A geostatistical framework to predict malaria incidence in the context of climatic events

Malaria is a life-threatening disease that causes a significant public health burden worldwide. Malaria incidences show considerable temporal and geographical variation, which can be linked at least partly to climatic conditions. Over the last twenty years, multiple Malaria Early Warning Systems (MEWS) have been developed. These systems primarily focus on short-term incidence predictions, but there is a growing demand to implement long-term predictions that are informed by climatic variables. The statistical methodology will be developed that makes use of spatial multivariate time series analyses and distributed lag models, in order to flexibly model spatio-temporal variation in malaria incidence and how this is affected by climatic events. It will embed these methods in a MEWS, which prioritizes applicability and interpretability for healthcare workers and policy-makers in developing countries.

Project Details

Climate Change
Modeling
Product Development

Nov 2022 — Oct 2024

Belgium