Spatial and Spatio-Temporal Epidemiology最新文献

{"title":"Pathways of spread of COVID-19 in Québec from 2020 to 2022","authors":"Adrien Saucier ,&nbsp;Élise Fortin ,&nbsp;Katia Charland ,&nbsp;Christine Lacroix ,&nbsp;Kate Zinszer","doi":"10.1016/j.sste.2025.100778","DOIUrl":"10.1016/j.sste.2025.100778","url":null,"abstract":"<div><div>Spatial and sociodemographic disparities in COVID-19 cases have been well documented in the literature although the specific pathways through which the virus spread across territories have received comparatively less attention. Yet, understanding these pathways can offer valuable insights into the progression of the pandemic and the spatial dynamics shaping COVID-19 transmission. In this study, we used a trend-surface analysis to retrospectively examine how COVID-19 diffused across Québec, Canada, drawing on provincial-wide surveillance data from 2020 to 2022. Our work shows how COVID-19 tended to spread outwards from urban centers and that its speed of spread increased substantially over time, from 6.1 km per day during Wave 1 to 24.7 km per day by Wave 7. By characterizing the spatial pathways and velocity of spread of COVID-19 in Québec, this work provides information that could support public health authorities in preparing for future pandemics and strengthening geographically informed surveillance systems.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"56 ","pages":"Article 100778"},"PeriodicalIF":1.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Relationship between time spent in outdoor recreational areas and stress among parents during the COVID-19 lockdown — A spatial temporal analysis of GPS traces from geographical EMA","authors":"Balaji Ramesh ,&nbsp;Bridget Freisthler ,&nbsp;Yun Ye ,&nbsp;Katie Kieninger ,&nbsp;Gia Barboza-Salerno ,&nbsp;Holly Thurston","doi":"10.1016/j.sste.2026.100782","DOIUrl":"10.1016/j.sste.2026.100782","url":null,"abstract":"<div><h3>Background</h3><div>The early COVID-19 period, with stay-at-home orders, was particularly stressful for parents. Outdoor recreation areas (ORAs), such as green spaces, may have helped alleviate stress.</div></div><div><h3>Aim</h3><div>To estimate the association between ORA visits and self-reported stress using geographical ecological momentary assessment (gEMA) with refined multi-sourced ORA boundaries.</div></div><div><h3>Methods</h3><div>Self-reported stress was collected from a cohort of 286 participants via EMA three times daily over 14 days, alongside continuous GPS tracking. ORA visit durations were derived by spatio-temporal clustering of GPS tracks. Generalized ordinal logistic regression model supporting partial proportional odds was used to estimate the association between ORA visit duration stress, adjusting for baseline covariates and weather.</div></div><div><h3>Results</h3><div>A minute-wise increase in ORA visit duration was not significantly associated with stress (Odds Ratio=0.99; 95% CI: 0.99 to 1.00). However, when the duration was categorized, ORA visits lasting between 15 and 35 min were associated with a 40% reduction in the odds of reporting higher stress (95% CI: 10% to 60%). A similar association was observed for shorter ORA visits (≤ 5 min), though the effect varied across stress levels. The odds of reporting higher stress were also associated with whether the parent was with their focal child, parental sex, marital status, work status, the time of day, and weekday/weekend.</div></div><div><h3>Conclusion</h3><div>Spending 15–35 min in ORAs may be optimal for parents to manage stress during challenging periods, such as the stay-at-home phase of the COVID-19 pandemic. Even brief ORA visits (&lt; 5 min) may help parents experiencing high stress.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"56 ","pages":"Article 100782"},"PeriodicalIF":1.