Spatial and Spatio-Temporal Epidemiology最新文献_第5页

Investigating the impact of precipitation and temperature on snakebite mortality in India: A spatial case-crossover study. 调查降水和温度对印度蛇咬伤死亡率的影响：一个空间病例交叉研究。

IF 1.5

Spatial and Spatio-Temporal Epidemiology Pub Date : 2025-08-01 Epub Date: 2025-08-05 DOI: 10.1016/j.sste.2025.100738

Guowen Huang, Patrick E Brown, Marta Blangiardo

引用次数: 0

Prevalence and spatial distribution of Toxoplasma gondii infection in domestic and stray cats (Felis catus) in Northwestern São Paulo, Brazil 巴西圣保罗西北部地区家猫和流浪猫刚地弓形虫感染流行及空间分布

IF 1.7

Spatial and Spatio-Temporal Epidemiology Pub Date : 2025-08-01 Epub Date: 2025-08-05 DOI: 10.1016/j.sste.2025.100740

Fernando Henrique Antunes Murata , Jéssica Priscilla Barboza , Fernanda Follis Tasso , Tainara Souza Pinho , Tiago Henrique , Janine Fusco Alves , FAMERP Toxoplasma Research Group , Carlos Alexandre Guimarães de Souza , Daniel Abrahão , Ubirajara Leoncy de Lavor , Chunlei Su , Luiz Carlos de Mattos , Cinara Cássia Brandão

{"title":"Prevalence and spatial distribution of Toxoplasma gondii infection in domestic and stray cats (Felis catus) in Northwestern São Paulo, Brazil","authors":"Fernando Henrique Antunes Murata , Jéssica Priscilla Barboza , Fernanda Follis Tasso , Tainara Souza Pinho , Tiago Henrique , Janine Fusco Alves , FAMERP Toxoplasma Research Group , Carlos Alexandre Guimarães de Souza , Daniel Abrahão , Ubirajara Leoncy de Lavor , Chunlei Su , Luiz Carlos de Mattos , Cinara Cássia Brandão","doi":"10.1016/j.sste.2025.100740","DOIUrl":"10.1016/j.sste.2025.100740","url":null,"abstract":"<div><div>Toxoplasmosis is a zoonotic disease caused by the apicomplexan parasite <em>Toxoplasma gondii</em>, that can infect any warm-blooded animal, including mammals and birds. Felids are the definitive hosts, with infected cats capable of shedding millions of resistant oocysts into the environment. This study aimed to evaluate the seroprevalence and geospatial distribution of <em>T. gondii</em> infection in pet and stray cats attended at the Zoonosis Control Center in São José do Rio Preto, northwest São Paulo, Brazil. Anti-<em>T. gondii</em> antibodies were detected in 36 (25.2 %) of 143 pet cats and 85 (27.8 %) of 306 stray cats, with an overall prevalence of 26.9 %. Male pet cats exhibited a significantly higher risk of infection compared to females (19.5 % vs 34.5 %; <em>p</em> = 0.045). Regional analysis revealed significant difference in seroprevalence between four regions (HB vs Bosque for pet cats, <em>p</em> = 0.035, and Cidade da Criança vs Central for stray cats, <em>p</em> = 0.040). Spatial cluster analysis identified 27 significant hotspots and 70 coldspots (<em>p</em> ≤ 0.05) throughout the municipality. This study represents the first investigation of the seroprevalence and geospatial distribution of <em>T. gondii</em> infection in domestic and stray cats within this region, providing valuable information on the epidemiology of <em>T. gondii</em>. These findings contribute to a better understanding of the transmission dynamics of <em>T. gondii</em>, supporting the development of effective prevention strategies and reinforcing the importance of a One Health approach.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"54 ","pages":"Article 100740"},"PeriodicalIF":1.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144866637","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}

引用次数: 0

Understanding the COVID-19 pandemic through bayesian spatio-temporal modeling of several outcomes 通过几种结果的贝叶斯时空建模来理解COVID-19大流行

IF 2.1

Spatial and Spatio-Temporal Epidemiology Pub Date : 2025-08-01 Epub Date: 2025-07-18 DOI: 10.1016/j.sste.2025.100737

Lander Rodriguez-Idiazabal , Miguel Angel Martinez-Beneito , Jose M. Quintana , Julia Garcia-Asensio , Maria Jose Legarreta , Nere Larrea , Irantzu Barrio

