ArticlePLoS neglected tropical diseases2026
Climate-driven spatiotemporal dynamics of Aedes infestation and dengue transmission in Porto Alegre, Southern Brazil.
Article in PLoS neglected tropical diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Dengue transmission is strongly influenced by climatic conditions that affect mosquito population dynamics and virus circulation. In Southern Brazil, where dengue historically occurred at low levels, recent climatic anomalies may be contributing to the expansion of Aedes vectors and increased local dengue incidence. This study investigated the spatiotemporal association between climatic variables, Aedes infestation and dengue cases in Porto Alegre (Southern Brazil, from 2018 to 2025). Entomological, surveillance and climatic data were analyzed using Moran's I and LISA for spatial association, Kendall correlation, polynomial regression and LASSO to identify relevant drivers and develop predictive models of mosquito infestation and dengue incidence. Strong spatial association between Aedes aegypti and Aedes albopictus was observed, with persistent local clusters detected across all years. Annual climatic variables were associated with mosquito abundance in several districts. Overall, rainfall frequency had a stronger effect on Ae. aegypti abundance than accumulated rainfall. Temperature and lagged infestation indices showed strong associations with both species and dengue incidence, with lag effects detected up to eight weeks. Predictive models demonstrated good agreement between observed and predicted values, particularly at low to moderate infestation levels. Lagged variables were consistently retained in both mosquito abundance and dengue incidence models, highlighting the importance of temporal predictors in anticipating vector dynamics and dengue risk. This approach is broadly applicable for predicting Aedes infestation and disease incidence and it underscores the importance of integrating entomological and climatic surveillance data to enhance anticipation and early detection of dengue risk periods in large urban settings and inform more effective public health interventions.
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