Integrating Mathematical Approaches to Quantify Uncertainty in Infectious Disease Models Using a Reproducible R and Python Workflow and Synthetic and Real Surveillance Data

Description This repository accompanies the manuscript "Integrating Mathematical Approaches to Quantify Uncertainty in Infectious Disease Models Using a Reproducible R and Python Workflow and Synthetic and Real Surveillance Data." The repository provides a fully reproducible implementation of a five-stage uncertainty quantification workflow for infectious disease modelling in both R and Python. The workflow integrates four complementary uncertainty quantification approaches: Sobol global sensitivity analysis, Bayesian Markov chain Monte Carlo (MCMC) calibration, Bayesian model averaging (BMA) for structural uncertainty, and Gillespie stochastic simulation. The repository includes: Complete R and Python source code implementing the full workflow. Synthetic datasets used for validation and benchmarking. Real surveillance datasets and analysis scripts where redistribution is permitted, or instructions for obtaining the original data. Scripts for data processing, model fitting, sensitivity analysis, stochastic simulation, Bayesian model averaging, uncertainty decomposition, and visualisation. Reproducible figures and tables presented in the manuscript. Documentation describing the workflow and instructions for reproducing all analyses. The workflow is first validated using synthetic SEIR epidemic data with known parameters before being applied to publicly available surveillance data from Malawi's 2021 SARS-CoV-2 Delta wave. To demonstrate generalisability, the framework is further adapted and applied to cholera, rabies, antenatal syphilis, and schistosomiasis datasets from Malawi. This repository is intended as an open educational and research resource for epidemiologists, infectious disease modellers, statisticians, data scientists, and public health researchers interested in reproducible uncertainty quantification for mathematical infectious disease models. Keywords: infectious disease modelling, uncertainty quantification, Bayesian inference, Bayesian model averaging, MCMC, Sobol sensitivity analysis, stochastic simulation, R, Python, reproducible research, epidemiology, Malawi.