{"title":"Understanding the COVID-19 pandemic through bayesian spatio-temporal modeling of several outcomes","authors":"Lander Rodriguez-Idiazabal , Miguel Angel Martinez-Beneito , Jose M. Quintana , Julia Garcia-Asensio , Maria Jose Legarreta , Nere Larrea , Irantzu Barrio","doi":"10.1016/j.sste.2025.100737","DOIUrl":"10.1016/j.sste.2025.100737","url":null,"abstract":"<div><div>Understanding the spatio-temporal dynamics of past pandemics and the factors driving these patterns can enhance preparedness against future pandemics. This study aimed to investigate the COVID-19 pandemic by analyzing the spatio-temporal variations in infections, hospitalizations, deaths and reinfections.</div><div>We conducted a retrospective analysis of data from the adult population of the Basque Country at Primary Care Unit level from March 1, 2020 to January 9, 2022. Using a Bayesian hierarchical spatio-temporal model, we estimated relative risks for each outcome, accounting for the effects of a deprivation index, urbanicity, and COVID-19 testing rates.</div><div>SARS-CoV-2 infections and mortality followed similar risk patterns, with a strong clustering in highly populated areas. Hospitalization risks were influenced by proximity to hospitals, revealing potential access barriers in remote areas. High reinfection risks were predominantly localized in the northwest coast of our region. Increased testing rates were associated with higher risks across all outcomes. Urbanicity showed positive associations with hospitalizations (relative risk, [95 % credible interval]: 1.22, [1.12–1.33]) and infections (1.34, [1.15–1.57]). Similarly, deprivation was positively associated with hospitalization risks (1.09, [1.04–1.15]) and mortality risks (1.07, [1.02–1.12]), reflecting the increased vulnerability of socioeconomically disadvantaged populations.</div><div>This comprehensive analysis of various COVID-19 outcomes provides valuable insights into the pandemic’s spatio-temporal dynamics and highlights key improvement areas. Addressing healthcare access disparities in rural areas and focusing on the deprived populations could help mitigate the impact of future pandemics. This approach could be extended to other regions to inform specific public health strategies.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"54 ","pages":"Article 100737"},"PeriodicalIF":2.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703646","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}

引用次数: 0

Evaluation of spatial and non-spatial factors on tuberculosis using geospatial information system and fuzzy logic 基于地理空间信息系统和模糊逻辑的结核病空间与非空间因素评价

IF 2.1

Spatial and Spatio-Temporal Epidemiology Pub Date : 2025-08-01 Epub Date: 2025-06-05 DOI: 10.1016/j.sste.2025.100729

Mahsa Rakhsh Khorshid , Saeed Behzadi , Alireza Sharifi , Alireza Vafaeinejad , Ziba Abbasian , Hossein Naderi

{"title":"Evaluation of spatial and non-spatial factors on tuberculosis using geospatial information system and fuzzy logic","authors":"Mahsa Rakhsh Khorshid , Saeed Behzadi , Alireza Sharifi , Alireza Vafaeinejad , Ziba Abbasian , Hossein Naderi","doi":"10.1016/j.sste.2025.100729","DOIUrl":"10.1016/j.sste.2025.100729","url":null,"abstract":"<div><div>Tuberculosis is a deadly infectious disease that has not been eradicated yet. The prevalence of this disease is still very high in some parts of the world, so it is considered a deadly disease. Iran is one of the countries which has not yet achieved the ability to eliminate this disease. The prevalence of tuberculosis is relatively higher in some provinces than in the other ones. Sistan and Baluchestan is the province with high rates of tuberculosis. In this paper, the factors affecting tuberculosis are modeled in Sistan and Baluchestan province using Geospatial Information Systems (GIS) and FL. This research contains two general analyzes. In the first analysis, three different scenarios of FL rules are presented. The first two scenarios examine spatial and non-spatial factors respectively. The third scenario also examines the combination of spatial and non-spatial factors simultaneously. As a result, the effect of spatial and non-spatial factors on tuberculosis is obtained. In the second analysis, a spatial scatter density map of tuberculosis is produced according to spatial data. This research reveals that the effects of spatial and non-spatial factors on tuberculosis are 57 % and 43 %, respectively. By comparing the results with samplings, the scatter rate map of tuberculosis is obtained with an accuracy of 71 %.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"54 ","pages":"Article 100729"},"PeriodicalIF":2.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144254152","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}

