ImpactSimul

ImpactSimul is an Agent Based Model aimed at helping estimate the impact of an intervention in an HIV program, using a simple and tractable approach. It was first developed to estimate the impact to be expected from Solthis’ Empower II program. It is written in R.

Installation

Install the R package

You can install ImpactSimul from GitHub just by running:

# install.packages("devtools")
devtools::install_github("grlurton/ImpactSimul")

Install Python

To obtain epidemiological data on a country, ImpactSim uses a Python script that queries the UNAIDS database and gets standardized data. To get this running, we need to set-up a python environment and download chromedriver. You shouldn’t have to write any line of Python and will use the script unaids_scrap.py which you can find under /utils in in this repo which you can put into the utils directory in your project’s folder..

We suggest installing Python using Anaconda. 1. Go to https://www.anaconda.com/products/individual 2. Download the appropriate installer for your operating system. 3.Just double-click the downloaded file. In most cases, you can just keep all default options in the installer.

Load the conda environment

To install the Python libraries you’ll need for the extraction, you just need to install the conda environment ImpactSimulEnv.yml which you can find under /utils in this repo.

Once you have downloaded it, just open a terminal in the directory you have downloaded it in, and run the following code.

conda env create -f ImpactSimulEnv.yml -n ImpactSimul

Install ChromeDriver

1. Install the Chrome web browser, available on https://www.google.com/chrome/
2. Download ChromeDriver from https://chromedriver.chromium.org/downloads . Make sure you pick the driver corresponding to your version of the Chrome browser.
3. Unzip ChromeDriver, and move the file into the utils directory in your project’s folder

Example

library(ImpactSimul)
library("readxl")
library(dplyr)
library(yaml)
library(reticulate)

download_data <- TRUE
run_life_table <- TRUE
n_sim <- 1000
parameters_folder <-
time.unit <- 7

###############################
##### SET UP THE LIFE TABLE ###
###############################

if(run_life_table){
  lt_male <- read_excel("data/lifeTables_SierraLeone.xlsx", sheet = "Male")
  lt_female <- read_excel("data/lifeTables_SierraLeone.xlsx", sheet = "Male")
  lt <- prep_life_tables(lt_male, lt_female, "data/ltSierraLeone.rds")
}

if(download_data){
  extract_unaids("ImpactSimul", "utils/unaids_scrap.py", "Sierra Leone", "utils/chromedriver", "data/unaids_estimates/")
  }

parameters_bio <- yaml.load_file("params/parameters_bio.yaml")

list_scenarios <- create_scenario_list()


#############################
### Loading external data ###
#############################

# estimates UNAIDS
data <- readRDS("data/unaids_estimates/SierraLeone/UNAIDS_estimates_SierraLeone.rds")
prop_male <- data$MeanNum[grepl(pattern = "Men aged 15 and over newly infected with HIV ", 
                                x = data$indic)]/data$MeanNum[grepl(pattern = "Adults aged 15 and over newly infected with HIV", 
                                                                    x = data$indic)]

#########################
### Loading functions ###
#########################
param <- params(time.unit = time.unit)

init <- init(i.prev.male = 1,
             i.prev.feml = 1,
             max.inf.time = 15 * 365,
             n=param$N)

for(scenario_simulation in names(list_scenarios)){
  print(scenario_simulation)
  assign(paste0("result_", scenario_simulation), run_simulations(init, param, scenario_simulation, intervention_start = 0, prop_male, nsteps = 52 * 5, nsim = 100))
}

## Simulation results for the different scenarios

res_0 <- summary_outcomes(result_parameters_baseline)
res_1 <- summary_outcomes(result_parameters_intervention)

## DALYs comparison for the different scenarios

DALYs_0 <- calculate_DALYs(res_0, parameters_bio, param)
DALYs_1 <- calculate_DALYs(res_1, parameters_bio, param)



hist(sample(DALYs_0$DALYs, 10000, replace=T) - sample(DALYs_1$DALYs, 10000, replace=T), 
     main = "DALYS averted")

## mean effect and 95% empirical confidence interval

c(mean(DALYs_0$DALYs - DALYs_1$DALYs),
  quantile(DALYs_0$DALYs - DALYs_1$DALYs, .025),
  quantile(DALYs_0$DALYs - DALYs_1$DALYs, .975)
  )