标题： 城市登革热与空间依赖性：结合里约热内卢贫民窟数据的SAR模型 显示英文标题

标题： Urban Dengue and Spatial Dependence: A SAR Model Incorporating Favela Data in Recife, Brazil

摘要： 在本研究中，我们使用了2015年至2024年巴西东北部首府累西腓约96,000例报告的登革热病例数据集，所有病例均按社区进行地理编码。 该数据集结合了来自国家人口普查的社会经济数据（人口密度、每户平均居民数）、每个社区的贫民区比例、一个表示高收入社区的虚拟变量（高于7.5倍最低工资）、季节信息（雨季/旱季）以及从NDWI和MNDWI指数派生的水体指标。 我们首先使用莫兰的I检验每平方公里登革热病例的空间依赖性，结果确认了显著的空间自相关性，从而证明了空间计量模型的合理性。 然后我们应用了包含所列变量的SAR（空间自回归）模型，并测试了四种空间权重结构（Queen、Rook、KNN、Distance Band）。 表现最佳的模型是具有Rook邻接性的SAR模型，其伪R平方值为0.6864，空间伪R平方值为0.6934。 五个变量显示p值低于0.05。 对残差应用莫兰的I显示没有显著的空间自相关性，表明模型有效地捕捉了空间结构。 还测试了SAC模型（同样使用Rook），但SAR模型仍略胜一筹。 除了生成主题风险图外，SAR模型还按预测易感性对社区进行了排序。 预测排名与观察排名之间的斯皮尔曼相关系数为0.901。 这些发现支持SAR作为理解城市登革热模式和为基于图的SIR模型提供信息的稳健工具。 显示英文摘要

摘要： In this study, we used a dataset of approximately 96,000 reported dengue cases from 2015 to 2024 in Recife, the capital of northeastern Brazil, all geocoded by neighborhood. The dataset was enriched with sociodemographic data from the national census (population density, average residents per household), the proportion of slum areas per neighborhood, a dummy variable for high-income neighborhoods (above 7.5 minimum wages), seasonal information (rainy/dry), and water indicators derived from NDWI and MNDWI indexes. We first tested for spatial dependence in dengue cases per square kilometer using Moran's I, which confirmed significant spatial autocorrelation, justifying spatial econometric models. We then applied the SAR (Spatial Autoregressive) model with the listed variables and tested four spatial weight structures (Queen, Rook, KNN, Distance Band). The best-performing model was SAR with Rook contiguity, yielding a pseudo-R-squared of 0.6864 and a spatial pseudo-R-squared of 0.6934. Five variables showed p-values below 0.05. Moran's I applied to the residuals showed no significant spatial autocorrelation, suggesting the model effectively captured spatial structure. The SAC model (also with Rook) was tested but SAR remained slightly superior. In addition to generating a thematic risk map, the SAR model ranked neighborhoods by predicted vulnerability. The Spearman correlation between predicted and observed rankings was 0.901. These findings support SAR as a robust tool for understanding urban dengue patterns and for informing graph-based SIR models.