High resolution coupled climate-hydrology-dynamical Malaria transmission modeling for regional Malaria transmission in sub-Saharan Africa
- 1Institute of Meteorology and Climate Research (IMK-IFU), Campus Alpine, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany (diarra.dieng@kit.edu)
- 2Institute of Geography, University of Augsburg, Augsburg, Germany
- 3International Centre for Theoretical Physics (ICTP), Trieste, Italy
- 4Centre de Recherche en Santé de Nouna (CRSN), Nouna, Burkina Faso
- 5Kenya Medical Research Institute (KEMRI), Kisumu, Kenya
- 6Remote Sensing Solutions (RSS) GmbH, München, Germany
Malaria remains a major health problem predominantly in tropical countries and is still being one of the biggest causes of mortality worldwide. It is an ancient vector borne infectious disease caused by parasitic protozoans of the genus Plasmodium and is transmitted by female mosquitos of the Anopheles species. The spatiotemporal distribution of this vector is sensitive to climate conditions and the distribution of hydrometeorological variables, particularly temperature, precipitation, and humidity. We present first results of a joint high resolution hydrometeorological- and subsequent dynamical vector transmission modelling. Our approach uses the couple atmospheric- and terrestrial model system WRF-Hydro, with a 1km grid spacing for the atmospheric part and a 100m grid spacing for the hydrological part. Besides traditional hydrometeorological variables, WRF-Hydro further resolves the surface water, which is potentially a crucial step forward for the grid cell distributed dynamical vector transmission model VECTRI. Our study addresses two Health and Demographic Surveillance Systems (HDSS) site regions at Nouna in Burkina Faso and Kisumu in Kenya. We present an analysis of the performance of the hydrometeorological model system and first results of the VECTRI modeling.
Preliminary results of the WRF-Hydro -VECTRI model system capture the Malaria seasonal variations correctly and show reasonable reproduction of the year-to-year variability of HDSS observed total Malaria cases.
How to cite: Bousso Dieng, M. D., Arnault, J., Tompkins, A., Sié, A., Munga, S., Franke, J., and Kunstmann, H.: High resolution coupled climate-hydrology-dynamical Malaria transmission modeling for regional Malaria transmission in sub-Saharan Africa, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13166, https://doi.org/10.5194/egusphere-egu22-13166, 2022.