Abstract
Malaria is a critical health issue across the world and especially in Africa. Studies based on dynamical models helped to understand inter-linkages between this illness and climate. In this study, we evaluated the ability of the VECTRI community vector malaria model to simulate the spread of malaria in Cameroon using rainfall and temperature data from FEWS-ARC2 and ERA-interim, respectively. In addition, we simulated the model using five results of the dynamical downscaling of the regional climate model RCA4 within two time frames named near future (2035–2065) and far future (2071–2100), aiming to explore the potential effects of global warming on the malaria propagation over Cameroon. The evaluated metrics include the risk maps of the entomological inoculation rate (EIR) and the parasite ratio (PR). During the historical period (1985–2005), the model satisfactorily reproduces the observed PR and EIR. Results of projections reveal that under global warming, heterogeneous changes feature the study area, with localized increases or decreases in PR and EIR. As the level of radiative forcing increases (from 2.6 to 8.5 W.m−2), the magnitude of change in PR and EIR also gradually intensifies. The occurrence of transmission peaks is projected in the temperature range of 26–28 °C. Moreover, PR and EIR vary depending on the three agro-climatic regions of the study area. VECTRI still needs to integrate other aspects of disease transmission, such as population mobility and intervention strategies, in order to be more relevant to support actions of decision-makers and policy makers.
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Data availability
RCA4 output data are available through the Earth System Grid Federation (ESGF) website (https://esgf-data.dkrz.de/search/cordex-dkrz/). The ERA‐Interim reanalysis is available from the European Centre for Medium‐Range Weather Forecast (ECMWF) and can be downloaded through the link: https://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/. The CHIRPS2 data are available at https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/netcdf/.
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Acknowledgements
The authors acknowledge Dr Adrian Mark Tompkins for providing the VECTRI model and supporting information. The second author also acknowledges the support from the DAAD within the framework of the ClimapAfrica programme. The authors also thank members of the LEMAP laboratory at the University of Yaounde I for their support. The authors would like to acknowledge the Rossby Centre, Swedish Meteorological and Hydrological Institute (SMHI), Norrköping, Sweden, where RCA4 simulations are performed. We also acknowledge all the reanalysis and observational data providers used in this study. We are grateful to anonymous reviewers and the editor for the thorough reviews and critical comments.
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Mbouna, A.D., Tamoffo, A.T., Asare, E.O. et al. Malaria metrics distribution under global warming: assessment of the VECTRI malaria model over Cameroon. Int J Biometeorol 67, 93–105 (2023). https://doi.org/10.1007/s00484-022-02388-x
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DOI: https://doi.org/10.1007/s00484-022-02388-x