Spatial and Spatio-Temporal Epidemiology最新文献

{"title":"Territorial suitability and human exposure interactions shaping dengue incidence in Mexico (2020–2024)","authors":"René Vázquez-Jiménez,&nbsp;Rocío N. Ramos-Bernal,&nbsp;Gustavo A. Alonso-Silverio,&nbsp;Miguel Alcaraz-Vázquez","doi":"10.1016/j.sste.2026.100834","DOIUrl":"10.1016/j.sste.2026.100834","url":null,"abstract":"<div><div>Dengue remains a major public health concern in tropical and subtropical regions, with spatial patterns shaped by environmental gradients and human population distribution. In Mexico, pronounced geographic heterogeneity complicates prevention and surveillance efforts. This study develops a state-level Territorial Suitability Index (TSI) for dengue during the 2020–2024 period. The framework integrates environmental suitability and human-exposure variables within a spatial analytical approach. Temperature, precipitation, relative humidity, vegetation, elevation, slope, built-up fraction, and population density were used to generate suitability surfaces at 1-km resolution in Google Earth Engine. These surfaces were subsequently aggregated and validated against confirmed dengue incidence from the national epidemiological surveillance system.</div><div>Results showed moderate and spatially consistent associations between the TSI and state-level dengue incidence, with Spearman correlation coefficients reaching 0.47 and AUC values up to 0.72 for the combined index, reflecting the joint influence of environmental suitability and population exposure. High territorial suitability was concentrated along the Gulf of Mexico and Pacific coastal regions, whereas high-altitude states consistently exhibited low suitability values. The index captures macro-territorial suitability patterns rather than local transmission dynamics, highlighting the importance of spatial scale when linking environmental processes with epidemiological data. The proposed framework demonstrates how multi-source environmental information can be integrated into reproducible territorial indicators that support strategic planning, spatial prioritization, and public health surveillance in vector-borne disease management.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"58 ","pages":"Article 100834"},"PeriodicalIF":1.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148736662","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":"Approximating Bayesian spatial relative risk of dengue in Bandung: A gradient boosting surrogate model with demographic and climatic features","authors":"Gwynne Joeloidian,&nbsp;Robyn Irawan,&nbsp;Benny Yong","doi":"10.1016/j.sste.2026.100832","DOIUrl":"10.1016/j.sste.2026.100832","url":null,"abstract":"<div><div>Dengue Hemorrhagic Fever (DHF), a disease transmitted by <em>Aedes aegypti</em> mosquitoes, remains a critical public health issue. Bandung is recorded as having one of the highest numbers of DHF cases in Indonesia, exhibiting a concerning increasing trend annually. This persistent rise indicates that the current control efforts for DHF have not been optimal. In this study, the relative risk of DHF transmission across subdistricts in Bandung is estimated using the Besag, York, and Mollié (BYM) model within a Bayesian inference framework. The BYM model is extended by incorporating additional covariates: demographic factors, specifically the proportion of residents with blood type O, and climatic factors, including temperature, rainfall, wind speed, and humidity. These additions enhance model performance by specifying prior distributions for parameters corresponding to each covariate. Furthermore, to assess the potential of machine learning as a scalable forecasting tool, XGBoost and LightGBM are applied as non-parametric surrogates to the complex BYM model, utilizing covariates and binary adjacency matrices to capture spatial dependencies. Results suggest that climatic and demographic variables drive the spatial pattern of DHF, with the proportion of the population having blood type O emerging as the dominant variable improving both Bayesian and machine learning model performance. After hyperparameter tuning, both models approximated DHF relative risk at different levels of fidelity, yielding RMSE values of 0.30 for XGBoost and 0.16 for LightGBM. These findings demonstrate that LightGBM in particular can effectively emulate Bayesian spatial patterns, serving as a deployable tool for rapid risk forecasting while reducing reliance on computationally intensive MCMC simulations required for standard inference.