arXiv subjects

Ezra Gayawan

Publications and source records attributed to Ezra Gayawan.

6 recordsLinked to original sources

arXiv2026

Copula and spatial-regularized variational autoencoder for mapping disease comorbidity in West Africa

Geospatial health disproportionality remains a critical public health concern, as communities face heterogeneous illness risks due to varying exposures to adverse socioeconomic and environmental conditions. While statistical models have been adopted to identify risk factors, studies that account for the complex, non-linear dependencies and spatial regularities inherent in comorbid disease patterns are underdeveloped. In this work, we propose a novel spatially regularized variational autoencoder (VAE) to characterize and map the geospatial disproportion of childhood comorbidity in West Africa, focusing on diarrhea, fever, and acute respiratory infection (ARI). To model dependence between these conditions, this study integrates a bivariate Gumbel copula into the VAE framework, enabling flexible modeling of asymmetric dependence and quantification of joint and conditional morbidity risks. Additionally, covariate effects within the framework were quantified to facilitate epidemiological interpretation of risk factors. The proposed method was benchmarked against commonly used methods and applied to characterize comorbidity in West Africa using the Demographic and Health Survey data. Findings reveal pronounced spatial heterogeneity in the likelihood of comorbidity among West African children, with the strongest co-occurrence observed between fever and ARI. Household wealth, maternal education, and access to improved water sources were associated with the likelihood of comorbidity. These patterns highlight high-risk areas and underscore the need for targeted, location-specific public health interventions.

stat.ME↗

arXiv2026

Decoupling Distance and Networks: Hybrid Graph Attention-Geostatistical Methods for Spatio-temporal Risk Mapping

Accurate spatial prediction and rigorous uncertainty quantification are central to modern spatial epidemiology and environmental risk analysis. We introduce a statistically principled hybrid modelling framework that integrates the nonlinear, attention-based representation learning capabilities of a dynamic Graph Attention Network (GATv2) with a latent Gaussian spatial process from model-based geostatistics (MBG). This framework jointly captures relational dependence encoded in graph structures and continuous spatial dependence governed by physical proximity. We evaluate the proposed model via a controlled simulation study and an applied analysis of malaria prevalence data, comparing its predictive accuracy, calibration, and uncertainty quantification against classical geostatistical models and standalone GATv2 architectures. Our analyses show that GATv2 captures complex nonlinear interactions but fails to account for residual spatial autocorrelation, resulting in miscalibrated predictive distributions. Conversely, geostatistical models provide coherent uncertainty quantification through structured covariance functions yet are constrained by linear predictor assumptions and by their reliance on Euclidean distance to encode spatial structure. By integrating attention mechanisms and nonlinear features with an explicit probabilistic spatial random field, the hybrid model captured the relational dependence, consistently improved predictive accuracy, and provided more realistic uncertainty quantification in both simulation and applied settings. Overall, the findings demonstrate that the hybrid model constitutes a statistically coherent and empirically robust framework for modelling complex spatial and spatio-temporal processes in settings where both distance-based and structure-based dependencies operate.

stat.ME↗

arXiv2025

Violent event-related fatality patterns in Ethiopia: a Bayesian spatiotemporal perspective

Fatalities resulting from violence in armed conflict have long been a significant public health issue in Ethiopia. Despite the severity of this problem, more comprehensive quantitative scientific studies need to be conducted to elucidate the sequence and dynamics of these occurrences. In response, this study introduces a spatio-temporal statistical method designed to uncover the patterns of fatalities associated with violent events in Ethiopia. The research employs a two-part zero-inflated Bayesian generalized additive mixed model, which integrates a spatio-temporal component to map the fatality patterns across Ethiopian regions. The dataset utilized originates from the Armed Conflict Location and Event Data Project, covering fatality counts related to violent events from 1997 to 2022. The analysis revealed that nine out of thirteen administrative regions exhibited a probability greater than 0.6 for fatality occurrence due to violent events, with five regions surpassing a 0.7 probability threshold. These five regions include Benishangul Gumz, Gambela, Oromia, Somali, and the South West Ethiopian People's Region. Notably, the Tigray region displayed the highest probability (0.558) of experiencing more than 20 deaths per violent event, followed by the Benishangul Gumz region with a probability of 0.306. Encouragingly, the findings also indicate an average decline in fatalities per violent event over time. Specifically, the probability of more than 20 deaths per event was 0.401 in 2020, which decreased to 0.148 by 2022. These insights are invaluable for the government, policymakers, political leaders, and traditional or religious authorities in Ethiopia, enabling them to make informed, strategic decisions to mitigate and ultimately prevent violence-related fatalities in the country.