引用次数: 0

DeepEVD: Integrating Epidemiological data into deep learning frameworks based on spatio-temporal feature learning for EVD forecasting DeepEVD：将流行病学数据整合到基于时空特征学习的深度学习框架中，用于EVD预测

IF 1.7

Spatial and Spatio-Temporal Epidemiology Pub Date : 2025-08-01 Epub Date: 2025-08-22 DOI: 10.1016/j.sste.2025.100741

Abdul Joseph Fofanah , Alpha Alimamy Kamara , Albert Patrick Sankoh , Tiegang Gao , Ibrahim Dumbuya , Zachariyah Bai Conteh

{"title":"DeepEVD: Integrating Epidemiological data into deep learning frameworks based on spatio-temporal feature learning for EVD forecasting","authors":"Abdul Joseph Fofanah , Alpha Alimamy Kamara , Albert Patrick Sankoh , Tiegang Gao , Ibrahim Dumbuya , Zachariyah Bai Conteh","doi":"10.1016/j.sste.2025.100741","DOIUrl":"10.1016/j.sste.2025.100741","url":null,"abstract":"<div><div>The paper introduces DeepEVD, an innovative framework that integrates human mobility data to forecast Ebola Virus Disease (EVD) outbreaks. Traditional epidemiological models often struggle to account for the dynamic nature of human movement, which is crucial for understanding EVD transmission. DeepEVD leverages diverse mobility data sources, including phone records, GPS traces, and social media posts, to extract significant spatio-temporal features. It utilises Graph Convolutional Networks (GCN) and Long Short Term Memory (LSTM) networks to establish connections between mobility patterns and EVD cases across both space and time. The framework was tested on real-world datasets from the 2014–2016 West Africa outbreak and the 2015–2016 Sierra Leone outbreak, demonstrating a 5%–10% reduction in forecasting errors compared to baseline methods. Ablation studies reveal the impact of various data sources and feature extraction methods on accuracy. DeepEVD not only delivers state-of-the-art performance, but it also provides actionable insights for EVD prevention and control. Implementation of the proposed DeepEVD can be accessed here <span><span>https://github.com/afofanah/DeepEVDMob</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"54 ","pages":"Article 100741"},"PeriodicalIF":1.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907263","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}

引用次数: 0

A Bayesian spatial measurement error approach to incorporate heterogeneous population-at-risk uncertainty in estimating small-area opioid mortality rates 估算小区域阿片类药物死亡率时纳入异质性高危人群不确定性的贝叶斯空间测量误差方法

IF 2.1

Spatial and Spatio-Temporal Epidemiology Pub Date : 2025-06-01 Epub Date: 2025-03-31 DOI: 10.1016/j.sste.2025.100719

Emily N. Peterson , Rachel C. Nethery , Jarvis T. Chen , Loni P. Tabb , Brent A. Coull , Frederic B. Piel , Lance A. Waller

{"title":"A Bayesian spatial measurement error approach to incorporate heterogeneous population-at-risk uncertainty in estimating small-area opioid mortality rates","authors":"Emily N. Peterson , Rachel C. Nethery , Jarvis T. Chen , Loni P. Tabb , Brent A. Coull , Frederic B. Piel , Lance A. Waller","doi":"10.1016/j.sste.2025.100719","DOIUrl":"10.1016/j.sste.2025.100719","url":null,"abstract":"<div><div>Monitoring small-area geographical population trends in opioid mortality has significant implications for informing preventative resource allocation. A common approach to estimating small-area opioid mortality uses a standard disease mapping method where population-at-risk estimates (denominators) are treated as fixed. This assumption ignores the uncertainty in small-area population estimates, potentially biasing risk estimates and underestimating their uncertainties. We compare a Bayesian Spatial Berkson Error model and a Bayesian Spatial Classical Error model to a naive approach that treats denominators as fixed. Using simulations, we illustrate potential bias from ignored population-at-risk uncertainty. We apply these methods to obtain 2020 opioid mortality risk estimates for 159 counties in Georgia. Assessing differences in bias and uncertainty across approaches can improve the accuracy of small-area opioid risk estimates, guiding public health interventions, policies, and resource allocation.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"53 ","pages":"Article 100719"},"PeriodicalIF":2.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800550","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}

引用次数: 0

Integrating data at multiple spatial scales to estimate the local burden of the opioid syndemic 整合多个空间尺度的数据，以估计阿片类药物综合征的当地负担

