AUTHOR=Talla Cheikh , Diarra Maryam , Diouf Ibrahima , Thiam Mareme S. , Gaye Aboubacry , Barry Mamadou A. , Igumbor Ehimario , Merle Corinne Simone , Audu Rosemary , Loucoubar Cheikh 

TITLE=Impact of climatic factors on malaria in Senegal based on the surveillance system between 2015 and 2022

JOURNAL=Frontiers in Tropical Diseases

VOLUME=Volume 6 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/tropical-diseases/articles/10.3389/fitd.2025.1631996

DOI=10.3389/fitd.2025.1631996

ISSN=2673-7515

ABSTRACT=IntroductionMalaria remains a major public health concern, particularly in sub-Saharan Africa, where climatic factors strongly influence its transmission dynamics. However, the delayed effects of these factors on malaria incidence remain poorly understood.MethodsThis study examines the relationship between meteorological variables (temperature, rainfall, and humidity) and malaria incidence in Senegal from 2015 to 2022, using a distributed lag non-linear model (DLNM). Daily malaria case data were obtained from the Senegal syndromic sentinel surveillance network (4S network), while daily climatic data were sourced from the Senegalese meteorology agency and NASA POWER DATA Access.ResultsThe results reveal significant associations between climatic factors and malaria cases. High maximum temperatures were associated with increased malaria risk at lag periods of 2–6 days, whereas extreme rainfall initially reduced mosquito breeding but contributed to increased malaria cases after 10–15 days. Similarly, relative humidity displayed non-linear, time-dependent effects on malaria incidence, underscoring the importance of considering lag effects in climate-health modelling.DiscussionThese findings highlight the necessity of integrating climate variability into malaria control strategies. Adaptive interventions, such as predictive modelling and early warning systems, could enhance response efficiency by enabling proactive vector control and healthcare resource allocation. Future research should explore additional factors, such as socio-economic and behavioural influences, to refine prediction models and optimise malaria prevention efforts in the context of climate change.