKGROUND_PA_SCALAR","PM_EMISSION_INVENTORY","CO2_EMISSION_INVENTORY",
"CHRONIC_DISEASE_SCALAR","PM_TRANS_SHARE","INJURY_REPORTING_RATE","BUS_TO_PASSENGER_RATIO", "CAR_OCCUPANCY_RATIO",
"TRUCK_TO_CAR_RATIO", "FLEET_TO_MOTORCYCLE_RATIO","BUS_WALK_TIME", 'RAIL_WALK_TIME',"PROPORTION_MOTORCYCLE_TRIPS" ,
"DISTANCE_SCALAR_CAR_TAXI",
"DISTANCE_SCALAR_WALKING", "DISTANCE_SCALAR_PT", "DISTANCE_SCALAR_CYCLING", "DISTANCE_SCALAR_MOTORCYCLE")
# logical for PA dose response: set T for city 1, and reuse values in 2 and 3; no need to recompute
pa_dr_quantile <- c(rep(as.logical(pa_dr_quantile_city1), length(cities)))
# logical for AP dose response: set T for city 1, and reuse values in 2 and 3; no need to recompute
ap_dr_quantile <- c(rep(as.logical(ap_dr_quantile_city1), length(cities)))
betaVariables <- c("PM_TRANS_SHARE",
"INJURY_REPORTING_RATE",
"CASUALTY_EXPONENT_FRACTION",
"BUS_TO_PASSENGER_RATIO",
"CAR_OCCUPANCY_RATIO",
"TRUCK_TO_CAR_RATIO",
"FLEET_TO_MOTORCYCLE_RATIO",
"PROPORTION_MOTORCYCLE_TRIPS",
"CHRONIC_DISEASE_SCALAR",
"SIN_EXPONENT_SUM",
"SIN_EXPONENT_SUM_NOV",
"SIN_EXPONENT_SUM_CYCLE",
"SIN_EXPONENT_SUM_PED",
"SIN_EXPONENT_SUM_VEH")
normVariables <- c('CYCLING_MMET',
'WALKING_MMET',
'PASSENGER_MMET',
'CAR_DRIVER_MMET',
'MOTORCYCLIST_MMET',
'SEDENTARY_ACTIVITY_MMET',
'LIGHT_ACTIVITY_MMET',
'MODERATE_PA_MMET',
'VIGOROUS_PA_MMET',
"PM_CONC_BASE",
"BACKGROUND_PA_SCALAR",
"CASUALTY_EXPONENT_FRACTION",
"CASUALTY_EXPONENT_FRACTION_CYCLE",
"CASUALTY_EXPONENT_FRACTION_PED",
"CASUALTY_EXPONENT_FRACTION_VEH",
"DISTANCE_SCALAR_CAR_TAXI",
"DISTANCE_SCALAR_WALKING",
"DISTANCE_SCALAR_PT",
"DISTANCE_SCALAR_CYCLING",
"DISTANCE_SCALAR_MOTORCYCLE")
save(cities,setting_parameters,injury_reporting_rate,chronic_disease_scalar,pm_conc_base,pm_trans_share,
background_pa_scalar,background_pa_confidence,cycling_mmet,walking_mmet,passenger_mmet,
car_driver_mmet,motorcyclist_mmet,sedentary_activity_mmet,light_activity_mmet,moderate_pa_mmet,
PM_emission_inventories,CO2_emission_inventories,
sin_exponent_sum,casualty_exponent_fraction, sin_exponent_sum_nov,
sin_exponent_sum_cycle,casualty_exponent_fraction_cycle, sin_exponent_sum_ped,casualty_exponent_fraction_ped,
sin_exponent_sum_veh,casualty_exponent_fraction_veh, pa_dr_quantile,ap_dr_quantile,
bus_to_passenger_ratio,car_occupancy_ratio,truck_to_car_ratio,PM_emission_confidence,CO2_emission_confidence,
distance_scalar_car_taxi,distance_scalar_motorcycle,
distance_scalar_pt,distance_scalar_walking,distance_scalar_cycling,add_motorcycle_fleet,add_personal_motorcycle_trips,
fleet_to_motorcycle_ratio, proportion_motorcycle_trips,
betaVariables,normVariables,file='diagnostic/parameter_settings.Rdata')
#parameters_only <- F
#numcores <- parallel::detectCores() - 1
multi_city_ithim <- outcome <- outcome_pp <- yll_per_hundred_thousand <- list()
print(system.time(
for(ci in 1:length(cities)){
city <- cities[ci]
print(city)
multi_city_ithim[[city]] <- run_ithim_setup(NSAMPLES = nsamples,
seed=ci,
# from multi city script
DIST_CAT = as.character(dist_cat),
ADD_WALK_TO_PT_TRIPS = as.logical(add_walk_to_pt_trips[[city]]),# originally = F,
CITY=city,
AGE_RANGE = c(min_age,max_age),
TREAT_TAXI_AS_CAR = as.logical(treat_taxi_as_car[[city]]),
ADD_TRUCK_DRIVERS = as.logical(add_truck_drivers),
ADD_BUS_DRIVERS = as.logical(add_bus_drivers),
ADD_CAR_DRIVERS = as.logical(add_car_drivers),
