标题： 巴西累西腓登革热的分层贝叶斯建模（2015-2024）：空间粒度和数据质量在流行病风险制图中的作用 显示英文标题

标题： Hierarchical Bayesian Modeling of Dengue in Recife, Brazil (2015-2024): The Role of Spatial Granularity and Data Quality for Epidemiological Risk Mapping

摘要： 登革热仍然是巴西主要的流行病学挑战，表现为城市内部严重的不平等现象以及气候和社会环境因素的影响。 本研究使用在R-INLA中实现的贝叶斯分层时空模型，分析了2015年至2024年累西腓的确认登革热病例，结合了BYM2空间结构和RW1时间成分。 协变量包括人口密度、家庭规模、收入、排水渠道、滞后降水和平均温度。 人口密度和家庭规模对登革热风险有积极影响，而收入和渠道存在具有保护作用。 滞后降水增加了风险，较高温度表现出负相关关系，表明了矢量活动的温度阈值。 该模型拟合良好（DIC=65817；WAIC=64506），收敛稳定，残差空间自相关性适中（phi=0.06），并在2016年至2019年之间呈现出平滑的时间趋势。 时空估计显示北部和西部累西腓持续的高风险集群，与高密度和社会脆弱区域重叠。 除了重现历史模式外，贝叶斯模型支持概率预测和早期预警系统。 与经典模型（GLM、SAR、GWR、GTWR）相比，INLA明确整合了不确定性和时空依赖性，为城市健康管理决策提供了可信区间推断。 显示英文摘要

摘要： Dengue remains one of Brazil's major epidemiological challenges, marked by strong intra-urban inequalities and the influence of climatic and socio-environmental factors. This study analyzed confirmed dengue cases in Recife from 2015 to 2024 using a Bayesian hierarchical spatio-temporal model implemented in R-INLA, combining a BYM2 spatial structure with an RW1 temporal component. Covariates included population density, household size, income, drainage channels, lagged precipitation, and mean temperature. Population density and household size had positive effects on dengue risk, while income and channel presence were protective. Lagged precipitation increased risk, and higher temperatures showed an inverse association, suggesting thermal thresholds for vector activity. The model achieved good fit (DIC=65817; WAIC=64506) and stable convergence, with moderate residual spatial autocorrelation (phi=0.06) and a smooth temporal trend between 2016 and 2019. Spatio-temporal estimates revealed persistent high-risk clusters in northern and western Recife, overlapping with areas of higher density and social vulnerability. Beyond reproducing historical patterns, the Bayesian model supports probabilistic forecasting and early warning systems. Compared with classical models (GLM, SAR, GWR, GTWR), INLA explicitly integrates uncertainty and spatial-temporal dependence, offering credible interval inference for decision-making in urban health management.