/
run_standard.R
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run_standard.R
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## Initialize the workspace
setwd("~/reff-covid-epifx-aus-2020-dev")
rm(list = ls())
library(tidyverse)
library(lubridate)
library(future.callr)
library(listenv)
library(furrr)
# The detection probability cutoff for daily case counts.
cutoff_threshold <- 0.95
### Downloading latest data
## This includes the latest local_cases_input, r_eff_12_local_samples, external exposures,
## as well as the vaccination time-series from the Curtain model
# source("interface_functions/dropbox.R")
source("interface_functions/data.R")
source("interface_functions/mediaflux_fetch.R")
dbx_files <- tribble(
~remote_file, ~local_file,
"/covid_output/local_cases_input.csv", "data/in/local_cases_input.csv",
"/covid_output/projection/r_eff_12_local_samples.csv", "data/in/r_eff_proj_samples.csv",
"/covid_output/epifx_in/external_exposures_all.csv", "data/in/external_exposures_all.csv",
)
# And then download all of them
# download_files(dbx_files)
# Download the vaccination files from mediaflux
# source("interface_functions/mediaflux.R")
# sync_latest_mediaflux()
# Download the Delta and Omicron VE data from dropbox
# download_files(get_vaccine_files())
# Download Reff samples, local cases, and vaccination effects.
mediaflux_fetch()
# Create a 'run_name' that we use to label our results
# This is the date of the latest case data date with pr_detect > 0.5
forecasting_dates <- get_forecast_dates("data/in/local_cases_input.csv", cutoff_threshold)
run_name <- forecasting_dates$date_last_onset_50 %>% format("%Y-%m-%d")
### Processing vaccine data
use_original_method <- FALSE
if (use_original_method) {
source("interface_functions/vaccination.R")
produce_vaccination_inputs("data/in/effective_dose_data.csv",
"data/in/vaccine_effect_timeseries.csv",
run_name)
} else {
source("interface_functions/vaccination_dummy.R")
produce_interim_vaccination_inputs("data/in/vaccine_effect_timeseries.csv")
}
### Processing R_eff, case counts:
# Read in the local cases file and split into SSV files for each state
# NOTE: change detection threshold to 95% for the forecasts run on 2022-01-20.
process_local_cases("data/in/local_cases_input.csv",
pr_detect_threshold = cutoff_threshold)
# pr_detect_threshold = 0.5)
# As above, for our external exposures listing
# NOTE: manually add SA exposures on 2021-11-18.
# NOTE: manually add TAS exposures on 2021-12-04.
# NOTE: manually add WA exposures on 2021-12-14.
# process_external_exposures("data/in/external_exposures_all.csv")
### Defining our different scenarios for ensembling:
args_with_reversion <- tribble(~key, ~value,
"%exps_dir%", "exps/with_reversion",
"%reffs_dir%", "with_reversion")
args_no_reversion <- tribble(~key, ~value,
"%exps_dir%", "exps/no_reversion",
"%reffs_dir%", "no_reversion")
args_back_to_school <- tribble(~key, ~value,
"%exps_dir%", "exps/back_to_school",
"%reffs_dir%", "back_to_school")
args_covidlive <- tribble(~key, ~value,
"%exps_dir%", "exps/covidlive",
"%reffs_dir%", "covidlive")