ADD_MOTORCYCLE_FLEET = as.logical(add_motorcycle_fleet[[city]]), #ADD_MOTORCYCLE_FLEET = add_motorcycle_fleet[[city]],
ADD_PERSONAL_MOTORCYCLE_TRIPS = as.character(add_personal_motorcycle_trips[[city]]),
PM_emission_inventory = PM_emission_inventories[[city]],
CO2_emission_inventory = CO2_emission_inventories[[city]], # added
speeds = speeds[[city]],
FLEET_TO_MOTORCYCLE_RATIO = fleet_to_motorcycle_ratio[[city]],
PROPORTION_MOTORCYCLE_TRIPS = proportion_motorcycle_trips[[city]],
CYCLING_MMET = cycling_mmet,
WALKING_MMET = walking_mmet,
PASSENGER_MMET = passenger_mmet,
CAR_DRIVER_MMET = car_driver_mmet,
MOTORCYCLIST_MMET = motorcyclist_mmet,
SEDENTARY_ACTIVITY_MMET = sedentary_activity_mmet,
LIGHT_ACTIVITY_MMET = light_activity_mmet,
MODERATE_PA_MMET = moderate_pa_mmet,
VIGOROUS_PA_MMET = vigorous_pa_mmet,
DAY_TO_WEEK_TRAVEL_SCALAR = as.numeric(day_to_week_scalar[[city]]),
SIN_EXPONENT_SUM = sin_exponent_sum,
CASUALTY_EXPONENT_FRACTION = casualty_exponent_fraction,
SIN_EXPONENT_SUM_NOV = sin_exponent_sum_nov,
SIN_EXPONENT_SUM_CYCLE = sin_exponent_sum_cycle,
CASUALTY_EXPONENT_FRACTION_CYCLE = casualty_exponent_fraction_cycle,
SIN_EXPONENT_SUM_PED = sin_exponent_sum_ped,
CASUALTY_EXPONENT_FRACTION_PED = casualty_exponent_fraction_ped,
SIN_EXPONENT_SUM_VEH = sin_exponent_sum_veh,
CASUALTY_EXPONENT_FRACTION_VEH = casualty_exponent_fraction_veh,
CALL_INDIVIDUAL_SIN = as.logical(call_individual_sin),
PA_DOSE_RESPONSE_QUANTILE = pa_dr_quantile[ci],
AP_DOSE_RESPONSE_QUANTILE = ap_dr_quantile[ci],
INJURY_REPORTING_RATE = injury_reporting_rate[[city]],
CHRONIC_DISEASE_SCALAR = chronic_disease_scalar[[city]],
PM_CONC_BASE = pm_conc_base[[city]],
PM_TRANS_SHARE = pm_trans_share[[city]],
BACKGROUND_PA_SCALAR = background_pa_scalar[[city]],
BUS_WALK_TIME = bus_walk_time[[city]],
RAIL_WALK_TIME = rail_walk_time[[city]],
#additional in VoI script
REFERENCE_SCENARIO= reference_scenario,
BACKGROUND_PA_CONFIDENCE = as.numeric(background_pa_confidence[[city]]),
BUS_TO_PASSENGER_RATIO = bus_to_passenger_ratio[[city]],
CAR_OCCUPANCY_RATIO = car_occupancy_ratio[[city]],
TRUCK_TO_CAR_RATIO = truck_to_car_ratio[[city]],
PM_EMISSION_INVENTORY_CONFIDENCE = as.numeric(PM_emission_confidence[[city]]),
CO2_EMISSION_INVENTORY_CONFIDENCE = as.numeric(CO2_emission_confidence[[city]]),
DISTANCE_SCALAR_CAR_TAXI = distance_scalar_car_taxi[[city]],
DISTANCE_SCALAR_WALKING = distance_scalar_walking[[city]],
DISTANCE_SCALAR_PT = distance_scalar_pt[[city]],
DISTANCE_SCALAR_CYCLING = distance_scalar_cycling[[city]],
DISTANCE_SCALAR_MOTORCYCLE = distance_scalar_motorcycle[[city]],
SCENARIO_NAME = scenario_name,
SCENARIO_INCREASE = scenario_increase,
BUS_DRIVER_PROP_MALE = as.numeric(bus_driver_prop_male[[city]]),
BUS_DRIVER_MALE_AGERANGE = bus_driver_male_agerange[[city]],
BUS_DRIVER_FEMALE_AGERANGE = bus_driver_female_agerange[[city]],
TRUCK_DRIVER_PROP_MALE = as.numeric(truck_driver_prop_male[[city]]),
TRUCK_DRIVER_MALE_AGERANGE = truck_driver_male_agerange[[city]],
TRUCK_DRIVER_FEMALE_AGERANGE = truck_driver_female_agerange[[city]],
COMMERCIAL_MBIKE_PROP_MALE = as.numeric(commerical_mbike_prop_male[[city]]),
COMMERCIAL_MBIKE_MALE_AGERANGE = commerical_mbike_male_agerange[[city]],
COMMERCIAL_MBIKE_FEMALE_AGERANGE = commerical_mbike_female_agerange[[city]],
MINIMUM_PT_TIME = as.numeric(minimum_pt_time)
)
