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. 2023 Nov 23;20(23):7102.
doi: 10.3390/ijerph20237102.

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

Madina Doumbia  1   2 , Jean Tenena Coulibaly  2   3 , Dieudonné Kigbafori Silué  2   3 , Guéladio Cissé  4   5 , Jacques-André N'Dione  6 , Brama Koné  1   2
Affiliations

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

Madina Doumbia et al. Int J Environ Res Public Health. .

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: EIR; Tiassalé; VECTRI; climate; malaria; vector density.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure A1
Figure A1
Completeness of the rainfall and temperature (minimum and maximum) observational data from the four closest stations to Tiassalé.
Figure A2
Figure A2
Taylor diagrams illustrating comparisons of daily rainfall (ARC2) and minimum and maximum temperature (ERA5) datasets as a reference for the observational meteorological data obtained from the closest four stations to Tiassalé, namely, (A) Abidjan, (B) Yamoussoukro, (C) Gagnoa, and (D) Dimbokro. The dotted lines indicate the correlation coefficients; the standard deviations are shown in blue and the root mean square errors in magenta. In the top row, “RR” means rainfall.
Figure A2
Figure A2
Taylor diagrams illustrating comparisons of daily rainfall (ARC2) and minimum and maximum temperature (ERA5) datasets as a reference for the observational meteorological data obtained from the closest four stations to Tiassalé, namely, (A) Abidjan, (B) Yamoussoukro, (C) Gagnoa, and (D) Dimbokro. The dotted lines indicate the correlation coefficients; the standard deviations are shown in blue and the root mean square errors in magenta. In the top row, “RR” means rainfall.
Figure A3
Figure A3
(i) Monthly averages of precipitation from the four stations closest to Tiassalé (Abidjan, Yamoussoukro, Gagnoa, and Dimbokro). (ii) Error bars, where the medians of the biases (ARC—OBS) are represented in the middle, and the 25th and 75th percentiles are represented by the bars.
Figure A4
Figure A4
Taylor diagrams obtained from the 14 models of CORDEX, model-averaged and observation data for Tiassalé. The dotted lines indicate the correlation coefficient between models and the reference dataset; the circles indicate standard deviations (blue) and root mean square errors (magenta).
Figure A5
Figure A5
List of 14 simulations taken from the CORDEX-AFRICA data. In this ensemble, each experiment comprises the historical and scenario (RCP4.5 and RCP8.5) runs.
Figure A6
Figure A6
The VECTRI outputs (some of these are optional and switched off by default).
Figure A7
Figure A7
Comparison of monthly prevalence with (a) rainfall and (b) Tmax and Tmin data for Tiassalé over the period 2010 to 2019.
Figure 1
Figure 1
Map of the study area (Tiassalé) located in the southern part of Côte d’Ivoire (5°53′ N, 4°49′ W).
Figure 2
Figure 2
The interannual dynamic of climatic factors and malaria cases in Tiassalé from 2010 to 2019. (Tmin + 4 was used in order to have the same scale as Tmax.)
Figure 3
Figure 3
The monthly variation in climatic factors and malaria cases for the period of 2010–2019. (Tmin + 4 was used in order to have the same scale as Tmax in the figure.)
Figure 4
Figure 4
Monthly malaria cases in Tiassalé from 2010 to 2019 and the VECTRI-simulated entomological inoculation rate (EIR).
Figure 5
Figure 5
Anomalies of VECTRI-simulated EIR and vector density with (a) rainfall, (b) minimum temperature, and (c) maximum temperature in Tiassalé.
Figure 6
Figure 6
A Comparison of rainfall, temperature, and VECTRI-simulated EIR and vector density for Tiassalé over the period 1987 to 2019. (a–c) represent respectively the rainfall, Tmin and Tmax. The malaria transmission indicators obtained by VECTRI model are EIR (d) and Vector density (e).
Figure 7
Figure 7
A comparison of rainfall, temperature, and VECTRI-simulated EIR and vector density for Tiassalé over the future period 2030 to 2080 under the RCP4.5 (A) and RCP8.5 (B) scenarios. For each subfigures, (a–c) represent the rainfall, Tmin and Tmax respectively. The malaria transmission indicators obtained by VECTRI model are EIR (d) and Vector density (e).
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