We investigate whether the structural connectivity of urban road networks helps explain dengue incidence in Recife, Brazil (2015--2024). For each neighborhood, we compute the average \emph{communicability curvature}, a graph-theoretic measure capturing the ability of a locality to influence others through multiple network paths. We integrate this metric into Negative Binomial models, fixed-effects regressions, SAR/SAC spatial models, and a hierarchical INLA/BYM2 specification. Across all frameworks, curvature is the strongest and most stable predictor of dengue risk. In the BYM2 model, the structured spatial component collapses ($φ\approx 0$), indicating that functional network connectivity explains nearly all spatial dependence typically attributed to adjacency-based CAR terms. The results show that dengue spread in Recife is driven less by geographic contiguity and more by network-mediated structural flows.

翻译：本研究探讨了城市道路网络的结构连通性是否有助于解释巴西累西腓（2015-2024年）的登革热发病率。针对每个社区，我们计算了平均**可传播性曲率**——一种图论度量，用于捕捉一个地区通过多重网络路径影响其他地区的能力。我们将该指标整合到负二项式模型、固定效应回归、SAR/SAC空间模型以及分层INLA/BYM2规范中。在所有分析框架中，曲率都是登革热风险最强且最稳定的预测因子。在BYM2模型中，结构化空间分量几乎消失（$φ\approx 0$），表明功能性网络连通性解释了通常归因于基于邻接性的CAR项的所有空间依赖性。结果表明，累西腓的登革热传播较少由地理邻接性驱动，而更多由网络介导的结构性流动驱动。