# for first city, store model parameters. For subsequent cities, copy parameters over.
if(ci==1){
model_parameters <- names(multi_city_ithim[[city]]$parameters)[!names(multi_city_ithim[[city]]$parameters)%in%setting_parameters]
parameter_names <- model_parameters[model_parameters!="DR_AP_LIST"]
parameter_samples <- sapply(parameter_names,function(x)multi_city_ithim[[city]]$parameters[[x]])
}else{
for(param in model_parameters) multi_city_ithim[[city]]$parameters[[param]] <- multi_city_ithim[[1]]$parameters[[param]]
if (!is.null(multi_city_ithim[[1]]$parameters$PM_CONC_BASE)){
background_quantile <- plnorm(multi_city_ithim[[1]]$parameters$PM_CONC_BASE,log(pm_conc_base[[1]][1]),log(pm_conc_base[[1]][2]))
multi_city_ithim[[city]]$parameters$PM_CONC_BASE <- qlnorm(background_quantile,log(pm_conc_base[[city]][1]),log(pm_conc_base[[city]][2]))
}
if (!is.null(multi_city_ithim[[1]]$parameters$PM_TRANS_SHARE)){
proportion_quantile <- pbeta(multi_city_ithim[[1]]$parameters$PM_TRANS_SHARE,pm_trans_share[[1]][1],pm_trans_share[[1]][2])
multi_city_ithim[[city]]$parameters$PM_TRANS_SHARE <- qbeta(proportion_quantile,pm_trans_share[[city]][1],pm_trans_share[[city]][2])
}
}
multi_city_ithim[[city]]$outcomes <- list()
doFuture::registerDoFuture()
run_ithm_fn <- function(nsamples, ithim_object,seed, FUN=run_ithim){
foreach(i=1:nsamples, .export = ls(globalenv()) ) %dorng% { #%dopar%
FUN(ithim_object, seed=i)
}
}
multi_city_ithim[[city]]$outcomes <- run_ithm_fn(nsamples,ithim_object = multi_city_ithim[[city]], seed)
#multi_city_ithim[[city]]$outcomes <- run_ithim(ithim_object=multi_city_ithim[[city]], seed = 1)
multi_city_ithim[[city]]$DEMOGRAPHIC <- DEMOGRAPHIC
scenario_names <- multi_city_ithim[[city]]$outcome[[1]]$SCEN
## rename city-specific parameters according to city
for(i in 1:length(multi_city_ithim[[city]]$parameters$PM_EMISSION_INVENTORY[[1]])){
extract_vals <- sapply(multi_city_ithim[[city]]$parameters$PM_EMISSION_INVENTORY,function(x)x[[i]])
if(sum(extract_vals)!=0)
multi_city_ithim[[city]]$parameters[[paste0('PM_EMISSION_INVENTORY_',names(multi_city_ithim[[city]]$parameters$PM_EMISSION_INVENTORY[[1]])[i],'_',city)]] <- extract_vals
}
for(i in 1:length(multi_city_ithim[[city]]$parameters$CO2_EMISSION_INVENTORY[[1]])){
extract_vals <- sapply(multi_city_ithim[[city]]$parameters$CO2_EMISSION_INVENTORY,function(x)x[[i]])
if(sum(extract_vals)!=0)
multi_city_ithim[[city]]$parameters[[paste0('CO2_EMISSION_INVENTORY_',names(multi_city_ithim[[city]]$parameters$CO2_EMISSION_INVENTORY[[1]])[i],'_',city)]] <- extract_vals
}
for(param in setting_parameters) names(multi_city_ithim[[city]]$parameters)[which(names(multi_city_ithim[[city]]$parameters)==param)] <- paste0(param,'_',city)
multi_city_ithim[[city]]$parameters <- multi_city_ithim[[city]]$parameters[-which(names(multi_city_ithim[[city]]$parameters)==paste0('PM_EMISSION_INVENTORY_',city))]
multi_city_ithim[[city]]$parameters <- multi_city_ithim[[city]]$parameters[-which(names(multi_city_ithim[[city]]$parameters)==paste0('CO2_EMISSION_INVENTORY_',city))]
parameter_names_city <- names(multi_city_ithim[[city]]$parameters)[sapply(names(multi_city_ithim[[city]]$parameters),function(x)grepl(x,pattern=city))]
## add to parameter names
parameter_names <- c(parameter_names,parameter_names_city)
## get parameter samples and add to array of parameter samples
parameter_samples <- cbind(parameter_samples,sapply(parameter_names_city,function(x)multi_city_ithim[[city]]$parameters[[x]]))
#if(ci>1) multi_city_ithim[[city]]$parameters <- c()
# save results for city and then delete
saveRDS(multi_city_ithim[[city]],paste0('results/voi/',city,'_',output_version,'.Rds'))
# if(ci>1){
# multi_city_ithim[[ci]] <- 0
# }else{
# multi_city_ithim[[ci]]$outcomes <- 0
# }
}
))
# add relevant model run information to multi_city_ithim list
timestamp <- Sys.time()
multi_city_ithim$ithim_run <- list()
multi_city_ithim$ithim_run$input_parameter_file <- input_parameter_file
multi_city_ithim$ithim_run$scenarios_used <- scenario_name
multi_city_ithim$ithim_run$reference_scenario <- reference_scenario
multi_city_ithim$ithim_run$scenario_increase <- scenario_increase
multi_city_ithim$ithim_run$scenario_names <- scenario_names
multi_city_ithim$ithim_run$compute_mode <- compute_mode
multi_city_ithim$ithim_run$timestamp <- timestamp
multi_city_ithim$ithim_run$output_version <- output_version
multi_city_ithim$ithim_run$author <- author
multi_city_ithim$ithim_run$comment <- comment
# save the sampled parameters for all model runs and cities
saveRDS(parameter_samples,paste('diagnostic/parameter_samples_',output_version,'.Rds'),version=2)
