Spatial and Spatio-Temporal Epidemiology最新文献_第9页

Restricted spatial models for the analysis of geographic and racial disparities in the incidence of low birthweight in Pennsylvania 用于分析宾夕法尼亚州出生体重不足发生率的地域和种族差异的限制性空间模型

IF 3.4

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-06-01 Epub Date: 2024-03-23 DOI: 10.1016/j.sste.2024.100649

Guangzi Song , Loni Philip Tabb , Harrison Quick

{"title":"Restricted spatial models for the analysis of geographic and racial disparities in the incidence of low birthweight in Pennsylvania","authors":"Guangzi Song , Loni Philip Tabb , Harrison Quick","doi":"10.1016/j.sste.2024.100649","DOIUrl":"10.1016/j.sste.2024.100649","url":null,"abstract":"<div><p>The incidence of low birthweight is a common measure of public health due to the increased risk of complications associated with infants having low and very low birthweights. Moreover, many factors that increase the risk of an infant having a low birthweight can be linked to the mother’s socioeconomic status, leading to large racial/ethnic disparities in its incidence. Our objective is thus to analyze the incidence of low and very low birthweight in Pennsylvania counties by race/ethnicity. Due to the small number of births in many Pennsylvania counties when stratified by race/ethnicity, our methods must walk a fine line: While we wish to leverage spatial structure to improve the precision of our estimates, we also wish to avoid oversmoothing the data, which can yield spurious conclusions. As such, we develop a framework by which we can measure (and control) the informativeness of our spatial model. To analyze the data, we first model the Pennsylvania birth data using the conditional autoregressive model to demonstrate the extent to which it can lead to oversmoothing. We then reanalyze the data using our proposed framework and highlight its ability to detect (or not detect) evidence of racial/ethnic disparities in the incidence of low birthweight.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"49 ","pages":"Article 100649"},"PeriodicalIF":3.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140275638","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

Model-based disease mapping using primary care registry data 利用初级保健登记数据绘制基于模型的疾病分布图

IF 3.4

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-06-01 Epub Date: 2024-05-03 DOI: 10.1016/j.sste.2024.100654

Arne Janssens , Bert Vaes , Gijs Van Pottelbergh , Pieter J.K. Libin , Thomas Neyens

{"title":"Model-based disease mapping using primary care registry data","authors":"Arne Janssens , Bert Vaes , Gijs Van Pottelbergh , Pieter J.K. Libin , Thomas Neyens","doi":"10.1016/j.sste.2024.100654","DOIUrl":"https://doi.org/10.1016/j.sste.2024.100654","url":null,"abstract":"<div><h3>Background:</h3><p>Spatial modeling of disease risk using primary care registry data is promising for public health surveillance. However, it remains unclear to which extent challenges such as spatially disproportionate sampling and practice-specific reporting variation affect statistical inference.</p></div><div><h3>Methods:</h3><p>Using lower respiratory tract infection data from the INTEGO registry, modeled with a logistic model incorporating patient characteristics, a spatially structured random effect at municipality level, and an unstructured random effect at practice level, we conducted a case and simulation study to assess the impact of these challenges on spatial trend estimation.</p></div><div><h3>Results:</h3><p>Even with spatial imbalance and practice-specific reporting variation, the model performed well. Performance improved with increasing spatial sample balance and decreasing practice-specific variation.</p></div><div><h3>Conclusion:</h3><p>Our findings indicate that, with correction for reporting efforts, primary care registries are valuable for spatial trend estimation. The diversity of patient locations within practice populations plays an important role.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"49 ","pages":"Article 100654"},"PeriodicalIF":3.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877584524000212/pdfft?md5=2abc0e361764c74dc95a5694546fab63&pid=1-s2.0-S1877584524000212-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140947078","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}

引用次数: 0

Geospatial patterns of excess mortality in Belgium: Insights from the first year of the COVID-19 pandemic 比利时超高死亡率的地理空间模式：COVID-19 大流行第一年的启示

IF 3.4

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-06-01 Epub Date: 2024-05-20 DOI: 10.1016/j.sste.2024.100660

Yessika Adelwin Natalia , Geert Molenberghs , Christel Faes , Thomas Neyens

{"title":"Geospatial patterns of excess mortality in Belgium: Insights from the first year of the COVID-19 pandemic","authors":"Yessika Adelwin Natalia , Geert Molenberghs , Christel Faes , Thomas Neyens","doi":"10.1016/j.sste.2024.100660","DOIUrl":"https://doi.org/10.1016/j.sste.2024.100660","url":null,"abstract":"<div><h3>Objectives:</h3><p>Belgium experienced multiple COVID-19 waves that hit various groups in the population, which changed the mortality pattern compared to periods before the pandemic. In this study, we investigated the geographical excess mortality trend in Belgium during the first year of the COVID-19 pandemic.</p></div><div><h3>Methods:</h3><p>We retrieved the number of deaths and population data in 2020 based on gender, age, and municipality of residence, and we made a comparison with the mortality data in 2017–2019 using a spatially discrete model.</p></div><div><h3>Results:</h3><p>Excess mortality was significantly associated with age, gender, and COVID-19 incidence, with larger effects in the second half of 2020. Most municipalities had higher risks of mortality with a number of exceptions in the northeastern part of Belgium. Some discrepancies in excess mortality were observed between the north and south regions.</p></div><div><h3>Conclusions:</h3><p>This study offers useful insight into excess mortality and will aid local and regional authorities in monitoring mortality trends.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"49 ","pages":"Article 100660"},"PeriodicalIF":3.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877584524000273/pdfft?md5=7a33aa87415bc6024b01678a9da46fea&pid=1-s2.0-S1877584524000273-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141091001","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}

