Partitioned Variance Analysis of Dengue Incidence in Rio de Janeiro City: Exploring the Impact of Urban Thermal Structure and Neighborhood Clustering

This study investigates the spatial distribution of dengue cases in the city of Rio de Janeiro by employing neighborhood-level partitioned variance analysis, k-means clustering, and multivariate linear regression modeling. By analyzing weekly dengue case reports from the Brazilian Ministry of Health alongside meteorological data from AlertaRio mesonet stations, we reveal a significant correlation between urban thermal structures—specifically the Urban Heat Island (UHI)—and dengue incidence. Notably, increases in daily maximum and minimum temperatures precede peaks in dengue cases by up to one week, indicating a potential influence of urban heat on dengue transmission and severity. The variance analysis identifies four distinct neighborhood clusters, each accounting for a substantial portion of the total variance in dengue incidence, with a confidence level exceeding 90%. These findings highlight the intricate interplay between urban environments, local climate patterns, and the proliferation of vector-borne diseases such as dengue. Furthermore, they underscore how critical socio-economic factors, such as inadequate infrastructure and limited access to healthcare, exacerbate community vulnerability. This study emphasizes the need for integrated urban planning and public health strategies to mitigate health risks associated with UHI, ultimately enhancing resilience against vector-borne diseases in densely populated urban areas.