print('finished ithim-run')
########################################### re-read and extract results #########################################
# set parameters
NSCEN <- length(scenario_names) - 1 # number of scenarios not including baseline scenario
SCEN_SHORT_NAME <- scenario_names
NSAMPLES <- nsamples
# get outputs from ithim run into correct formats and calculate summary statistics
ithim_results <- ithimr::extract_data_for_voi(NSCEN, NSAMPLES, SCEN_SHORT_NAME,cities,multi_city_ithim, output_version,
level1, level2, level3)
# combined AP and PA
voi_complete_df <- ithim_results$voi_complete
voi_complete_summary_df <- ithim_results$voi_complete_summary
voi_complete_100k_df <- ithim_results$voi_complete_100k
voi_complete_100k_summary_df <- ithim_results$voi_complete_100k_summary
# separate AP and PA
voi_complete_pathway_df <- ithim_results$voi_complete_pathway
voi_complete_summary_pathway_df <- ithim_results$voi_complete_summary_pathway
voi_complete_100k_pathway_df <- ithim_results$voi_complete_100k_pathway
voi_complete_100k_summary_pathway_df <- ithim_results$voi_complete_100k_summary_pathway
voi_outputs_all_csv <- paste0('results/voi/All_samples_output_',output_version,".csv")
write.csv(voi_complete_df ,voi_outputs_all_csv,row.names = FALSE) # save as csv file
voi_outputs_all_100k_csv <- paste0('results/voi/All_samples_100k_output_',output_version,".csv")
write.csv(voi_complete_100k_df ,voi_outputs_all_100k_csv,row.names = FALSE) # save as csv file
ci_outputstats_csv <- paste0('results/voi/VoI_CI_output_stats_',output_version,".csv")
write.csv(voi_complete_summary_df,ci_outputstats_csv,row.names = FALSE) # save as csv file
ci_100k_outputstats_csv <- paste0('results/voi/VoI_CI_100k_output_stats_',output_version,".csv")
write.csv(voi_complete_100k_summary_df,ci_100k_outputstats_csv,row.names = FALSE) # save as csv file
voi_outputs_all_pathway_csv <- paste0('results/voi/All_samples_output_pathway_',output_version,".csv")
write.csv(voi_complete_pathway_df ,voi_outputs_all_pathway_csv,row.names = FALSE) # save as csv file
voi_outputs_all_100k_pathway_csv <- paste0('results/voi/All_samples_100k_output_pathway_',output_version,".csv")
write.csv(voi_complete_100k_pathway_df ,voi_outputs_all_100k_pathway_csv,row.names = FALSE) # save as csv file
ci_outputstats_pathway_csv <- paste0('results/voi/VoI_CI_output_stats_pathway_',output_version,".csv")
write.csv(voi_complete_summary_pathway_df,ci_outputstats_pathway_csv,row.names = FALSE) # save as csv file
ci_100k_outputstats_pathway_csv <- paste0('results/voi/VoI_CI_100k_output_stats_pathway_',output_version,".csv")
write.csv(voi_complete_100k_summary_pathway_df,ci_100k_outputstats_pathway_csv,row.names = FALSE) # save as csv file
######################################################### plot results #######################################################
print('plot results')
# plots only work if more than one sample was selected
if(nsamples > 1){
# plot level 1 results - one plot for all cities plus individual plots for each city
{pdf(paste0('results/voi/city_yll_level1_',output_version,'.pdf'),height=6,width=6)
# one plot for all cities - might be difficult to read if too many cities
par <- par(mar=c(5,5,1,1))
level1_df <- voi_complete_summary_df %>% filter(age_sex == 'all all') %>% dplyr::select(city, value_type, matches('level1'))
means <- as.matrix(unlist(transpose(level1_df %>% filter(value_type == 'mean') %>% dplyr::select(matches('level1')) )))
ninefive <- list()
for (cityname in cities){
ninefive[[cityname]] <- as.matrix(level1_df %>% filter(city == cityname & (value_type == '2.5perc' |
value_type == '97.5perc')) %>% dplyr::select(!c(city, value_type)))
}
yvals <- rep(1:length(cities),each=NSCEN)/10 + rep(1:NSCEN,times=length(cities))
cols <- rainbow(length(cities))
plot(as.vector(means),yvals,pch=16,cex=1,frame=F,ylab='',xlab='Change in total YLL relative to baseline - Level 1',
col=rep(cols,each=NSCEN),yaxt='n',xlim=range(unlist(ninefive)))
axis(2,las=2,at=(1+0.1):(NSCEN+0.1),labels=SCEN_SHORT_NAME[2:length(SCEN_SHORT_NAME)])
for(i in 1:length(cities)) for(j in 1:NSCEN) lines(ninefive[[i]][,j],rep(yvals[j+(i-1)*NSCEN],2),lwd=2,col=cols[i])
abline(v=0,col='grey',lty=2,lwd=2)
text(y=(NSCEN-1)+0.2,x=ninefive[[1]][1,(NSCEN-1)],'95%',col='navyblue',adj=c(-0,-0.3*1))
legend(col=rev(cols),lty=1,bty='n',x= mean(means),legend=rev(cities),y=NSCEN-1,lwd=2)
par(par)
# one plot per city
for(cityname in cities){
sp_index <- which(cities==cityname)
scen_out_city <- level1_df %>% filter(city == cityname)
