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Marcílio Ferreira dos Santos

Publications and source records attributed to Marcílio Ferreira dos Santos.

6 recordsLinked to original sources

Urban Structure Predicts a Persistent Place-Based Spatial Signature of Dengue Susceptibility Across Epidemic Conditions

Urban structure alone consistently recovered the relative spatial distribution of dengue susceptibility in unseen years, with year-specific Spearman correlations ranging from 0.49 to 0.76. Structural predictors achieved stronger mean prospective spatial ranking than recent epidemiological history, while combining both information sources produced little additional gain. Temporal generalization collapsed when year-specific spatial correspondence was disrupted (null mean Spearman = 0.0048; empirical $p=0.002$), but was reproduced when coherent place-level susceptibility trajectories were preserved. Cross-classification models retained 93.79\% of within-classification ranking performance after matching training-set size, although the corresponding transportability gap was not unusual relative to random partitions of epidemic years ($p=0.8024$). These findings support a persistent place-based spatial hierarchy of dengue susceptibility that remains informative across markedly different epidemic conditions. Measured urban structure provides an informative predictive representation of this hierarchy, but does not constitute a complete or causal explanation of its persistence. Persistent susceptibility mapping may therefore complement short-term epidemiological surveillance by supporting longer-term, place-based urban health planning.

stat.AP↗

Scale-dependent Temporal Signatures of Arboviral Transmission in Urban Environments

Understanding epidemic dynamics in urban environments requires models that capture interactions across space and time while incorporating biological constraints. In this work, we propose a probabilistic spatiotemporal framework based on pairwise interaction kernels to analyze arboviral transmission using large-scale georeferenced data from Recife, Brazil. The model describes interactions as a function of spatial distance and temporally delayed influence, with parameters estimated via maximum likelihood. Our results reveal a marked asymmetry between spatial and temporal components. The spatial parameter systematically collapses, indicating that spatial proximity does not provide discriminatory information between diseases at the urban scale. In contrast, temporal dynamics exhibit scale-dependent behavior: statistical differentiation between dengue, Zika, and chikungunya emerges only beyond a critical temporal window. We show that unconstrained models primarily capture short-term co-occurrence, leading to apparent but non-robust differences, while biologically constrained models reveal a common underlying transmission structure. Additionally, reconstructed transmission networks exhibit localized and structured interaction patterns consistent with plausible epidemic propagation. These findings demonstrate that epidemic differentiation is not intrinsic, but an emergent phenomenon dependent on temporal scale, highlighting the importance of biologically grounded and scale-aware modeling in spatiotemporal epidemic analysis.

q-bio.PE↗

Topological Percolation in Urban Dengue Transmission: A Multi-Scale Analysis of Spatial Connectivity

We investigate the spatial organization of dengue cases in the city of Recife, Brazil, from 2015 to 2024, using tools from statistical physics and topological data analysis. Reported cases are modeled as point clouds in a metric space, and their spatial connectivity is studied through Vietoris-Rips filtrations and zero-dimensional persistent homology, which captures the emergence and collapse of connected components across spatial scales. By parametrizing the filtration using percentiles of the empirical distance distribution, we identify critical percolation thresholds associated with abrupt growth of the largest connected component. These thresholds define distinct geometric regimes, ranging from fragmented spatial patterns to highly concentrated, percolated structures. Remarkably, years with similar incidence levels exhibit qualitatively different percolation behavior, demonstrating that case counts alone do not determine the spatial organization of transmission. Our analysis further reveals pronounced temporal heterogeneity in the percolation properties of dengue spread, including a structural rupture in 2020 characterized by delayed or absent spatial percolation. These findings highlight percolation-based topological observables as physically interpretable and sensitive descriptors of urban epidemic structure, offering a complementary perspective to traditional spatial and epidemiological analyses.

q-bio.PE↗

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.

physics.soc-ph↗

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

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.

stat.AP↗

Urban Dengue and Spatial Dependence: A SAR Model Incorporating Favela Data in Recife, Brazil

In this study, we used a dataset of approximately 96,000 reported dengue cases from 2015 to 2024 in Recife, the capital of northeastern Brazil, all geocoded by neighborhood. The dataset was enriched with sociodemographic data from the national census (population density, average residents per household), the proportion of slum areas per neighborhood, a dummy variable for high-income neighborhoods (above 7.5 minimum wages), seasonal information (rainy/dry), and water indicators derived from NDWI and MNDWI indexes. We first tested for spatial dependence in dengue cases per square kilometer using Moran's I, which confirmed significant spatial autocorrelation, justifying spatial econometric models. We then applied the SAR (Spatial Autoregressive) model with the listed variables and tested four spatial weight structures (Queen, Rook, KNN, Distance Band). The best-performing model was SAR with Rook contiguity, yielding a pseudo-R-squared of 0.6864 and a spatial pseudo-R-squared of 0.6934. Five variables showed p-values below 0.05. Moran's I applied to the residuals showed no significant spatial autocorrelation, suggesting the model effectively captured spatial structure. The SAC model (also with Rook) was tested but SAR remained slightly superior. In addition to generating a thematic risk map, the SAR model ranked neighborhoods by predicted vulnerability. The Spearman correlation between predicted and observed rankings was 0.901. These findings support SAR as a robust tool for understanding urban dengue patterns and for informing graph-based SIR models.

q-bio.OT↗