Impact of climatic factors on malaria in Senegal based on the surveillance system between 2015 and 2022
Loading...
Date
Journal Title
Journal ISSN
Volume Title
Publisher
Frontiers Media
Abstract
INTRODUCTION : 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.
METHODS : 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.
RESULTS : 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.
DISCUSSION : 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.
Description
AVAILABILITY DATA STATEMENT : The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
SUPPLEMENTARY MATERIAL SUPPLEMENTARY FIGURE 1. Monthly distribution of climate variables, with the red line representing the calculated medians. The numbers 1 to 12 represent the months of the year from January to December. SUPPLEMENTARY FIGURE 2. Variance inflation factor (VIF) of all climate variables included in the model estimated using a generalised linear model with quasi-poisson distribution.
SUPPLEMENTARY MATERIAL SUPPLEMENTARY FIGURE 1. Monthly distribution of climate variables, with the red line representing the calculated medians. The numbers 1 to 12 represent the months of the year from January to December. SUPPLEMENTARY FIGURE 2. Variance inflation factor (VIF) of all climate variables included in the model estimated using a generalised linear model with quasi-poisson distribution.
Keywords
Malaria, Climate variables, Lag non-linear model, Infectious disease, Modelling, Distributed lag non-linear model (DLNM)
Sustainable Development Goals
SDG-03: Good health and well-being
SDG-13: Climate action
SDG-13: Climate action
Citation
Talla, C., Diarra, M., Diouf, I., Thiam, M.S., Gaye, A., Barry, M.A., Igumbor, E., Merle, C.S., Audu, R. & Loucoubar, C. (2025) Impact of climatic factors on malaria in Senegal based on the surveillance system between 2015 and 2022. Frontiers in Tropical Diseases 6: 1631996: 1-9. doi: 10.3389/fitd.2025.1631996.