means <- as.matrix(unlist(transpose(scen_out_city %>% filter(value_type == 'mean') %>% dplyr::select(matches('level1')) )))
ninefive <- as.matrix(scen_out_city %>% filter(value_type == '2.5perc' | value_type == '97.5perc'
) %>% dplyr::select(matches('level1')))
yvals <- rep(1,each=NSCEN)/10 + rep(1:NSCEN)
cols <- rainbow(length(cities))
col_city <- cols[sp_index]
par_city <- par(mar=c(5,5,1,1))
xlab <- paste0(cityname,': Change in total YLL relative to baseline per - Level 1')
plot(as.vector(means),yvals,pch=16,cex=1,frame=F,ylab='',xlab=xlab,col=rep(col_city,each=NSCEN),
yaxt='n',xlim=range(unlist(ninefive)))
axis(2,las=2,at=(1+0.1):(NSCEN+0.1),labels=SCEN_SHORT_NAME[2:length(SCEN_SHORT_NAME)])
for(j in 1:NSCEN) lines(ninefive[,j],rep(yvals[j],2),lwd=2,col=col_city)
abline(v=0,col='grey',lty=2,lwd=2)
text(y=(NSCEN-1)+0.2,x=ninefive[1,(NSCEN-1)],'95%',col='navyblue',adj=c(-0,-0.3*sp_index))
legend(col=col_city, lty=1,bty='n',x= mean(means),legend=cityname,y=NSCEN-1,lwd=2)
par(par_city)
}
dev.off()
}
# plot level 1 results per 100k - one plot for all cities plus individual plots for each city
{pdf(paste0('results/voi/city_yll_level1_100k_',output_version,'.pdf'),height=6,width=6)
# one plot for all cities - might be difficult to read if too many cities
par <- par(mar=c(5,5,1,1))
level1_df <- voi_complete_100k_summary_df %>% filter(age_sex == 'all all') %>% dplyr::select(city, value_type, matches('level1'))
means <- as.matrix(unlist(transpose(level1_df %>% filter(value_type == 'mean') %>% dplyr::select(matches('level1')) )))
ninefive <- list()
for (cityname in cities){
ninefive[[cityname]] <- as.matrix(level1_df %>% filter(city == cityname & (value_type == '2.5perc' | value_type == '97.5perc')) %>% dplyr::select(matches('level1')))
}
yvals <- rep(1:length(cities),each=NSCEN)/10 + rep(1:NSCEN,times=length(cities))
cols <- rainbow(length(cities))
plot(as.vector(means),yvals,pch=16,cex=1,frame=F,ylab='',xlab='Change in total YLL relative to baseline per 100k - Level 1',
col=rep(cols,each=NSCEN),yaxt='n',xlim=range(unlist(ninefive)))
axis(2,las=2,at=(1+0.1):(NSCEN+0.1),labels=SCEN_SHORT_NAME[2:length(SCEN_SHORT_NAME)])
for(i in 1:length(cities)) for(j in 1:NSCEN) lines(ninefive[[i]][,j],rep(yvals[j+(i-1)*NSCEN],2),lwd=2,col=cols[i])
abline(v=0,col='grey',lty=2,lwd=2)
text(y=(NSCEN-1)+0.2,x=ninefive[[1]][1,(NSCEN-1)],'95%',col='navyblue',adj=c(-0,-0.3*1))
legend(col=rev(cols),lty=1,bty='n',x= mean(means),legend=rev(cities),y=NSCEN-1,lwd=2)
par(par)
# one plot per city
for(cityname in cities){
sp_index <- which(cities==cityname)
scen_out_city <- level1_df %>% filter(city == cityname)
means <- as.matrix(unlist(transpose(scen_out_city %>% filter(value_type == 'mean') %>% dplyr::select(matches('level1')) )))
ninefive <- as.matrix(scen_out_city %>% filter(city == cityname & (value_type == '2.5perc' | value_type == '97.5perc')
) %>% dplyr::select(matches('level1')))
yvals <- rep(1,each=NSCEN)/10 + rep(1:NSCEN)
cols <- rainbow(length(cities))
col_city <- cols[sp_index]
par_city <- par(mar=c(5,5,1,1))
xlab <- paste0(cityname,': Change in total YLL per 100k relative to baseline \n - Level 1')
plot(as.vector(means),yvals,pch=16,cex=1,frame=F,ylab='',xlab=xlab,col=rep(col_city,each=NSCEN),
yaxt='n',xlim=range(unlist(ninefive)))
axis(2,las=2,at=(1+0.1):(NSCEN+0.1),labels=SCEN_SHORT_NAME[2:length(SCEN_SHORT_NAME)])
for(j in 1:NSCEN) lines(ninefive[,j],rep(yvals[j],2),lwd=2,col=col_city)
abline(v=0,col='grey',lty=2,lwd=2)
text(y=(NSCEN-1)+0.2,x=ninefive[1,(NSCEN-1)],'95%',col='navyblue',adj=c(-0,-0.3*sp_index))
legend(col=col_city, lty=1,bty='n',x= mean(means),legend=cityname,y=NSCEN-1,lwd=2)
par(par_city)
}
dev.off()
}
# plot total YLL per 100k for 3 levels for all scenarios - one plot per city
{pdf(paste0('results/voi/city_yll_all_levels_100k_',output_version,'.pdf'),height=6,width=6)
# one plot per city
for(cityname in cities){
sp_index <- which(cities==cityname)
# extract information for all 3 levels
scen_out_city <- voi_complete_100k_summary_df %>% filter(age_sex == 'all all' & city == cityname
) %>% dplyr::select(city, value_type, matches('level'))
# level 1 means and CI interval values
means <- as.matrix(unlist(transpose(scen_out_city %>% filter(value_type == 'mean') %>% dplyr::select(matches('level1')) )))
ninefive <- as.matrix(scen_out_city %>% filter(city == cityname & (value_type == '2.5perc' | value_type == '97.5perc')
) %>% dplyr::select(matches('level1')))
yvals <- rep(3,each=NSCEN)/10 + rep(1:NSCEN)
# level 2
means_l2 <- as.matrix(unlist(transpose(scen_out_city %>% filter(value_type == 'mean') %>% dplyr::select(matches('level2')) )))