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"58 ","pages":"Article 100832"},"PeriodicalIF":1.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148656532","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":"A study on the impact of climate-adjusted greenspace exposure on mosquito-borne disease risk in Guangzhou City","authors":"Yingxuan Zheng,&nbsp;Yingbiao Chen,&nbsp;Yin Liu,&nbsp;Yuting Chen,&nbsp;Jie Li","doi":"10.1016/j.sste.2026.100837","DOIUrl":"10.1016/j.sste.2026.100837","url":null,"abstract":"<div><div>This study investigates the association between climate-adjusted greenspace exposure and street-level mosquito vector surveillance indices in Guangzhou, China—a rapidly urbanizing subtropical city. Using high-resolution greenspace data and climate reanalysis products (2000–2023), we developed three novel population-weighted exposure metrics: Heat-adjusted (HPGE), Wet-adjusted (WPGE), and Wet-Heat-adjusted Greenspace Exposure (WHPGE). These were linked to monthly mosquito-borne monitoring indices (May–November 2023) across all 176 subdistricts/towns. Applying spatial analytical methods (Standard Deviational Ellipse, Concentration Index) and Bayesian change-point detection, we quantified spatial associations, inequality patterns, and non-linear response thresholds. Results show that high mosquito index clusters are concentrated in south-central and eastern Guangzhou, with the Standard Space Index (SSI) exhibiting the strongest spatial alignment with greenspace exposure. HPGE shows the strongest correlation with BI (household container breeding), while WPGE is significantly associated with MOI (oviposition site abundance). Crucially, greenspace exposure shows divergent associations—sometimes protective, sometimes amplifying—with mosquito indices depending on local temperature, humidity, and exposure intensity. We identify distinct threshold intervals where risk trends reverse, revealing potential leverage points for intervention. Findings underscore that urban greening strategies must consider climate adaptability and spatially targeted management to provide evidence for equitable urban planning and climate-resilient vector control in tropical cities.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"58 ","pages":"Article 100837"},"PeriodicalIF":1.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148843049","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":"A Bayesian additive spatiotemporal model for young adolescent birth records in the Philippines","authors":"John Gilbert P. Rabanal,&nbsp;Welfredo R. Patungan","doi":"10.1016/j.sste.2026.100830","DOIUrl":"10.1016/j.sste.2026.100830","url":null,"abstract":"<div><div>Quantitative studies about young adolescent pregnancy in the Philippines had been done through the analysis of survey data. Official birth records provide a more advantageous data source in examining the dynamics of the young adolescent pregnancy due to regular recording of births. In this study, we propose a statistical methodology utilizing official birth records through a Bayesian hierarchical framework. The method accounts for the unique features of the aggregated young adolescent birth counts such as being a spatiotemporal data with known upper bounds and being characterized by the presence of excess zero counts. Our framework consists of the binomial hurdle model and Poisson hurdle model with offset, the Leroux model for spatial effects (<span><span>Leroux et al., 2000</span></span>), and an additive nonparametric trend model for temporal effects (<span><span>Knorr-Held, 2000</span></span>). We analyze and assess the performance of the proposed models using the quarterly data on young adolescent birth counts in Luzon, Philippines from 2006 to 2019 via Hamiltonian Monte Carlo. Areas that are more prone to observe a young adolescent birth and a temporal pattern about the occurrence of young adolescent births are identified.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"58 ","pages":"Article 100830"},"PeriodicalIF":1.