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial-temporal coupling of malaria vector habitat suitability and biting probability","authors":"Grace R. Aduvukha ,&nbsp;Elfatih M. Abdel-Rahman ,&nbsp;Onisimo Mutanga ,&nbsp;John Odindi ,&nbsp;Henri E.Z. Tonnang","doi":"10.1016/j.sste.2025.100777","DOIUrl":"10.1016/j.sste.2025.100777","url":null,"abstract":"<div><div>Effective control of malaria vectors is crucial for achieving successful malaria elimination. Modelling techniques play a key role in understanding the behaviour and distribution of <em>Anopheles</em> mosquito, the vector responsible for malaria transmission upon infection with the <em>Plasmodium</em> parasite. Despite advances in species distribution modelling (SDM), the behavioural dynamics of malaria vectors, particularly biting probability, have not been fully integrated into these models. This study aimed to model both the habitat suitability and biting probability of malaria vectors. Specifically, relevant remotely sensed data, climatic and topographic variables, and presence-only malaria vector data were integrated into MaxEnt (maximum entropy), an SDM to assess the distribution of malaria vectors. Subsequently, additional variables such as the modelled malaria vector presence, human availability, confirmed insecticide resistance and bed net usage were incorporated in fuzzy logic rule-based techniques to assess the spatial and temporal biting risk probability of these vectors from 2000 to 2018. Two different rule-based model scenarios with distinct rule combinations (Model 1: flexible optimal climatic and environmental conditions (i.e., ORs) and Model 2: strict optimal climatic and environmental conditions (i.e., ANDs)) were evaluated. An independent set of validation data with <em>An. gambiae</em> complex biting observations (<em>n</em> =25) for the years (2017-2021) was used. Validation of the models yielded a mean accuracy of 91% for both models. The models showed reduced biting probability with increased bed net usage amid optimal conditions for biting. This pattern was also comparable to the reduced <em>Plasmodium falciparum</em> prevalence rate from 2000 to 2018 due to an increase in intervention measures. The results highlight the significance of integrating modelled malaria vector presence, ecological conditions, human availability/presence and control methods in the assessment of malaria transmission risk as an early warning system in changes to climate and control methods usage. These findings are pivotal for optimizing targeted malaria vector management and malaria elimination strategies, providing essential insights for public health and disease control stakeholders.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"56 ","pages":"Article 100777"},"PeriodicalIF":1.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145748075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial and spatio-temporal county-level trends in COVID-19 mortality and emergency department visits in U.S. with R-INLA","authors":"Diba Khan ,&nbsp;Mark J. Panaggio ,&nbsp;Molly E. Gallagher ,&nbsp;Philip Graff ,&nbsp;Lance Broeker ,&nbsp;Mark Weng ,&nbsp;Farida Ahmad ,&nbsp;Michael Sheppard","doi":"10.1016/j.sste.2025.100779","DOIUrl":"10.1016/j.sste.2025.100779","url":null,"abstract":"<div><h3>Background</h3><div>Weekly county-level COVID-19 mortality and emergency department (ED) visits data are critical data sources for understanding COVID-19 trends, but subject to reporting delays, sampling variability, potential instability and concerns due to statistical reliability as well as data suppression due to small numbers and the need to protect personally identifiable information. Such suppression limits meaningful examination of county-level variation in COVID-19 mortality rates and ED visits.</div></div><div><h3>Methods</h3><div>In this study, we use Bayesian inference on latent Gaussian models in the software R-INLA (Integrated Nested Laplace Approximation) to generate reliable weekly estimates of COVID-19 ED visits and mortality rates at the county level in order to examine spatiotemporal variation.</div></div><div><h3>Results</h3><div>The results demonstrate that weekly county-level COVID-19 mortality rates and ED visits can be accurately modeled using the INLA method. Model-based estimates reflect marked geographic variability for the years 2020–2025.</div></div><div><h3>Conclusions</h3><div>Effective public health interventions rely on access to timely and detailed spatiotemporal data. Granular estimates that are subject to less reporting noise, such as those produced via INLA modeling, can be used to guide surveillance, improve response strategies, enhance preparedness, and inform public health policy.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"56 ","pages":"Article 100779"},"PeriodicalIF":1.