ninefive_l2 <- as.matrix(scen_out_city %>% filter(city == cityname & (value_type == '2.5perc' | value_type == '97.5perc')
) %>% dplyr::select(matches('level2')))
yvals_l2<- rep(2,each=NSCEN)/10 + rep(1:NSCEN)
# level 3
means_l3 <- as.matrix(unlist(transpose(scen_out_city %>% filter(value_type == 'mean') %>% dplyr::select(matches('level3')) )))
ninefive_l3 <- as.matrix(scen_out_city %>% filter(city == cityname & (value_type == '2.5perc' | value_type == '97.5perc')
) %>% dplyr::select(matches('level3')))
yvals_l3 <- rep(1,each=NSCEN)/10 + rep(1:NSCEN)
par_city <- par(mar=c(5,7,1,1))
xlab <- paste0(cityname,': Change YLL per 100k relative to baseline for each level')
plot(as.vector(means),yvals,pch=16,cex=1,frame=F,ylab='',xlab=xlab,col='black',
yaxt='n', ylim = range(.9,4.2),xlim=range(unlist(ninefive),unlist(ninefive_l2),unlist(ninefive_l3)))
axis(2,las=2,at= seq(1.2, 1.05*NSCEN + 0.2, by = 1.05)
,labels=SCEN_SHORT_NAME[2:length(SCEN_SHORT_NAME)])
points(as.vector(means_l2),yvals_l2,pch=16,cex=1,col='blue')
points(as.vector(means_l3),yvals_l3,pch=16,cex=1,col='red')
for(j in 1:NSCEN){
lines(ninefive[,j],rep(yvals[j],2),lwd=2,col='black')
lines(ninefive_l2[,j],rep(yvals_l2[j],2),lwd=2, col='blue')
lines(ninefive_l3[,j],rep(yvals_l3[j],2),lwd=2, col='red')
}
abline(v=0,col='grey',lty=2,lwd=2)
text(y=(NSCEN-1)+0.4,x=ninefive[1,(NSCEN-1)],'95%',col='black',adj=c(-0,-0.3*sp_index))
legend(col='black', lty=1,bty='n',x= mean(means),legend=paste0(cityname,': Level 1'),y=NSCEN-1,lwd=2, cex = 0.8)
legend(col='blue', lty=1,bty='n',x= mean(means),legend=paste0(cityname,': Level 2'),y=NSCEN-1.1,lwd=2, cex = 0.8)
legend(col='red', lty=1,bty='n',x= mean(means),legend=paste0(cityname,': Level 3'),y=NSCEN-1.2,lwd=2, cex = 0.8)
par(par_city)
}
dev.off()
}
# plot total YLL per 100k for 3 levels, both sexes and for all scenarios - one plot per city
{pdf(paste0('results/voi/city_yll_all_levels_sex_100k_',output_version,'.pdf'),height=6,width=6)
# one plot per city
for(cityname in cities){
sp_index <- which(cities==cityname)
# extract information for all 3 levels
scen_out_city <- voi_complete_100k_summary_df %>% filter(age_cat == 'all' & city == cityname
) %>% dplyr::select(city, sex, value_type, matches('level'))
# level 1 means and CI interval values
means <- as.matrix(unlist(transpose(scen_out_city %>% filter(value_type == 'mean' & sex == 'all') %>% dplyr::select(matches('level1')) )))
ninefive <- as.matrix(scen_out_city %>% filter(city == cityname & sex == 'all' & (value_type == '2.5perc' | value_type == '97.5perc')
) %>% dplyr::select(matches('level1')))
yvals <- rep(6,each=NSCEN)/10 + rep(1:NSCEN)
# level 1 means and CI interval values - male
means_m <- as.matrix(unlist(transpose(scen_out_city %>% filter(value_type == 'mean' & sex == 'male') %>% dplyr::select(matches('level1')) )))
ninefive_m <- as.matrix(scen_out_city %>% filter(city == cityname & sex == 'male' & (value_type == '2.5perc' | value_type == '97.5perc')
) %>% dplyr::select(matches('level1')))
yvals_m <- rep(5.5,each=NSCEN)/10 + rep(1:NSCEN)
# level 1 means and CI interval values - female
means_f <- as.matrix(unlist(transpose(scen_out_city %>% filter(value_type == 'mean' & sex == 'female') %>% dplyr::select(matches('level1')) )))
ninefive_f <- as.matrix(scen_out_city %>% filter(city == cityname & sex == 'female' & (value_type == '2.5perc' | value_type == '97.5perc')
) %>% dplyr::select(matches('level1')))
yvals_f <- rep(5,each=NSCEN)/10 + rep(1:NSCEN)
# level 2
means_l2 <- as.matrix(unlist(transpose(scen_out_city %>% filter(value_type == 'mean' & sex == 'all') %>% dplyr::select(matches('level2')) )))
ninefive_l2 <- as.matrix(scen_out_city %>% filter(city == cityname & sex == 'all' & (value_type == '2.5perc' | value_type == '97.5perc')
) %>% dplyr::select(matches('level2')))
yvals_l2<- rep(4,each=NSCEN)/10 + rep(1:NSCEN)
# level 2 - male
means_l2_m <- as.matrix(unlist(transpose(scen_out_city %>% filter(value_type == 'mean' & sex == 'male') %>% dplyr::select(matches('level2')) )))
ninefive_l2_m <- as.matrix(scen_out_city %>% filter(city == cityname & sex == 'male' & (value_type == '2.5perc' | value_type == '97.5perc')
) %>% dplyr::select(matches('level2')))
yvals_l2_m<- rep(3.5,each=NSCEN)/10 + rep(1:NSCEN)
# level 2 - female
means_l2_f <- as.matrix(unlist(transpose(scen_out_city %>% filter(value_type == 'mean' & sex == 'female') %>% dplyr::select(matches('level2')) )))
ninefive_l2_f <- as.matrix(scen_out_city %>% filter(city == cityname & sex == 'female' & (value_type == '2.5perc' | value_type == '97.5perc')