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148656533","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":"Zonal variations in childhood stunting in Ethiopia: Bayesian geo-additive analysis","authors":"Solomon Keflie Assefa,&nbsp;Adem Tsegaw Zegeye,&nbsp;Helina Mebratu Abegaz,&nbsp;Belayneh Jejaw Abate,&nbsp;Dejen Kahsay Asgedom,&nbsp;Eliyas Addisu Taye,&nbsp;Endalew Minwuye Andargie,&nbsp;Eyob Akalewold Alemu,&nbsp;Halima Ayalew Kebede,&nbsp;Helen Birhan Alemaw,&nbsp;Simachew Getaneh Endalamew,&nbsp;Sofiya Ayalew Kebede","doi":"10.1016/j.sste.2026.100835","DOIUrl":"10.1016/j.sste.2026.100835","url":null,"abstract":"<div><h3>Introduction</h3><div>Childhood stunting remains a major public health challenge in Ethiopia, affecting more than one-third of children under five years of age. Achieving the Seqota Declaration target of ending stunting by 2030 requires understanding where the burden is highest and the factors driving geographic disparities. This study provides the first zonal-level analysis of childhood stunting in Ethiopia, identifying key determinants and spatial patterns to inform targeted interventions.</div></div><div><h3>Methods</h3><div>Data from 4916 children under five years of age from the 2019 Ethiopian Mini Demographic and Health Survey were analyzed. A Bayesian semiparametric geo-additive model was fitted using Markov Chain Monte Carlo methods to estimate linear, nonlinear, and spatial effects while separating structured and unstructured spatial variation across zones.</div></div><div><h3>Results</h3><div>Substantial spatial variation in childhood stunting persisted after adjusting for socioeconomic, demographic, and maternal factors. High-risk zones were concentrated in northern Ethiopia, particularly Wag Hemra, Southern Tigray, and Afar Zone 2. Low maternal education, younger maternal age, early age at first birth, higher birth order, multiple births, rural residence, and household poverty were significantly associated with stunting. Residual spatial clustering indicated the presence of additional unmeasured local factors.</div></div><div><h3>Conclusions</h3><div>Childhood stunting remains unevenly distributed across Ethiopian zones, with distinct geographic hotspots persisting beyond measured risk factors. Targeting high-burden zones and incorporating spatial evidence into nutrition and maternal-child health programs will be essential for accelerating progress toward Ethiopia's goal of ending childhood stunting by 2030.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"58 ","pages":"Article 100835"},"PeriodicalIF":1.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148736666","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":"Seasonal climate synchronizes dengue timing but not spatial risk in subtropical Brazil","authors":"Renan de Souza Rezende,&nbsp;Franciele Mendonça Ferreira,&nbsp;Cristiano Ilha,&nbsp;Emanuel Rampanelli Cararo,&nbsp;Michele Victória Dalmutt Scarparo,&nbsp;Ana Luiza Caldatto,&nbsp;Laura Jablonski Straube,&nbsp;Amanda Caroline Martinelli Sehn,&nbsp;Adrieli Marsango de Bispo,&nbsp;Cássia Alves Lima-Rezende","doi":"10.1016/j.sste.2026.100831","DOIUrl":"10.1016/j.sste.2026.100831","url":null,"abstract":"<div><div>Climate-informed early-warning systems are increasingly used to anticipate dengue risk and guide preparedness at regional scales. These frameworks often assume that shared seasonal climate forcing produces spatially coherent epidemic dynamics across connected municipalities. We analyzed a decade-long dengue surveillance dataset from ten major municipalities in southern Brazil, integrating non-linear seasonal modelling, pairwise synchrony metrics, lagged climate-disease associations, and time-frequency coherence analyses. Seasonal temperature forcing strongly synchronized the timing of dengue epidemics across municipalities, with epidemic peaks consistently concentrated within a narrow seasonal window. In contrast, spatial synchrony in epidemic trajectories was weak and heterogeneous, with neighboring municipalities frequently exhibiting asynchronous or negatively correlated dynamics and no evidence of distance-dependent decay. Municipalities were synchronized by distinct temperature-related metrics under a shared thermal regime, indicating locally specific climate-transmission pathways. Climate-informed early-warning systems designed at regional scales may systematically misallocate surveillance and response resources in heterogeneous subtropical settings. Our findings indicate that shared climate seasonality constrains when dengue transmission is possible but does not ensure spatial coherence in epidemic dynamics. Public health preparedness and outbreak response should therefore be locally calibrated, incorporating municipality-specific climatic sensitivities to improve risk stratification and operational targeting in emerging dengue regions.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"58 ","pages":"Article 100831"},"PeriodicalIF":1.