Spatial and Spatio-Temporal Epidemiology最新文献

{"title":"Social and territorial barriers to healthcare access and care resolutiveness in quilombola communities of the Brazilian Amazon","authors":"Leanna Silva Aquino,&nbsp;Ana Camille Viana Falcao Brito,&nbsp;Rayane Pires da Silva,&nbsp;Lais Aguiar Barrozo,&nbsp;Raquel Castro Carvalho,&nbsp;Ellen Mara Fernandes da Silva,&nbsp;Victoria Valentim Aguiar,&nbsp;Cesar Ferreira Fernandes Filho,&nbsp;Sheyla Mara Silva de Oliveira,&nbsp;Tatiane Costa Quaresma,&nbsp;Valney Mara Gomes Conde,&nbsp;Nivia Saldanha Xavier Oliveira,&nbsp;Jose Eilton de Oliveira,&nbsp;Ézyo Leonardo Calderaro Soares,&nbsp;Mirla Rego Ribeiro,&nbsp;Guilherme Augusto Barros Conde,&nbsp;Franciane de Paula Fernandes,&nbsp;Lívia de Aguiar Valentim","doi":"10.1016/j.sste.2026.100857","DOIUrl":"10.1016/j.sste.2026.100857","url":null,"abstract":"<div><h3>Background</h3><div>Healthcare access among quilombola communities in the Brazilian Amazon is shaped by socioeconomic and territorial conditions. This study examined factors related to healthcare access and service resolutiveness among quilombola residents.</div></div><div><h3>Methods</h3><div>This cross-sectional study included 574 participants from eight quilombola communities in Santarém, Pará, Brazil. Healthcare use, healthcare obtained outside the community, and problem resolution were examined using crude and adjusted logistic regression models. Socioeconomic inequality was assessed using Gini, Theil, and concentration indices. Transportation cost burden was explored as an economic dimension of accessibility. Health-unit availability was analyzed descriptively because only one community had a local health unit.</div></div><div><h3>Results</h3><div>Women reported higher healthcare use than men (73.9 %vs. 59.6 %; p &lt; 0.001). Educational attainment was not associated with the outcomes. Household income was associated with problem resolution in crude analysis (global p = 0.035), but the association was attenuated after adjustment (p = 0.106), with evidence of non-linearity. Income inequality was observed (Gini=0.399; Theil=0.287), without clear socioeconomic concentration of healthcare outcomes. Transportation cost burden decreased with increasing income (ρ=−0.509; p &lt; 0.001), whereas absolute transportation cost did not (ρ=0.039; p = 0.421). In Tiningu, the only community with a local health unit, 23.1 % obtained healthcare outside the community versus 63.9 % in the other communities; this comparison was descriptive.</div></div><div><h3>Conclusions</h3><div>Healthcare access did not follow a consistent socioeconomic gradient. However, transportation imposed a greater potential economic burden on lower-income households. The findings highlight the importance of considering service availability, territorial mobility, and the economic conditions required to reach healthcare in remote quilombola communities.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"59 ","pages":"Article 100857"},"PeriodicalIF":1.5,"publicationDate":"2026-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149362132","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":"Spatiotemporal modeling of climatic effects on dengue transmission across Mexican municipalities","authors":"Oluwatosin Babasola,&nbsp;Olumuyiwa James Peter,&nbsp;Edwin Moyo,&nbsp;Kayode Oshinubi","doi":"10.1016/j.sste.2026.100858","DOIUrl":"10.1016/j.sste.2026.100858","url":null,"abstract":"<div><div>Dengue remains one of the fastest expanding mosquito borne viral diseases, with transmission strongly influenced by climatic conditions. In Mexico, recurrent dengue outbreaks show substantial geographic variation across municipalities, yet the extent to which climatic variability and spatial dependence contribute to this heterogeneity remains insufficiently understood. This study examined the spatiotemporal distribution of dengue incidence across Mexican municipalities and quantified associations with climatic variability and spatial dependence using a hierarchical spatial model. Municipality level surveillance data were compiled into a weekly panel of confirmed dengue cases and linked with gridded temperature, rainfall, and relative humidity data. Temporal relationships were assessed using weekly national time series and lagged correlation analysis, while spatial dependence was evaluated using Moran’s <span><math><mi>I</mi></math></span> statistics. A negative binomial regression model with conditional autoregressive spatial random effects was used to estimate associations between dengue incidence and contemporaneous climatic conditions, with additional adjustment for population density. The results showed pronounced seasonal and interannual variation in dengue incidence, with climatic variables exhibiting distinct temporal associations with dengue activity. Significant positive spatial autocorrelation was observed throughout the study period, with elevated dengue burden concentrated primarily along the Gulf of Mexico, the Yucatán Peninsula, and parts of the Pacific coast. The spatial model showed positive associations of temperature and population density with dengue incidence, while substantial residual spatial variation remained after adjustment for the measured covariates. These findings indicate that climatic, demographic, and spatial processes contribute to geographic variation in dengue transmission across Mexico and support spatially explicit approaches for targeted surveillance and prevention.