) %>% dplyr::select(matches('level2')))
yvals_l2_f<- rep(3,each=NSCEN)/10 + rep(1:NSCEN)
# level 3
means_l3 <- as.matrix(unlist(transpose(scen_out_city %>% filter(value_type == 'mean' & sex == 'all') %>% dplyr::select(matches('level3')) )))
ninefive_l3 <- as.matrix(scen_out_city %>% filter(city == cityname & sex == 'all' & (value_type == '2.5perc' | value_type == '97.5perc')
) %>% dplyr::select(matches('level3')))
yvals_l3 <- rep(2,each=NSCEN)/10 + rep(1:NSCEN)
# level 3 - male
means_l3_m <- as.matrix(unlist(transpose(scen_out_city %>% filter(value_type == 'mean' & sex == 'male') %>% dplyr::select(matches('level3')) )))
ninefive_l3_m <- as.matrix(scen_out_city %>% filter(city == cityname & sex == 'male' & (value_type == '2.5perc' | value_type == '97.5perc')
) %>% dplyr::select(matches('level3')))
yvals_l3_m <- rep(1.5,each=NSCEN)/10 + rep(1:NSCEN)
# level 3 - female
means_l3_f <- as.matrix(unlist(transpose(scen_out_city %>% filter(value_type == 'mean' & sex == 'female') %>% dplyr::select(matches('level3')) )))
ninefive_l3_f <- as.matrix(scen_out_city %>% filter(city == cityname & sex == 'female' & (value_type == '2.5perc' | value_type == '97.5perc')
) %>% dplyr::select(matches('level3')))
yvals_l3_f <- rep(1,each=NSCEN)/10 + rep(1:NSCEN)
par_city <- par(mar=c(5,7,1,1))
xlab <- paste0(cityname,': Change YLL per 100k relative to baseline for each level and sex')
plot(as.vector(means),yvals,pch=16,cex=1,frame=F,ylab='',xlab=xlab,col='black',
yaxt='n', ylim = range(.9,4.2),xlim=range(unlist(ninefive),unlist(ninefive_m),unlist(ninefive_f),
unlist(ninefive_l2), unlist(ninefive_l2_m), unlist(ninefive_l2_f),
unlist(ninefive_l3), unlist(ninefive_l3_m), unlist(ninefive_l3_f)),
cex.lab = 0.8)
axis(2,las=2,at= seq(1.3, 1.3*NSCEN + 0.2, by = 1.05)
,labels=SCEN_SHORT_NAME[2:length(SCEN_SHORT_NAME)])
# levels
points(as.vector(means_l2),yvals_l2,pch=16,cex=1,col='blue')
points(as.vector(means_l3),yvals_l3,pch=16,cex=1,col='red')
# male
points(as.vector(means_m),yvals_m,pch=16,cex=1,col='grey39')
points(as.vector(means_l2_m),yvals_l2_m,pch=16,cex=1,col='cornflowerblue')
points(as.vector(means_l3_m),yvals_l3_m,pch=16,cex=1,col='coral1')
# female
points(as.vector(means_f),yvals_f,pch=16,cex=1,col='grey')
points(as.vector(means_l2_f),yvals_l2_f,pch=16,cex=1,col='dodgerblue')
points(as.vector(means_l3_f),yvals_l3_f,pch=16,cex=1,col='orange')
for(j in 1:NSCEN){ # levels both sexes
lines(ninefive[,j],rep(yvals[j],2),lwd=2,col='black')
lines(ninefive_l2[,j],rep(yvals_l2[j],2),lwd=2, col='blue')
lines(ninefive_l3[,j],rep(yvals_l3[j],2),lwd=2, col='red')
}
for(j in 1:NSCEN){ # levels for males
lines(ninefive_m[,j],rep(yvals_m[j],2),lwd=2,col='grey39', lty = 1)
lines(ninefive_l2_m[,j],rep(yvals_l2_m[j],2),lwd=2, col='cornflowerblue', lty = 1)
lines(ninefive_l3_m[,j],rep(yvals_l3_m[j],2),lwd=2, col='coral1', lty = 1)
}
for(j in 1:NSCEN){ # levels for females
lines(ninefive_f[,j],rep(yvals_f[j],2),lwd=2,col='grey', lty = 1)
lines(ninefive_l2_f[,j],rep(yvals_l2_f[j],2),lwd=2, col='dodgerblue', lty = 1)
lines(ninefive_l3_f[,j],rep(yvals_l3_f[j],2),lwd=2, col='orange', lty = 1)
}
abline(v=0,col='grey',lty=2,lwd=2)
text(y=(NSCEN-1)+0.7,x=ninefive[1,(NSCEN-1)],'95%',col='black',adj=c(-0,-0.3*sp_index))
starting_y = 3
legend(col='black', lty=1,bty='n',x= mean(means),legend=paste0(cityname,': Level 1'),y=starting_y,lwd=2, cex = 0.8)
legend(col='grey39', lty=1,bty='n',x= mean(means),legend=paste0(cityname,': Level 1 - male'),y=starting_y-0.1,lwd=2, cex = 0.8)
legend(col='grey', lty=1,bty='n',x= mean(means),legend=paste0(cityname,': Level 1 - female'),y=starting_y-0.2,lwd=2, cex = 0.8)
legend(col='blue', lty=1,bty='n',x= mean(means),legend=paste0(cityname,': Level 2'),y=starting_y-0.4,lwd=2, cex = 0.8)
legend(col='cornflowerblue', lty=1,bty='n',x= mean(means),legend=paste0(cityname,': Level 2 - male'),y=starting_y-0.5,lwd=2, cex = 0.8)
legend(col='dodgerblue', lty=1,bty='n',x= mean(means),legend=paste0(cityname,': Level 2 - female'),y=starting_y-0.6,lwd=2, cex = 0.8)
legend(col='red', lty=1,bty='n',x= mean(means),legend=paste0(cityname,': Level 3'),y=starting_y-0.8,lwd=2, cex = 0.8)
legend(col='coral1', lty=1,bty='n',x= mean(means),legend=paste0(cityname,': Level 3 - male'),y=starting_y-0.9,lwd=2, cex = 0.8)
legend(col='orange', lty=1,bty='n',x= mean(means),legend=paste0(cityname,': Level 3 - female'),y=starting_y-1,lwd=2, cex = 0.8)
par(par_city)
}
dev.off()
}
# plot total YLL per 100k for level 1, ap_pa_all_cause and inj, both sexes and for all scenarios - one plot per city
{pdf(paste0('results/voi/city_yll_level1_inj_allcause_sex_100k_',output_version,'.pdf'),height=6,width=6)