IF 2.1

Spatial and Spatio-Temporal Epidemiology Pub Date : 2025-06-01 Epub Date: 2025-04-22 DOI: 10.1016/j.sste.2025.100720

Eva Murphy , David Kline , Erin McKnight , Andrea Bonny , William C. Miller , Lance Waller , Staci A. Hepler

{"title":"Integrating data at multiple spatial scales to estimate the local burden of the opioid syndemic","authors":"Eva Murphy , David Kline , Erin McKnight , Andrea Bonny , William C. Miller , Lance Waller , Staci A. Hepler","doi":"10.1016/j.sste.2025.100720","DOIUrl":"10.1016/j.sste.2025.100720","url":null,"abstract":"<div><div>The opioid epidemic has been particularly severe in Ohio, prompting significant efforts to understand its spatial patterns, mainly using available data at the county level. However, relying solely on county-level analysis can overlook crucial information relevant to localized effects. To address this, we integrate spatially misaligned data observed at the county and ZIP code levels to explore the complex interaction of five opioid-related outcomes, providing a more detailed local understanding of the opioid epidemic. We demonstrate how to map ZIP-code level data to ZIP-code Tabulation Areas (ZCTAs) and relate the county-level and ZCTA-level outcomes to a spatially correlated latent factor. The latent factor is defined on the intersection of the misaligned areal units, which provides a more granular understanding of the opioid epidemic. Furthermore, this approach allows us to identify areas with varying levels of opioid burden and reveals local regions with relatively high burden that county-level analyses might miss. Finally, we highlight the need for careful consideration when relying solely on ZIP code level data for naloxone, as it may lead to misinterpretations, particularly in rural regions.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"53 ","pages":"Article 100720"},"PeriodicalIF":2.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863591","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}

引用次数: 0

Disease mapping with individual level information; a case study of acute myocardial infarction mortality 基于个体水平信息的疾病制图；急性心肌梗死死亡率个案研究

IF 2.1

Spatial and Spatio-Temporal Epidemiology Pub Date : 2025-06-01 Epub Date: 2025-04-28 DOI: 10.1016/j.sste.2025.100721

Xavier Puig, Josep Ginebra

引用次数: 0

Physician and healthcare partner engagement in the creation of healthfulness indices for West Michigan 医生和医疗保健合作伙伴参与创建健康指数为西密歇根州

IF 2.1

Spatial and Spatio-Temporal Epidemiology Pub Date : 2025-06-01 Epub Date: 2025-04-19 DOI: 10.1016/j.sste.2025.100722

Richard Casey Sadler , Samantha Gailey , Erin R. McNeely

{"title":"Physician and healthcare partner engagement in the creation of healthfulness indices for West Michigan","authors":"Richard Casey Sadler , Samantha Gailey , Erin R. McNeely","doi":"10.1016/j.sste.2025.100722","DOIUrl":"10.1016/j.sste.2025.100722","url":null,"abstract":"<div><div>Community participatory mapping can direct health research, offering opportunity to build spatial awareness and generate future research. Here we establish healthfulness indices by consulting healthcare system partners for their expert opinions on characteristics they felt influenced health. Partners started from 36 variables and narrowed to 16 in 4 simplified categories. The analytic hierarchy process was used to identify variable and category weights. Opinions were consolidated for each partner sub-group and overall. Map layers were assigned calculated weights and indices were created from weighted layers. Areas with more amenities scored higher, including in and around downtown areas and smaller towns. Lower scores were found in suburban and lower-income urban areas. Variation in maps among subgroups reflect differing priorities in tackling health equity issues. This work increases healthcare partner engagement in built environment work and generates future research pathways. Partners now have a tool for interrogating and communicating the environment’s cumulative impact.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"53 ","pages":"Article 100722"},"PeriodicalIF":2.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860326","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}

引用次数: 0

A multivariate generalized logistic approach with spatially varying nonlinear components for modeling epidemic data 一种具有空间变化非线性分量的流行病数据建模的多元广义逻辑方法

IF 2.1

Spatial and Spatio-Temporal Epidemiology Pub Date : 2025-06-01 Epub Date: 2025-04-04 DOI: 10.1016/j.sste.2025.100718

Marcos O. Prates , Dani Gamerman , Samuel F. Candido , Luis M. Castro

引用次数: 0