## Different case-ascertainment scenarios from 2021-12-01.
args_ascertain_125 <- tribble(~key, ~value,
"%exps_dir%", "exps/ascertain_12.5",
"%reffs_dir%", "ascertain_12.5")
args_ascertain_250 <- tribble(~key, ~value,
"%exps_dir%", "exps/ascertain_25.0",
"%reffs_dir%", "ascertain_25.0")
args_ascertain_375 <- tribble(~key, ~value,
"%exps_dir%", "exps/ascertain_37.5",
"%reffs_dir%", "ascertain_37.5")
scenarios <- tribble(
~name, ~args, ~reff_truncation_date,
# "with_reversion", list(args_with_reversion), ymd(NA),
# "no_reversion", list(args_no_reversion), forecasting_dates$date_last_infection_50,
"back_to_school", list(args_back_to_school), forecasting_dates$date_last_infection_50,
"ascertain_12.5", list(args_ascertain_125), forecasting_dates$date_last_infection_50,
"ascertain_25.0", list(args_ascertain_250), forecasting_dates$date_last_infection_50,
"ascertain_37.5", list(args_ascertain_375), forecasting_dates$date_last_infection_50,
# "covidlive", list(args_covidlive), forecasting_dates$date_last_infection_50,
) %>%
rowwise() %>%
mutate(exps_dir = args[[1]] %>% filter(key == "%exps_dir%") %>% pull(value))
# Produce C1/2 trajectories, with truncation at reff_truncation_date, if that is not NULL
for(i_scen in 1:nrow(scenarios)) {
scenario <- scenarios[i_scen,]
dir.create(paste0("data/",scenario$name), showWarnings = FALSE)
process_reff_trajs(
reff_proj_file = "data/in/r_eff_proj_samples.csv",
vaccine_effect_file = "data/in/vaccine_effect_timeseries.csv",
output_path = paste0("data/",scenario$name),
truncation_date = scenario$reff_truncation_date)
}
source("interface_functions/epifx.R")
run_forecast <- function(name, args, reff_truncation_date, exps_dir) {
print(paste0("Running scenario forecast ", name))
dir.create(exps_dir, recursive = TRUE, showWarnings = FALSE)
file.remove(list.files(exps_dir, full.names = TRUE))
scen_template_file <- paste0("epifx_params/forecast_", name, ".toml")
# Create a custom TOML file for this scenario:
create_forecast_file("epifx_params/forecast_template.toml",
args[[1]],
scen_template_file)
system(paste0(venv_prefix, "python3 run_forecast.py ", "--ff ", scen_template_file))
}
### Running the forecast across our scenarios:
plan(callr)
future_pmap(scenarios, run_forecast)
# Plotting functions
source("forecast_plotting/plot_parameter_CIs.R")
source("forecast_plotting/plot_parameter_CIs_by_name.R")
source("forecast_plotting/plot_incidence.R")
source("forecast_plotting/plotting_common.R")
source("forecast_plotting/produce_CI_plots.R")
source("forecast_plotting/produce_state_summaries.R")
source("forecast_plotting/plot_vaccine_effect.R")
source("interface_functions/ensemble_samples.R")
job_list <- listenv()
for(i_scen in 1:nrow(scenarios)) {
scenario <- scenarios[i_scen,]
plot_subdir <- paste0("results/", run_name, "/", scenario$name)
# Load in basic information about exps in forecast_exps_dir
plotting_data <- get_plotting_data(scenario$exps_dir)
dir.create(plot_subdir, recursive = TRUE, showWarnings = FALSE)
job_list[[length(job_list) + 1]] %<-% { create_ensemble_samples(scenario$name, run_name, plotting_data) }
job_list[[length(job_list) + 1]] %<-% { produce_CI_plots(plotting_data, plot_subdir) }
job_list[[length(job_list) + 1]] %<-% { produce_state_summaries(plotting_data, plot_subdir) }
job_list[[length(job_list) + 1]] %<-% { plot_vaccine_effect(plotting_data, plot_subdir,
"data/in/vaccine_effect_timeseries.csv") }
}
# Run every job in parallel now
job_list <- as.list(job_list)
### Upload ensemble samples
ensembles_for_upload <- str_c("ensemble_samples/",
"moss_forecast_samples_vacc_",
scenarios$name,
"_", run_name, ".csv")
for(i_file in ensembles_for_upload) {
print(paste0("Uploading ", i_file))
system(paste0("bash upload.sh ", i_file))
}
### Upload plots
source("interface_functions/dropbox.R")
upload_plot_files(paste0("results/", run_name))
### Backup our exps directory in case we ever care about that!
source("interface_functions/spartan_backup.R")
backup_exps_files(run_name)