Benchmarking predictive architectures for meningitis forecasting and outbreak detection using environmental drivers - code

Abstract

This repository contains the analytical code for the manuscript, Benchmarking predictive architectures for meningitis forecasting and outbreak detection using environmental drivers. It provides the complete Python and PyTorch codebase to replicate our multi-architecture evaluation of early warning systems for epidemic meningitis across 40 countries within the WHO Regional Office for Africa (AFRO). Repository Contents: Spatiotemporal Deep Learning: PyTorch implementations of the Spatiotemporal Graph Neural Networks (ST-GNN) and the custom Zero-Inflated Negative Binomial Neural Network (ZINB-NN) designed for sparse case counts. Tabular Ensembles: Training and evaluation scripts for the AutoGluon gradient-boosted tabular models, including the two-stage hurdle classifier for binary outbreak detection. Feature Engineering: Spatial processing pipelines generating the deterministic gravity network from Overture Maps building footprints, alongside climate covariates like the binary Harmattan extreme flag. Evaluation Framework: Scripts calculating continuous magnitude estimation (R²) and binary alert detection (PR-AUC) across feature ablations.