Effects of Climate Variability on Malaria Transmission in Southern Côte d’Ivoire, West Africa

Madina Doumbia (), Jean Tenena Coulibaly, Dieudonné Kigbafori Silué, Guéladio Cissé, Jacques-André N’Dione and Brama Koné
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Madina Doumbia: Unité de Formation et de Recherche des Sciences Biologiques, Université Péléforo Gon Coulibaly, Korhogo BP 1328, Côte d’Ivoire
Jean Tenena Coulibaly: Centre Suisse de Recherches Scientifiques en Côte d’Ivoire (CSRS), Abidjan 01 BP 1303, Côte d’Ivoire
Dieudonné Kigbafori Silué: Centre Suisse de Recherches Scientifiques en Côte d’Ivoire (CSRS), Abidjan 01 BP 1303, Côte d’Ivoire
Guéladio Cissé: Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, CH 4002 Basel, Switzerland
Jacques-André N’Dione: Centre de Suivi Ecologique, BP 15532, Fann Résidense, Dakar 10700, Senegal
Brama Koné: Unité de Formation et de Recherche des Sciences Biologiques, Université Péléforo Gon Coulibaly, Korhogo BP 1328, Côte d’Ivoire

IJERPH, 2023, vol. 20, issue 23, 1-20

Abstract: Malaria continues to be a major public health concern with a substantial burden in Africa. Even though it has been widely demonstrated that malaria transmission is climate-driven, there have been very few studies assessing the relationship between climate variables and malaria transmission in Côte d’Ivoire. We used the VECTRI model to predict malaria transmission in southern Côte d’Ivoire. First, we tested the suitability of VECTRI in modeling malaria transmission using ERA5 temperature data and ARC2 rainfall data. We then used the projected climatic data pertaining to 2030, 2050, and 2080 from a set of 14 simulations from the CORDEX-Africa database to compute VECTRI outputs. The entomological inoculation rate (EIR) from the VECTRI model was well correlated with the observed malaria cases from 2010 to 2019, including the peaks of malaria cases and the EIR. However, the correlation between the two parameters was not statistically significant. The VECTRI model predicted an increase in malaria transmissions in both scenarios (RCP8.5 and RCP4.5) for the time period 2030 to 2080. The monthly EIR for RCP8.5 was very high (1.74 to 1131.71 bites/person) compared to RCP4.5 (0.48 to 908 bites/person). These findings call for greater efforts to control malaria that take into account the impact of climatic factors.

Keywords: malaria; climate; VECTRI; EIR; vector density; Tiassalé (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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