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Cleiton de Lima Ricardo

Publications and source records attributed to Cleiton de Lima Ricardo.

5 recordsLinked to original sources

Causal Edge Rees Algebras for Spatiotemporal Graphs

Understanding the evolution of connectivity in spatiotemporal systems requires mathematical frameworks capable of encoding not only instantaneous interactions but also their cumulative causal structure. In this work, we introduce the \emph{Causal Edge Rees Algebra} (CERA), a new algebraic construction associated with causal spatiotemporal graphs. Given a temporal filtration induced by causal constraints, we associate a sequence of edge ideals whose Rees algebra encodes the full history of connectivity evolution in a single graded object. This construction establishes a bridge between dynamic graph topology and commutative algebra. In particular, we show that successive quotients of the filtration capture the emergence of new structural connections, allowing the identification of critical edges responsible for the fusion of previously disconnected components. This leads to the definition of temporal bridge modules and to a bridge detection theorem, which relates the dimension of these modules to the reduction in the number of connected components over time. Unlike existing algebraic approaches in topological data analysis, which are primarily based on geometric filtrations, the proposed framework is driven by intrinsic causal constraints. As a result, the CERA captures not only topological features but also their temporal organization and mechanisms of coalescence. The theory provides a new algebraic perspective on causal network dynamics, connecting edge ideals, Rees algebras, and temporal graphs. Beyond its theoretical significance, the framework opens new directions for the analysis of spatiotemporal systems, including epidemic networks, transport systems, and information propagation processes.

math.CO↗

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↗