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Source files used to study malaria in Burkina Faso using the spatial individual-based modeling.

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PSU-CIDD-Burkina-Faso

This repository contains configuration, GIS, analysis files used for modeling the prevalence of malaria (P. falciparum) in Burkina Faso. These configurations are primarily intended to study 1) the development of antimalarial resistance in the parasite, and 2) possible interventions that the National Malaria Control Programme can implement.

The origin repository for the simulation can be found at maciekboni/PSU-CIDD-Malaria-Simulation, although the studies in this repository are intended to be run against 4.0.0 and 4.0.x versions that can be found under rjzupkoii/PSU-CIDD-Malaria-Simulation.

Organization

  • Analysis contains Scripts and data related to analysis subtasks (ex., movement calibration).

  • GIS contains the GIS files for Burkina Faso, as prepared for the simulation.

  • Plots contains the MATLAB and Python scripts used to generate analysis and manuscript plots.

  • Replicates contains the scripts and files needed to run the replicates on the ACI cluster.

  • Studies contains the configuration files used for model validation and de novo mutation simulations (business as usual, private market elimination, and multiple-first line therapies).

Model Execution

All simulations were performed on the Pennsylvania State University’s Institute for Computational and Data Sciences’ Roar supercomputer using the configurations present in Studies and the replicate queuing scripts present in Studies/Replicates.

Model Validation

The model validation can be checked using the plot_validation.m MATLAB function in PSU-CIDD-MaSim-Support with the following parameters:

plot_validation('cases', 'data/bfa-17576-verification-data.csv', 'data/bfa-weighted_pfpr.csv', 'ci', [605 360 990], 'treated', 0.832)

Sources

Adam Auton (2021). Red Blue Colormap (https://www.mathworks.com/matlabcentral/fileexchange/25536-red-blue-colormap), MATLAB Central File Exchange. Retrieved August 9, 2021.

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Source files used to study malaria in Burkina Faso using the spatial individual-based modeling.

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