# one plot per city
for(cityname in cities){
sp_index <- which(cities==cityname)
# extract information for all 3 levels
scen_out_city <- voi_complete_100k_summary_df %>% filter(age_cat == 'all' & city == cityname
) %>% dplyr::select(city, sex, value_type, matches('level1|inj|pa_ap_all_cause'))
# level 1 means and CI interval values
means <- as.matrix(unlist(transpose(scen_out_city %>% filter(value_type == 'mean' & sex == 'all') %>% dplyr::select(matches('level1')) )))
ninefive <- as.matrix(scen_out_city %>% filter(city == cityname & sex == 'all' & (value_type == '2.5perc' | value_type == '97.5perc')
) %>% dplyr::select(matches('level1')))
yvals <- rep(6,each=NSCEN)/10 + rep(1:NSCEN)
# level 1 means and CI interval values - male
means_m <- as.matrix(unlist(transpose(scen_out_city %>% filter(value_type == 'mean' & sex == 'male') %>% dplyr::select(matches('level1')) )))
ninefive_m <- as.matrix(scen_out_city %>% filter(city == cityname & sex == 'male' & (value_type == '2.5perc' | value_type == '97.5perc')
) %>% dplyr::select(matches('level1')))
yvals_m <- rep(5.5,each=NSCEN)/10 + rep(1:NSCEN)
# level 1 means and CI interval values - female
means_f <- as.matrix(unlist(transpose(scen_out_city %>% filter(value_type == 'mean' & sex == 'female') %>% dplyr::select(matches('level1')) )))
ninefive_f <- as.matrix(scen_out_city %>% filter(city == cityname & sex == 'female' & (value_type == '2.5perc' | value_type == '97.5perc')
) %>% dplyr::select(matches('level1')))
yvals_f <- rep(5,each=NSCEN)/10 + rep(1:NSCEN)
# pa_ap_all_cause
means_l2 <- as.matrix(unlist(transpose(scen_out_city %>% filter(value_type == 'mean' & sex == 'all') %>% dplyr::select(matches('pa_ap_all_cause')) )))
ninefive_l2 <- as.matrix(scen_out_city %>% filter(city == cityname & sex == 'all' & (value_type == '2.5perc' | value_type == '97.5perc')
) %>% dplyr::select(matches('pa_ap_all_cause')))
yvals_l2<- rep(4,each=NSCEN)/10 + rep(1:NSCEN)
# pa_ap_all_cause - male
means_l2_m <- as.matrix(unlist(transpose(scen_out_city %>% filter(value_type == 'mean' & sex == 'male') %>% dplyr::select(matches('pa_ap_all_cause')) )))
ninefive_l2_m <- as.matrix(scen_out_city %>% filter(city == cityname & sex == 'male' & (value_type == '2.5perc' | value_type == '97.5perc')
) %>% dplyr::select(matches('pa_ap_all_cause')))
yvals_l2_m<- rep(3.5,each=NSCEN)/10 + rep(1:NSCEN)
# pa_ap_all_cause - female
means_l2_f <- as.matrix(unlist(transpose(scen_out_city %>% filter(value_type == 'mean' & sex == 'female') %>% dplyr::select(matches('pa_ap_all_cause')) )))
ninefive_l2_f <- as.matrix(scen_out_city %>% filter(city == cityname & sex == 'female' & (value_type == '2.5perc' | value_type == '97.5perc')
) %>% dplyr::select(matches('pa_ap_all_cause')))
yvals_l2_f<- rep(3,each=NSCEN)/10 + rep(1:NSCEN)
# inj
means_l3 <- as.matrix(unlist(transpose(scen_out_city %>% filter(value_type == 'mean' & sex == 'all') %>% dplyr::select(matches('inj')) )))
ninefive_l3 <- as.matrix(scen_out_city %>% filter(city == cityname & sex == 'all' & (value_type == '2.5perc' | value_type == '97.5perc')
) %>% dplyr::select(matches('inj')))
yvals_l3 <- rep(2,each=NSCEN)/10 + rep(1:NSCEN)
# inj - male
means_l3_m <- as.matrix(unlist(transpose(scen_out_city %>% filter(value_type == 'mean' & sex == 'male') %>% dplyr::select(matches('inj')) )))
ninefive_l3_m <- as.matrix(scen_out_city %>% filter(city == cityname & sex == 'male' & (value_type == '2.5perc' | value_type == '97.5perc')
) %>% dplyr::select(matches('inj')))
yvals_l3_m <- rep(1.5,each=NSCEN)/10 + rep(1:NSCEN)
# inj - female
means_l3_f <- as.matrix(unlist(transpose(scen_out_city %>% filter(value_type == 'mean' & sex == 'female') %>% dplyr::select(matches('inj')) )))
ninefive_l3_f <- as.matrix(scen_out_city %>% filter(city == cityname & sex == 'female' & (value_type == '2.5perc' | value_type == '97.5perc')
) %>% dplyr::select(matches('inj')))
yvals_l3_f <- rep(1,each=NSCEN)/10 + rep(1:NSCEN)
par_city <- par(mar=c(5,7,1,1))
xlab <- paste0(cityname,': Change YLL per 100k relative to baseline for level1, \nall cause mortality and injury, by sex')
plot(as.vector(means),yvals,pch=16,cex=1,frame=F,ylab='',xlab=xlab,col='black',
yaxt='n', ylim = range(.9,4.2),xlim=range(unlist(ninefive),unlist(ninefive_m),unlist(ninefive_f),
unlist(ninefive_l2), unlist(ninefive_l2_m), unlist(ninefive_l2_f),
unlist(ninefive_l3), unlist(ninefive_l3_m), unlist(ninefive_l3_f)),
cex.lab = 0.8)
axis(2,las=2,at= seq(1.3, 1.3*NSCEN + 0.2, by = 1.05)
,labels=SCEN_SHORT_NAME[2:length(SCEN_SHORT_NAME)])