ArticlePLoS neglected tropical diseases2025
Impact of weather extremes on the spatiotemporal dynamics of visceral leishmaniasis in Brazil.
Article in PLoS neglected tropical diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Recent advances in machine learning and Bayesian modeling for tropical disease prediction, diagnosis, and risk analysis: a scoping review.BMC infectious diseases · 2026Article
- Modeling the seasonal and climate-dependent dynamics of visceral leishmaniasis in Brazil: Implications for transmission and Control.Infectious Disease Modelling · 2026Article
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Authors and funding
8 authors.
Funding
Abstract
backgroundVector-borne diseases are highly sensitive to environmental and climatic conditions, which can directly affect vector behavior, parasite development, and transmission dynamics. Identifying the key meteorological drivers of these diseases and understanding the timing of their impacts is crucial for enhancing public health preparedness. This study focuses on visceral leishmaniasis (VL) in Brazil; a parasitic vector-borne disease spread by the bite of infected sandflies whose distribution is heavily influenced by environmental conditions. METHODOLOGY: We analyzed monthly confirmed VL cases from 2007-2022 using distributed lag nonlinear models within a spatiotemporal Bayesian hierarchical model framework to assess the nonlinear, time-lagged associations between locally defined weather anomalies and VL risk across space. We evaluated the exposure-lag-response relationships between anomalies in monthly average temperature, precipitation, and relative humidity; and VL incidence across Brazilian microregions, considering lags ranging from 0 to 4 months. PRINCIPAL
findingsAmong the 53,968 VL cases reported during the study period, the majority occurred in the Northeast and Central North regions. Our model revealed statistically significant nonlinear relationships between meteorological anomalies and VL risk. Associations were most pronounced in rural and deforested microregions, where climatic extremes intensified transmission risk. CONCLUSIONS AND SIGNIFICANCE: This analysis identified an increased VL risk at higher-than-usual temperatures and a lower risk with higher-than-usual humidity and precipitation across various lags. We offer novel foundational insights for the future development of early warning systems, especially relevant to regions like Brazil facing a substantial VL burden.
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