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148656534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What is the impact of the concentration of particulate matter on the respiratory health of households in urban Cameroon?","authors":"Yannick Cédric Ngangmo,&nbsp;Fabrice Nzepang,&nbsp;Adamou Ntieche,&nbsp;Cyrille Adiang Mezoue","doi":"10.1016/j.sste.2026.100836","DOIUrl":"10.1016/j.sste.2026.100836","url":null,"abstract":"<div><div>The galloping increase in the world's population is promoting growth in the number of cars in major cities, with an impact on air quality in urban areas caused by road traffic. Data on the concentration of particulate matter (PM) are obtained through an on-site data campaign in the city of Douala using the OC 300 Dust Particle Laser, a low-cost portable sensor. We surveyed 1,721 individuals in 344 households in the vicinity of the particulate pollutant measurement campaigns. The instrumental Logit model show that PM concentration have significantly associate with respiratory diseases, with specific effects of 0.231 (PM₁₀), 0.122 (PM₅) and 0.018 (PM_2.5). The results show that unsealed roads and traffic density have a strong influence on PM levels. Our results also show that socio-demographic characteristics, in particular the level of education and the distance of households from intersections, moderately influence the impacts of pollution.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"58 ","pages":"Article 100836"},"PeriodicalIF":1.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148776548","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":"Externally validated delay-embedded dynamic mode decomposition and annual-frequency SPOD reveal robust state-level dengue timing organization in Brazil","authors":"Marlon N.L. Malheiros,&nbsp;Antonio Pereira","doi":"10.1016/j.sste.2026.100833","DOIUrl":"10.1016/j.sste.2026.100833","url":null,"abstract":"<div><div>We evaluated delay-embedded dynamic mode decomposition (Hankel DMD) as a signal-diagnostic framework for annual dengue timing across Brazil. Weekly InfoDengue estimates were aggregated into 27 state-level incidence trajectories for 2015–2024. The primary model used state-standardized log1p incidence, a 26-week delay embedding, and rank 12. Annual structure was extracted with Hankel DMD, corroborated by annual-frequency spectral proper orthogonal decomposition (SPOD), and related to official OpenDataSUS epidemic onset in a state-year model adjusted for preseason climate, serotype information, and year. Robustness was assessed across 143 transformation-delay-rank specifications and by leave-one-year-out, rolling four-year, and annual-harmonic analyses. The primary Hankel mode had frequency 0.9865 cycles/year. SPOD selected 1.000 cycle/year, assigned 81.9% of leading-mode spectral energy to the annual bin, and reproduced the DMD phase pattern (circular correlation 0.996; median absolute difference 0.72 weeks). The adjusted phase coefficient was <span><math><mrow><mo>−</mo><mn>2.23</mn></mrow></math></span> weeks per standard deviation (95% CI <span><math><mrow><mo>−</mo><mn>2.78</mn></mrow></math></span> to <span><math><mrow><mo>−</mo><mn>1.80</mn></mrow></math></span>), indicating earlier official onset toward the leading end of the externally oriented phase. Of 143 sensitivity specifications, 135 yielded an unambiguous annual mode; their median phase correlation with the primary result was 0.969 and median absolute difference was 1.09 weeks. Leave-one-year-out correlations ranged from 0.984 to 0.999 and were not reduced when major epidemic years were omitted (permutation <span><math><mrow><mi>p</mi><mo>=</mo><mn>0.7384</mn></mrow></math></span>). These analyses identify a reproducible state-level annual timing structure. The phase coordinate represents relative timing within the annual mode; its epidemiological orientation is established by the onset data and should not be interpreted as a causal transmission parameter.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"58 ","pages":"Article 100833"},"PeriodicalIF":1.