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Domestic violence, socio-spatial vulnerability and maxillofacial trauma: an ecological study to identify high-risk areas","authors":"Ítalo de Macedo Bernardino ,&nbsp;Lorena Marques da Nóbrega ,&nbsp;Larissa Trajano de Souza ,&nbsp;Tayná Ribeiro Monteiro de Figueiredo ,&nbsp;Andreza Cristina de Lima Targino Massoni ,&nbsp;Sérgio d’Ávila","doi":"10.1016/j.sste.2025.100781","DOIUrl":"10.1016/j.sste.2025.100781","url":null,"abstract":"<div><div>The aim of this study was to determine the incidence and spatial-temporal distribution of maxillofacial trauma caused by domestic violence using an innovative approach, through geostatistical techniques. For this purpose, an ecological study was carried out, involving the analysis of cases of maxillofacial trauma caused by domestic violence, using aggregated data from Brazilian victims attended at a Center of Forensic Medicine and Forensic Dentistry during a period of four years. Three types of spatial-temporal analysis techniques were performed: (i) analysis of the incidence of cases using finite mixture modeling; (ii) spatial autocorrelation analysis; and (iii) spatial regression analysis (p &lt; 0.05). During the investigation period, 858 new cases of maxillofacial trauma caused by domestic violence were recorded. The results showed that the incidence was high and with tendency to increase in the last year of observation in about 30.0% of neighborhoods under study. The Getis-Ord indicator (Gi*) pointed to predominance of high-risk neighborhoods in the Eastern zone (p &lt; 0.05) and in the Western zone (p &lt; 0.05). Based on the final regression model, it was found that indicator 2 (proxy of socioeconomic status) remained significantly associated with the incidence of events (β = 8.403; SE = 2.738; p &lt; 0.001). The association between socioeconomic status, domestic violence and maxillofacial trauma reinforces the role of social inequalities. The findings suggest an association between a higher number of cases and high socio-spatial vulnerability, highlighting the need for the development of public policies to prevent domestic violence and maxillofacial injuries.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"56 ","pages":"Article 100781"},"PeriodicalIF":1.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Topological data analysis for cluster-based dengue forecasting in Mexico","authors":"J.A. Martínez-Cadena","doi":"10.1016/j.sste.2026.100784","DOIUrl":"10.1016/j.sste.2026.100784","url":null,"abstract":"<div><div>Topological Data Analysis (TDA) offers shape-aware descriptions for complex epidemic patterns. We have created a clustered forecasting pipeline for weekly dengue incidence at the state level in Mexico. This pipeline integrates TDA, using persistence diagrams and persistence images (PI), into feature engineering, Gaussian-mixture clustering, and cluster-level count Generalized Linear Models (GLMs). We compare Poisson and Negative-Binomial (NB2) models using a rolling-origin evaluation with naïve and seasonal-naïve baselines. With three clusters, NB2 consistently performs better than Poisson in skill, showing improved Mean Absolute Error (MAE) across clusters. The results for NB2 are <span><math><mrow><mo>{</mo><mn>0</mn><mo>.</mo><mn>264</mn><mo>,</mo><mspace></mspace><mn>0</mn><mo>.</mo><mn>475</mn><mo>,</mo><mspace></mspace><mn>0</mn><mo>.</mo><mn>604</mn><mo>}</mo></mrow></math></span>, compared to <span><math><mrow><mo>{</mo><mn>0</mn><mo>.</mo><mn>131</mn><mo>,</mo><mspace></mspace><mn>0</mn><mo>.</mo><mn>382</mn><mo>,</mo><mspace></mspace><mn>0</mn><mo>.</mo><mn>495</mn><mo>}</mo></mrow></math></span> for Poisson. An analysis across the Baseline, +TDA, and +TDA+PI demonstrates that adding TDA/PI does not significantly alter one-week forecast skill. However, it enhances interpretability and stratification. The means of PI and differences between cluster and global PI highlight unique, cluster-specific dynamic patterns. The workflow is fully reproducible, including seeds, versions, and configuration, and it generates operational, cluster-based forecasts suitable for regional dengue surveillance.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"56 ","pages":"Article 100784"},"PeriodicalIF":1.