Climate change engenders a better Early Warning System development across Sub-Saharan Africa: The malaria case

Ayanlade, Ayansina; Sergi, Consolato; Sakdapolrak, Patrick; Ayanlade, Oluwatoyin S.; Carlo, Paola Di; Babatimehin, Oyekanmi; Weldemariam, Lemlem F.; Jegede, Margaret O.

doi:10.1016/j.resenv.2022.100080

Cited by 4 publications

(1 citation statement)

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“…Sub-Saharan Africa (SSA) is severely afflicted by malaria disease with significant morbidity and mortality caused by a combination of these factors and an all-year-round transmission potential. Most infectious disease outbreaks and epidemics in SSA are connected to climate change and variability (Ayanlade et al, 2022;Moyo et al, 2023;Opoku et al, 2021;Van de Vuurst & Escobar, 2023;Yamana et al, 2016). For instance, the incidence of malaria is related to climatic factors like rainfall and temperature at specific time lags (Abiodun et al, 2016(Abiodun et al, , 2017(Abiodun et al, , 2018Caminade et al, 2014;Christiansen-Jucht et al, 2015;Diouf et al, 2020;Ikeda et al, 2017;Tompkins & Ermert, 2013).…”

Section: Introductionmentioning

confidence: 99%

Process‐Based Atmosphere‐Hydrology‐Malaria Modeling: Performance for Spatio‐Temporal Malaria Transmission Dynamics in Sub‐Saharan Africa

Dieng,

Tompkins,

Arnault

et al. 2024

Water Resources Research

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With the goal of eradication by 2030, Malaria poses a significant health threat, profoundly influenced by meteorological and hydrological conditions. In support of malaria vector control efforts, we present a high‐resolution, coupled physically‐based modeling approach integrating WRF‐Hydro and VECTRI. This model approach accurately captures topographic details at the scale of larvae habitats in the Nouna Health and Demographic Surveillance Systems in Sub‐Saharan Africa. Our study demonstrates the proficiency of the high‐resolution hydrometeorological model, WRF‐Hydro, in replicating observed climate characteristics. Comparisons with in‐situ local weather data reveal root mean square errors between 0.6 and 0.87 mm/day for rainfall and correlations ranging from 0.79 to 0.87 for temperatures. Additionally, WRF‐Hydro's surface hydrology reproduces the seasonal and intraseasonal variability of the ponded water fraction with 96% accuracy, validated against Sentinel‐1 data at a 100‐m resolution. The VECTRI model demonstrates sensitivity to surface hydrology representation, particularly when comparing conceptual and detailed physical process models, for variables such as larvae density, mosquito abundance, and EIR. The model's ability to replicate the seasonality of malaria transmission aligns well with available cohort malaria data suggesting its potential for predicting the impacts of climate change on mosquito abundance and transmission intensity in endemic tropical and subtropical zones. This integrated approach opens avenues for enhanced understanding and proactive management of malaria.

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