Explainable Graph Neural Networks for Multimodal Malaria Risk Prediction Using Malaria Atlas Project (MAP)-Inspired Synthetic Data and Climate Indicators in Sub-Saharan Africa

Abstract

Malaria remains one of the most significant infectious disease burdens in Sub-Saharan Africa, placing sustained pressure on public health systems and malaria-control programmes. Accurate and interpretable identification of high-risk districts is therefore essential for early warning, resource allocation, and targeted intervention planning. This study proposes an explainable multimodal artificial intelligence framework that integrates epidemiological, climatic, geospatial, and demographic information through a modality-specific encoder-fusion architecture. The framework is further extended with a spatial Graph Neural Network component designed to capture inter-district dependencies and potential spatial spillover effects. Using a synthetically generated district-level dataset (n = 1,200), calibrated to reflect the statistical properties of variables commonly used in Malaria Atlas Project (MAP), NASA POWER, CHIRPS, WorldPop, and Sentinel-2 products across five malaria-endemic countries (the Democratic Republic of the Congo, Uganda, Nigeria, Tanzania, and Kenya), the proposed framework achieved a validation R² of 0.7285 for Plasmodium falciparum parasite rate regression. For binary high-risk classification, the framework attained an area under the curve of 0.9740 and an F1-score of 0.9630 on the held-out test dataset. SHapley Additive exPlanations were used to examine the contribution of individual predictors at global and local levels. Incidence rate (67.2%), rainfall (10.2%), and temperature (6.2%) emerged as the leading drivers of predicted risk, while intervention coverage showed a protective contribution. The results should be read as a synthetic proof-of-concept rather than as an operational malaria map. Even so, they show how multimodal learning, graph-based spatial modeling, and explainable AI can be combined into a transparent surveillance pipeline for future validation with real-world malaria data in resource-constrained settings.