Mapping Dengue Vulnerability in Recife, Brazil: Socioeconomic Insights from PCA and Robust Regression (2507.08814v1)

Abstract: Based on approximately 90,000 confirmed dengue cases reported in Recife - a major city in northeastern Brazil - between 2015 and 2024, we conducted a neighborhood-level spatial analysis. Socioeconomic and demographic indicators from the 2022 Brazilian Census were integrated to explore factors associated with the spatial distribution of dengue incidence. To address multicollinearity and reduce dimensionality, we applied Principal Component Analysis (PCA) to the explanatory variables. Using the resulting components, we built predictive models via Ordinary Least Squares (OLS), robust regression, and Random Forest algorithms. The OLS model explained 60.4% of the variance in case density (cases per square kilometer), while the robust model - more resilient to outliers - accounted for 43.2%. The Random Forest model, capturing nonlinear patterns, achieved 37.3%. Despite some localized gains from nonlinearity, linear models showed greater overall stability and interpretability. Using PCA scores, we constructed a dengue risk ranking of neighborhoods and compared it to the actual 2024 distribution, achieving an 83.5% match in relative ordering. Our findings indicate that census-based socioeconomic data, when combined with dimensionality reduction and predictive modeling, can effectively estimate urban dengue risk and guide spatially targeted public health strategies.