Regime-Adaptive Identification of Dengue Transmission Hubs Using Discrete Morse Theory in Brazil

BackgroundDengue fever represents a persistent public health challenge in Brazil. Traditional outbreak prediction models prioritize high-incidence areas, potentially overlooking municipalities that serve as critical transmission bridges. MethodsWe analyzed dengue surveillance data from Brazils SINAN across three epidemic regimes: 2023 (1.51M cases), 2024 (6.43M cases, hyperendemic), and partial 2025 (1.50M cases). We constructed transmission networks using documented importation flows and temporal cross-correlations with regime-adaptive thresholds. Discrete Morse theory classified municipalities as transmission sources (maxima), bridges (saddles), or sinks (minima) based on composite risk scores incorporating case counts, connectivity, and importation patterns. ResultsDespite 4.3-fold case variation across years, network density remained stable (0.0024-0.0027), with edge counts scaling proportionally to municipality coverage. Critical point distributions varied systematically: 2023 had 449 critical nodes; hyperendemic 2024 showed only 274 despite highest case burden; partial 2025 revealed 414 critical nodes. Critical municipalities exhibited significantly higher hub scores (M=2.08-2.16) versus non-critical nodes (M=0.47-0.91, Cohens d=4.2-6.8, p<0.001). Hub scores correlated modestly with case counts ({rho}=0.35-0.42), confirming structural criticality diverges from epidemic volume. ConclusionsDiscrete Morse theory successfully identifies transmission-critical municipalities across varying epidemic intensities. The paradoxical reduction in critical points during hyperendemic transmission (274 vs. 449 in moderate years) suggests topological simplification rather than elaboration during peak transmission. Stable network density across 4.3-fold case variation indicates resilient transmission architecture where epidemic intensity affects volume rather than structure. This provides actionable surveillance tools for public health systems managing fluctuating dengue transmission, suggesting authorities to prioritize action areas structure-based rather than volume-based.