High-Temporal-Resolution Prediction of Malaria Transmission Risk in South Kivu, Democratic Republic of the Congo, Based on Multi-Criteria Evaluation Using Geospatial Data

Malaria is a major public health concern, and accurate mapping of malaria risk is essential to effectively managing the disease. However, current models are unable to predict malaria risk with high temporal and spatial resolution. This study describes a climate-based model that can predict malaria risk in South Kivu, Democratic Republic of the Congo, daily at a resolution of 2 km using meteorological (relative humidity, precipitation, wind speed, and temperature) and elevation data. We used the multi-criteria evaluation technique to develop the model. For the weighting of factors, we employed the analytical hierarchy process and linear regression techniques to compare expert knowledge-driven and mathematical methods. Using climate data from the prior 2 weeks, the model successfully mapped regions with high malaria case numbers, enabling accurate prediction of high-risk regions. This research may contribute to the development of a sustainable malaria risk forecasting system, which has been a longstanding challenge. Overall, this study provides insights into model development to predict malaria risk with high temporal and spatial resolution, supporting malaria control and management efforts.