Subseasonal to seasonal hydrometeorological- and dynamical Malaria transmission forecasting over Sub-Saharan Africa

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Sub-Saharan Africa (SSA) remains the most populous region affected by malaria, despite being curable and intense attempts from the World Health Organization (WHO) in collaboration with endemics countries to combat it. While the relationships between climate, climate change and malaria are addressed already for a longer time, this project focuses on possibilities and limitations to predict malaria transmission weeks and months ahead. We further develop and apply sub-seasonal to seasonal (S2S) prediction within a climate-malaria modeling chain. Particularly, we investigate if and how far the consideration of downscaled high resolution S2S forecasts of joint climate-malaria prediction offers benefit for malaria early warning. Our target region are the hyper-endemic malaria zones in Nouna (Burkina Faso) and Kisumu (Kenya) and their Health and Demographic Surveillance System (HDSS) sites. Indeed, anticipated knowledge on which areas are most conducive to malaria transmission and how expected hydrometeorological conditions likely influence infection patterns may have substantial consequences for disease prevention and control. Thus, recent advances in numerical models may help to reduce/prevent climate impacts on malaria when realizing seasonal and sub-seasonal forecasting scales. In this study, we will contribute to malaria early warming initiatives in SSA by developing a weather-based malaria predictions using regionalized S2S forecasts and the grid cell distributed VECTRI dynamical malaria transmission model to target interventions against disease outbreaks. To do so, we will 1) further develop the hydro-meteorological modeling chain for VECTRI and adapt its ponding descriptions, 2) apply multivariate bias-correction and spatially disaggregate the S2S forecasts raw product using available observational products (gridded precipitation datasets and re-analyses), 3) verify the probability forecast after bias correction, 4) estimate the skill of the components of the model chain system and the final malaria early warning system (MEWS), 5) assess the economic gain of the S2S forecasting system for decision-making in malaria action plans and 6) contribute to the Heat to Harvest study (H2H). Our S2S forecasting chain is based on the global SEAS5 system from the European Centre for Medium-Range Weather Forecasts (ECMWF). Our study will be accomplished through six interconnected aims, in collaboration with seven research unit projects, and in close collaboration between German and African partners.