Correlation-Weighted Communicability Curvature as a Structural Driver of Dengue Spread: A Bayesian Spatial Analysis of Recife (2015-2024)

TL;DR

This study demonstrates that the structural connectivity of urban road networks, measured by communicability curvature, is a key predictor of dengue spread in Recife, surpassing traditional geographic proximity models.

Contribution

It introduces the use of communicability curvature as a novel graph-theoretic predictor for disease spread and integrates it into various spatial models, revealing its dominant role in dengue transmission.

Findings

Communicability curvature is the strongest predictor of dengue risk.

Network connectivity explains most spatial dependence in dengue spread.

Traditional adjacency-based models are less effective than network-based models.

Abstract

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 ($\phi \approx 0$), 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.