PLoS neglected tropical diseases · 2025
Impact of weather extremes on the spatiotemporal dynamics of visceral leishmaniasis in Brazil.
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
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Adams Quinn H, Gause Emma L, Baker Rachel E, Hamer Davidson H, Werneck Guilherme L, Hutyra Lucy R, Shioda Kayoko, Wellenius Gregory A
Аннотация
BACKGROUND: Vector-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 FINDINGS: Among 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.
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 FINDINGS: Among 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.