stat.AP↗

arXiv2025

The impact of Women's empowerment on childhood vaccination coverage in Nigeria: a spatio-temporal analysis

Immunization remains one of the most effective public health interventions, substantially reducing childhood morbidity and mortality worldwide. Yet, gender disparity and women's disempowerment continue to hinder access to vaccination services in low- and middle-income countries. In Nigeria, variations in social norms and cultural values shape gender roles, limiting women's autonomy in healthcare decisions and household participation. These constraints contribute to spatial differences in immunization uptake. Using data from four waves of the Nigeria Demographic and Health Survey, we developed two empowerment indices capturing women's participation in household decision-making and their ability to decide on personal healthcare needs. A structured spatiotemporal statistical model was applied to assess how much of the observed vaccination disparities could be attributed to women's empowerment and to predict vaccination outcomes at the third administrative level. We examined five indicators: Bacillus Calmette-Guerin (BCG), zero-dose, complete DPT, MCV-1 (first dose of measles-containing vaccine), and all-basic vaccination coverage. Model validation involved comparing empirical estimates with projections at the second administrative level. Results indicate that empowerment related to household participation and healthcare autonomy generally increases vaccination uptake, though the magnitude of effects varies geographically, particularly among highly empowered women. Despite ongoing national efforts to close immunization gaps, the study highlights the need for context-specific strategies that enhance women's decision-making power and community engagement to reduce regional disparities and improve overall vaccination coverage.

stat.AP↗

arXiv2024

Accounting for Multiple Covariates in Non-Stationary Geostatistical Modelling

Model-based geostatistics (MBG) is a subfield of spatial statistics focused on predicting spatially continuous phenomena using data collected at discrete locations. Geostatistical models often rely on the assumptions of stationarity and isotropy for practical and conceptual simplicity. However, an alternative perspective involves considering non-stationarity, where statistical characteristics vary across the study area. While previous work has explored non-stationary processes, particularly those leveraging covariate information to address non-stationarity, this research expands upon these concepts by incorporating multiple covariates and proposing different ways for constructing non-stationary processes. Through a simulation study, the significance of selecting the appropriate non-stationary process is demonstrated. The proposed approach is then applied to analyse malaria prevalence data in Mozambique, showcasing its practical utility

stat.ME↗

arXiv2024

Mathematical modelling to inform outbreak response vaccination

Mathematical models are established tools to assist in outbreak response. They help characterise complex patterns in disease spread, simulate control options to assist public health authorities in decision-making, and longer-term operational and financial planning. In the context of vaccine-preventable diseases (VPDs), vaccines are one of the most-cost effective outbreak response interventions, with the potential to avert significant morbidity and mortality through timely delivery. Models can contribute to the design of vaccine response by investigating the importance of timeliness, identifying high-risk areas, prioritising the use of limited vaccine supply, highlighting surveillance gaps and reporting, and determining the short- and long-term benefits. In this review, we examine how models have been used to inform vaccine response for 10 VPDs, and provide additional insights into the challenges of outbreak response modelling, such as data gaps, key vaccine-specific considerations, and communication between modellers and stakeholders. We illustrate that while models are key to policy-oriented outbreak vaccine response, they can only be as good as the surveillance data that inform them.

q-bio.PE↗