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146229315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying territorial inequalities in the population-level double burden of child malnutrition in Argentina: A joint spatial modeling approach","authors":"Nicolás Flaibani ,&nbsp;Adriana Pérez ,&nbsp;Pablo Nuñez ,&nbsp;Gerardo Cueto","doi":"10.1016/j.sste.2026.100783","DOIUrl":"10.1016/j.sste.2026.100783","url":null,"abstract":"<div><div>The double burden of malnutrition (DBM) at the population level, the coexistence of undernutrition and overnutrition within the same region, is a growing public health challenge in many low- and middle-income countries. Historically, its components (i.e. stunting and overweight) have been analysed independently, limiting the identification of overlapping spatial patterns. We studied territorial inequalities in population-level DBM among children under five in Argentina by jointly modelling the spatial distribution of stunting and overweight.</div><div>We conducted a cross-sectional study using 2019 anthropometric data from 970,516 children under five with public healthcare coverage across 513 departments. Stunting and overweight were defined using the WHO growth standards. Bayesian spatial joint models were fitted under the INLA framework, studying structures with independent and shared spatial effects for each outcome.</div><div>The mean departmental proportions were 11.4 % for stunting and 14.2 % for overweight, with geographic location explaining 55.0 % and 41.7 % of their respective variances. The shared spatial structure accounted for 47.9 % of the total random variation. Departments with the highest DBM proportion were located in southwestern Patagonia and the Central and Northern region. This study provides the first department-level mapping of DBM in Argentina, providing a comprehensive view of its spatial distribution while simultaneously estimating the distinct spatial patterns of each indicator. These findings are essential for identifying high-risk areas and guiding integrated strategies to reduce inequalities. Although limited to children with public coverage and potentially introducing ecological bias, the departmental scale likely mitigated these issues, yielding robust, policy-relevant estimates.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"56 ","pages":"Article 100783"},"PeriodicalIF":1.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146229247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Climate-driven potential for tularemia in East Africa: skill testing and ecological consistency of a transferred risk model","authors":"Komi Mensah Agboka ,&nbsp;Allan Muohi Ngángá ,&nbsp;Bonoukpoè Mawuko Sokame ,&nbsp;Steve Soh Bernard Baleba ,&nbsp;Tobias Landmann ,&nbsp;Elfatih M. Abdel-Rahman ,&nbsp;Chrysantus M. Tanga ,&nbsp;Souleymane Diallo","doi":"10.1016/j.sste.2025.100756","DOIUrl":"10.1016/j.sste.2025.100756","url":null,"abstract":"<div><div>Tularemia, a neglected zoonosis, remains underreported in Africa despite growing concern over its climate-driven expansion. This study aims to quantify the specific contribution of climate to tularemia risk using a climate attribution framework. We trained a Least Squares Dummy Variable (LSDV) fixed-effects panel model on United States (U.S.) county-level tularemia incidence data from 2011–2020 (n = 500, R² = 0.90), incorporating only climatic predictors: cumulative temperature, cumulative precipitation, and their respective variabilities. The climate-only model explained 86% of variance in the training data, demonstrating strong climate influence on tularemia disease dynamics. We then applied the model to East Africa, using environmental similarity analysis to assess transferability. Results show moderate-to-high climatic analogues in northern Kenya, eastern Uganda, and South Sudan. Between 2017 and 2020, predicted tularemia suitability increased by a median of +0.18 compared to the 2012–2015 baseline, particularly in arid and semi-arid zones. Low interannual variability suggests persistent climatic suitability. A thermal plausibility test showed strong agreement (r = 0.82) between predicted risk and the Gaussian thermal profile of <em>Francisella tularensis</em>. Our findings suggest that climate alone can spatially explain tularemia risk across Africa’s drylands. This method provides a transferable framework for early warning in data-poor regions and supports anticipatory surveillance in the context of climate change.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"55 ","pages":"Article 100756"},"PeriodicalIF":1.