标题： 在巴西累西腓登革热易感性映射：主成分分析和稳健回归的社会经济见解 显示英文标题

标题： Mapping Dengue Vulnerability in Recife, Brazil: Socioeconomic Insights from PCA and Robust Regression

摘要： 基于2015年至2024年间在巴西东北部主要城市累西腓报告的约90,000例确诊登革热病例，我们进行了社区层面的空间分析。 2022年巴西人口普查的社会经济和人口统计指标被整合，以探讨与登革热发病率空间分布相关的因素。 为解决多重共线性和降低维度，我们对解释变量应用了主成分分析（PCA）。 使用生成的成分，我们通过普通最小二乘法（OLS）、稳健回归和随机森林算法构建了预测模型。 OLS模型解释了病例密度（每平方公里病例数）60.4%的方差，而更抗异常值的稳健模型则解释了43.2%。 随机森林模型捕捉到了非线性模式，达到了37.3%。 尽管非线性带来了一些局部改进，但线性模型表现出更大的整体稳定性和可解释性。 使用PCA得分，我们构建了社区的登革热风险排名，并将其与实际的2024年分布进行比较，相对顺序匹配度达到83.5%。 我们的研究结果表明，结合降维和预测建模的人口普查社会经济数据可以有效估计城市登革热风险，并指导空间针对性的公共卫生策略。 显示英文摘要

摘要： 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.