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

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.

💡 Research Summary

This paper investigates whether the structural connectivity of Recife’s urban road network can explain the spatial distribution of dengue cases from 2015 to 2024, beyond the traditional adjacency‑based spatial dependence. The authors construct a functional graph where each vertex represents a street‑level location with an associated dengue incidence time series. Edges are drawn between vertices whose Pearson correlation exceeds a preset threshold and whose Euclidean distance is less than 600 m, thereby embedding both temporal synchrony and realistic mosquito flight range constraints.

From this graph they compute communicability C_{ij}(β)= (e^{βA}){ij}, which aggregates all walks between i and j, weighting longer walks exponentially less. By varying β they capture multiscale accessibility. To incorporate dynamic epidemiological information, they introduce a correlation‑weighted communicability curvature κ{ij}=C_{ij}(β)·w_{ij}, where w_{ij}∈