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"59 ","pages":"Article 100858"},"PeriodicalIF":1.5,"publicationDate":"2026-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149362134","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":"Temporal analysis using machine learning of morbidity and mortality from vector-borne diseases in Brazilian municipalities","authors":"Nícholas de Paula Nicomedes,&nbsp;Albina Santos Costa,&nbsp;Gabriela Gomes,&nbsp;Arthur Pereira dos Santos,&nbsp;Darllan Collins da Cunha e Silva","doi":"10.1016/j.sste.2026.100855","DOIUrl":"10.1016/j.sste.2026.100855","url":null,"abstract":"<div><div>This study examined morbidity and mortality from four vector-borne diseases—dengue, yellow fever, leishmaniasis, and malaria—in Brazilian municipalities from 1999 to 2022. Rates were compared between eight triennia (Kruskal–Wallis, Dunn post-hoc) and tested for monotonic trend (Mann–Kendall, Theil–Sen) at national and municipal levels. Because the four diseases differ in vector and transmission ecology, a separate Random Forest was estimated for each disease–outcome combination on the full 1999–2022 municipality-year panel, under a common covariate set, with calendar year as a categorical split variable and covariates standardized within each year; importance uncertainty was estimated by resampling municipalities as clusters. Temporal patterns were heterogeneous, malaria declined consistently in both morbidity and mortality, dengue mortality increased monotonically, dengue morbidity rose in 16.5% of municipalities without a monotonic national trend, yellow fever was confined to the 2017–2018 sylvatic epidemic, and leishmaniasis showed no trend. Municipalities with extreme rates were concentrated in the North, Northeast, and Central-West, regions combining rapid urban growth, social vulnerability, and environmental degradation. Predictive performance was negligible, out-of-bag R² ranged from −0.03 to 0.08 and fell to −0.05 to 0.02 under cross-validation grouped by municipality, so variable importance is reported as a ranking within low-performing models, not as evidence of prediction or causation. On that basis, the urbanization index was the covariate most frequently ranked first, followed by population density and CO₂ emissions. The framework provides a descriptive basis for targeting surveillance under the Sustainable Development Goals (SDGs 3, 10, and 13).</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"59 ","pages":"Article 100855"},"PeriodicalIF":1.5,"publicationDate":"2026-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149362723","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":"Do social frontiers matter for depression?","authors":"Duncan Lee,&nbsp;Gwilym Pryce,&nbsp;Miguel R. Ramos","doi":"10.1016/j.sste.2026.100854","DOIUrl":"10.1016/j.sste.2026.100854","url":null,"abstract":"<div><div>Social frontiers – abrupt borders between communities – may heighten territorial and defencive behaviour, reduce opportunities for positive contact between groups, and exacerbate the sense of outgroup threat, resulting in a negative impact on mental ill health such as depression for residents living in neighbourhoods bounded by social frontiers. Previous research on the links between residential segregation and depression has largely ignored the effect of social frontiers. To study this association we link Place Based Longitudinal Data Resource data on the numbers of depression diagnoses and antidepressant drugs prescribed by GPs with estimates of ethnic and religious social frontiers produced from the 2011 and 2021 Census for all Lower Super Output Areas in England. The estimated associations are produced from spatial binomial/Poisson regression models that allow for spatial autocorrelation via adding a spatially lagged response term to the model. We find strong and consistent evidence of an association between the prevalence of depression at the neighbourhood level (Lower Super Output Areas) in England and the intensity of social frontiers for particular ethnic (Chinese, Indian, Pakistani, White British) and religious (Hindu, Jewish, Muslim) groups. For example, in 2021 depression rates are estimated to be between 1.5% and 2.2% higher for every 5% point increase in the intensity of social frontiers between Chinese and non-Chinese residents. These results demonstrate the importance of understanding the role of community boundaries when considering the links between segregation and mental wellbeing.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"59 ","pages":"Article 100854"},"PeriodicalIF":1.5,"publicationDate":"2026-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149362133","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":"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}