Multi-Country Model Application for RSV Cost-Effectiveness Policy (McMarcel)

README: RSV modelling project McMarcel The objective of this modelling project is to evaluate the impact and cost-effectiveness of potential maternal and neonatal RSV immunisation strategies in 72 Gavi countries. The acronym stands for M ultiC ountry Model Application for RSV Cost-Effectiveness poLicy (McMarcel). This software is distributed under the terms of the Attribution 4.0 International (CC BY 4.0) licence. If you use (parts of) this modelling project, please cite: Li, Willem, Antillon, Bilcke, Jit, Beutels. Health and economic burden of Respiratory Syncytial Virus (RSV) disease and the cost-effectiveness of potential interventions against RSV among children under 5 years in 72 Gavi-eligible countries. BMC Medicine. (2020) Where to start? We organised our R-project upon RSV_main.R , which is the workbench to coordinate the model input, the number of runs, random seeds,. . . and all output. Please make sure that your Working Directory is specified as the location of this R file, since all links in de code are relative to the location of this script. We included RSV_main.pdf with syntax highlighting to get started. Where are my results stored? During the execution of the model (by using the RSV_main script), an output directory will be created. Each run will create a separate sub-directory to store all results. How to specify interventions and countries? The main script uses a csv file in the “config” directory, of which each row leads to one cost-beneft analysis for a specific country, intervention, coverage, etc. Simulation results are grouped per “config_tag” to create figures and tables. Main directory Directory or file name Content config Directory with model configurations by country, year, coverage, duration of protection, efficacy and intervention (maternal or mAb) functions Directory with R help functions input Directory with input data to run the analysis for 72 Gavi countries README.pdf Readme file with the project introduction and reference RSV_main.R Main script to run the cost-effectiveness analysis RSV_main.pdf Main script with syntax highlighting to get started LICENCE.txt GNU GENERAL PUBLIC LICENSE Version 3 All code is tested with R Version 3.5.3 on MacOS 10.15.2 and R Version 3.5.1 on CentOS Linux 7. 1 Input files File name Content cfr_lmic_ts_n5000.csv RSV case fatality rate estimations for hospitalised RSV cases, based on data from studies in LMIC countries, represented by 5000x tin-plate splines. country_details_gavi72.csv Country data such as, ISO3 code, WHO income region, target population (newborns) and stillbirth rate cost_inpatient.csv Estimated RSV inpatient cost based on Zhang et al and WHO unit costs, represented by 5000 samples from a meta-analysis. cost_outpatient.csv Estimated RSV outpatient cost based on Zhang et al, WHO unit costs and health care seeking behavior, represented by 5000 samples from a meta-analysis. hosp_prob_ts_n5000.csv RSV hospital probability estimations for RSV cases, based on data from Nokes et al, represented by 5000 tin-plate splines (Default) hosp_prob_ts_n5000_pooled.csv RSV hospital probability estimations for RSV cases, based on data from Nokes et al and Homaira et al, represented by 5000 tin-plate splines (Sensitivity Analysis) incidence_lmic_ts_n5000.csv RSV incidence estimations, based on data from LMIC countries, represented by 5000 tin-plate splines natural_earth_data Directory with free vector and raster map data from www.naturalearthdata.com RSV_burden_Shi_2017.csv Country-specific RSV related burden of disease, derived from the systematic review by Shi et al (2017) More details are provided in the main text and additional file of the reference above. Dependencies R package Reason countrycode To convert country names into iso3 codes doParallel To run foreach loops in parallel dplyr To aggregate simulation results ggplot2 To make fancy plots mgcv To use the gam function in the EVPI code rgdal To plot results on Geospatial scales scales To use transparent colors wpp2017 To obtain population data from from the United Nations World Population Prospects 2017 Team •Lander Willem ([email protected]) •Xiao Li •Marina Antillon •Joke Bilcke •Mark Jit •Philippe Beutels Last update: February 12, 2020 2