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

Cheikh Talla^1*Maryam Diarra¹Ibrahima Diouf^2,3Mareme Seye Thiam¹Aaboubacry Gaye¹Mamadou Aliou Barry⁴Ehimario Uche Igumbor^5,6Corinne Simone Merle⁷Rosemary Audu⁵Cheikh Loucoubar¹

• ¹Epidemiology, Clinical Research and Data Science Department, Institut Pasteur de Dakar, Dakar, Senegal
• ²Faculty of Science and Technology, Centre Universitaire de Labé, Labé, Guinea
• ³Laboratory of Physics of the Atmosphere and Ocean, Cheikh Anta Diop University, Dakar, Senegal
• ⁴Institut Pasteur de Dakar, Dakar, Senegal
• ⁵Nigerian Institute of Medical Research (NIMR), Lagos, Nigeria
• ⁶School of Health Systems and Public Health, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
• ⁷Special Programme of Research and Training in Tropical Diseases, World Health Organization (Switzerland), Geneva, Geneva, Switzerland

Malaria 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. This 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. The 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. These 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.