series event

The added value of climate forecasts in disease decision-support models


The World Health Organization has advocated the use of climate information in early warning and response systems for climate-sensitive diseases, such as dengue and malaria. The incorporation of seasonal climate forecasts in the planning and prevention phases could provide warnings of public health threats months in advance. This can help decision makers optimise scarce resources through targeted and focused interventions. In this seminar, I will present a Bayesian spatio-temporal model framework, which quantifies the extent to which climate indicators can explain variations in disease risk. The framework is designed to disentangle the impacts of climatic drivers from other risk factors, using multi-source data and random effects. This helps to account for additional layers of uncertainty and identify unknown spatial patterns and inter-annual signatures. The model has been applied to produce real-time probabilistic early warnings of dengue in Brazil, using seasonal climate forecasts. The framework can be adapted to model any climate-sensitive disease at different spatial/temporal scales and geographical settings. I will provide examples of modelling the impact of climate on dengue and malaria in Latin America, Southeast Asia and Africa and temperature-related mortality in Europe.