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148738290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intra-urban inequalities in the community food environment of a Brazilian metropolitan region: An analysis of food deserts, swamps, and oases","authors":"Emília Chagas Costa ,&nbsp;Vanessa Sá Leal ,&nbsp;Juliana Souza Oliveira ,&nbsp;Nathalia Paula de Souza ,&nbsp;Emerson Iago Garcia e Silva ,&nbsp;Marco Aurélio de Valois Correia Júnior ,&nbsp;Risia Cristina Egito de Menezes ,&nbsp;Pedro Israel Cabral de Lira ,&nbsp;Paula Santana ,&nbsp;Ricardo Almendra","doi":"10.1016/j.sste.2025.100768","DOIUrl":"10.1016/j.sste.2025.100768","url":null,"abstract":"<div><div>The food environment in metropolitan areas is characterized by population concentration, the dynamism of food systems, and the intensity of social, cultural, and labor flows. In Brazil, these areas reflect profound intra-urban social and economic inequalities that condition access to food choices. This work aims to characterize the food environment, identifying areas of deserts, swamps, and oases according to health vulnerabilities in a context of social inequalities. A cross-sectional and ecological study was performed. A total of 231 food stores were audited, using buffer analysis(1600 m) and census data. Food deserts, swamps, and oases were classified according to the methodology of the CDC from the Modified Retail Food Environment Index(number of healthy food stores/total available). Information on social vulnerabilities involved sanitation, socioeconomic aspects, and the Health Vulnerability Index of the census tracts. The analyzed region was classified as highly vulnerable in terms of health and no food oases were found. There was scarce availability of healthy foods, with over 90 % of the territory classified as a food swamp. The populations living in the included areas reported a low average monthly income and there were high percentages of black, brown, and indigenous individuals. In a context of social inequality, the region studied presents high health vulnerability, with socioeconomic inequalities and a greater concentration of black, brown, and indigenous people in areas of food deserts and swamps. These findings reinforce the need for public policies that guarantee equity in the distribution of establishments that offer healthy foods, ensuring their accessibility to the population.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"56 ","pages":"Article 100768"},"PeriodicalIF":1.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145610581","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":"Individual level modeling of infectious disease transmission with reinfection dynamics: Application to Tuberculosis in Manitoba, Canada","authors":"Amin Abed ,&nbsp;Mahmoud Torabi ,&nbsp;Zeinab Mashreghi","doi":"10.1016/j.sste.2025.100780","DOIUrl":"10.1016/j.sste.2025.100780","url":null,"abstract":"<div><div>Recent advancements in stochastic modeling of infectious disease transmission have increasingly incorporated spatial factors, enhancing the accuracy of disease spread predictions and public health interventions. For many infectious diseases, reinfection is a key factor that impacts disease dynamics, epidemic progression, prevalence, and control efforts, complicating management strategies. Accurately incorporating reinfection into disease modeling is essential for developing effective interventions. This study expands upon previously proposed Geographically Dependent Individual Level Models (GD-ILMs) of infectious diseases by integrating them within a Susceptible–Exposed–Infectious–Recovered–Susceptible (SEIRS) compartmental framework, termed GD-ILM SEIRS, to consider reinfection. A Monte Carlo Expectation Conditional Maximization algorithm was employed to estimate the parameters of the model. The GD-ILM SEIRS was applied to Tuberculosis data from Manitoba, Canada, covering the period from 2011 to 2018. It considers spatial dependencies, along with individual and regional risk factors influencing susceptibility to initial infection, reinfection, and infectivity. An analysis of Manitoba’s health authority districts highlights specific risk factors related to susceptibility to initial infection, reinfection, and infectivity. Additionally, the fitted model enables calculation of infection probabilities at high-resolution geographic scales. The results allow for targeted interventions and optimized resource allocation by detecting high-risk areas and vulnerable populations to reduce transmission rates, prevent reinfection, and enhance health outcomes in Manitoba. Moreover, a simulation study across various grid configurations demonstrates the model’s effectiveness in estimating parameters. This study highlights the need to integrate reinfection dynamics into infectious disease models to strengthen the impact of public health interventions and disease control strategies.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"56 ","pages":"Article 100780"},"PeriodicalIF":1.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840220","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}