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying county-level effect modifiers of the association between heat waves and preterm birth using a Bayesian spatial meta regression approach","authors":"Shuqi Lin ,&nbsp;Howard H. Chang ,&nbsp;Lyndsey A. Darrow ,&nbsp;Matthew J. Strickland ,&nbsp;Amy Fitch ,&nbsp;Andrew Newman ,&nbsp;Xiaping Zheng ,&nbsp;Joshua L. Warren","doi":"10.1016/j.sste.2025.100762","DOIUrl":"10.1016/j.sste.2025.100762","url":null,"abstract":"<div><div>High temperature is associated with adverse health outcomes, particularly for vulnerable subpopulations including pregnant individuals and their unborn babies. Several recent studies have investigated the association between temperature and preterm birth at different geographic scales and across different spatial locations. However, there has been less focus on characterizing spatial heterogeneity in risks and identifying modifiable factors that contribute to the observed variation. In this work, we carry out a two-stage modeling approach to (i) estimate county-level short-term associations between heat waves and preterm birth across eight states in the United States and (ii) explore county-level factors that modify these associations using a newly developed hierarchical Bayesian spatial meta-regression approach. Specifically, we extend the traditional meta-regression framework to account for spatial correlation between counties within the same state by modeling the effect estimates using a variant of the conditional autoregressive model. We report several variables that modified the associations between heatwaves and preterm birth, including housing quality, energy affordability, and social vulnerability for minority status and language barriers. An R package, <strong>SpMeta</strong>, is developed for analyses that aims to synthesize area-level risk estimates while accounting for spatial dependence.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"55 ","pages":"Article 100762"},"PeriodicalIF":1.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Socio-spatial characterization of sub-sewersheds for wastewater-based epidemiology (WBE): Developing and evaluating two estimators for population-related variables","authors":"Yassine Talleb ,&nbsp;Ferry Heinzelmann ,&nbsp;Timo Haselhoff ,&nbsp;Tina Schmidt ,&nbsp;Issa Nafo ,&nbsp;Susanne Moebus ,&nbsp;Katja Ickstadt ,&nbsp;Dennis Schmiege","doi":"10.1016/j.sste.2025.100759","DOIUrl":"10.1016/j.sste.2025.100759","url":null,"abstract":"<div><div>Wastewater-based epidemiology (WBE) offers valuable population-level health insights. Sampling within sewer systems enables small-scale differentiation, but requires accurate local population data. Allocating population data to sewersheds is complicated by mismatched administrative and sewershed boundaries, for which no established method exists. This study introduces and evaluates two methods for estimating sub-sewershed populations: Proportional Building-based Population Estimation (PBPE) and the Spatial Grid Population Estimation (SGPE). Both were compared with the reported population figures and the result of a simple overlay procedure. The methods were then applied to characterize populations in 195 sub-sewersheds in a large German metropolitan area.</div><div>PBPE reallocates population data by weighting intersecting city sub-units based on the number of residential buildings, while SGPE uses inverse-distance weighting to interpolate population density based on residential land use across a hexagonal lattice. Despite different data requirements and methodologies, both estimators produced highly concordant results with strong correlations between estimated and reported population numbers (Pearson r: 0.97 for both) and between estimates for all population-related parameters (Pearson r range: 0.89–0.95). Median deviations were -0.6% (PBPE) and +3.3% (SGPE) for total population, and under 5% across age and socio-economic strata.</div><div>The strong agreement confirms both estimators are reliable. PBPE shows lower deviations but requires detailed building data and is sensitive to missing structures and uniform-occupancy assumptions. SGPE works without exhaustive building footprints but depends on user-selected grid parameters. This study provides an empirical basis for improving small-scale WBE data use and interpretability in densely populated urban areas, facilitating an improved public health database.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"55 ","pages":"Article 100759"},"PeriodicalIF":1.7,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}