引用次数: 0

Prediction of the size and spatial distribution of free-roaming dog populations in urban areas of Nepal 预测尼泊尔城市地区自由放养狗群的规模和空间分布

IF 3.4

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-06-01 Epub Date: 2024-03-12 DOI: 10.1016/j.sste.2024.100647

Sarah Tavlian , Mark A. Stevenson , Barbara Webb , Khageshwaar Sharma , Jim Pearson , Andrea Britton , Caitlin N. Pfeiffer

{"title":"Prediction of the size and spatial distribution of free-roaming dog populations in urban areas of Nepal","authors":"Sarah Tavlian , Mark A. Stevenson , Barbara Webb , Khageshwaar Sharma , Jim Pearson , Andrea Britton , Caitlin N. Pfeiffer","doi":"10.1016/j.sste.2024.100647","DOIUrl":"10.1016/j.sste.2024.100647","url":null,"abstract":"<div><p>A factor constraining the elimination of dog-mediated human rabies is limited information on the size and spatial distribution of free-roaming dog populations (FRDPs). The aim of this study was to develop a statistical model to predict the size of free-roaming dog populations and the spatial distribution of free-roaming dogs in urban areas of Nepal, based on real-world dog census data from the Himalayan Animal Rescue Trust (HART) and Animal Nepal. Candidate explanatory variables included proximity to roads, building density, specific building types, human population density and normalised difference vegetation index (NDVI). A multivariable Poisson point process model was developed to estimate dog population size in four study locations in urban Nepal, with building density and distance from nearest retail food establishment or lodgings as explanatory variables. The proposed model accurately predicted, within a 95 % confidence interval, the surveyed FRDP size and spatial distribution for all four study locations. This model is proposed for further testing and refinement in other locations as a decision-support tool alongside observational dog population size estimates, to inform dog health and public health initiatives including rabies elimination efforts to support the ‘zero by 30′ global mission.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"49 ","pages":"Article 100647"},"PeriodicalIF":3.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877584524000145/pdfft?md5=366670866d5c699f5b8990eb65839b3e&pid=1-s2.0-S1877584524000145-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140154935","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}

引用次数: 0

Analyzing the geographic influence of financial inclusion on illicit drug use in Nigeria 分析尼日利亚金融包容性对非法药物使用的地域影响

IF 3.4

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-06-01 Epub Date: 2024-04-30 DOI: 10.1016/j.sste.2024.100655

Richard Adeleke , Ayodeji Emmanuel Iyanda

{"title":"Analyzing the geographic influence of financial inclusion on illicit drug use in Nigeria","authors":"Richard Adeleke , Ayodeji Emmanuel Iyanda","doi":"10.1016/j.sste.2024.100655","DOIUrl":"https://doi.org/10.1016/j.sste.2024.100655","url":null,"abstract":"<div><p>Nigeria grapples with a formidable public health concern, as approximately 14 million individuals partake in illicit drug use (IDU). This predicament significantly impacts psychiatric disorders, suicides, disability, and mortality rates. Despite previous investigations into predictors and remedies, the role of financial inclusion (FI) remains inadequately explored. Leveraging existing literature on FI and population health, this study asserts that bolstering FI could be instrumental in mitigating IDU prevalence in Nigeria. We employ spatial analysis to scrutinize the influence of FI and other social factors on IDU, revealing a 14.4 % national prevalence with spatial variations ranging from 7 % in Jigawa state to 33 % in Lagos state. Significant IDU hotspots were identified in the southwest states, while cold spots were observed in the Federal Capital Territory and Nassarawa. Multivariate spatial analysis indicates that FI, income, unemployment, and the proportion of the young population are pivotal predictors of IDU nationwide, explaining approximately 67 % of the spatial variance. Given these findings, the study advocates heightened levels of FI and underscores the need for intensified government initiatives to prevent and address illicit drug use.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"49 ","pages":"Article 100655"},"PeriodicalIF":3.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140825893","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

Geographic accessibility to physiotherapy care in Aotearoa New Zealand 新西兰奥特亚罗瓦地区物理治疗护理的地理可达性

IF 3.4

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-06-01 Epub Date: 2024-05-05 DOI: 10.1016/j.sste.2024.100656

Miranda Buhler , Tayyab Shah , Meredith Perry , Marc Tennant , Estie Kruger , Stephan Milosavljevic

{"title":"Geographic accessibility to physiotherapy care in Aotearoa New Zealand","authors":"Miranda Buhler , Tayyab Shah , Meredith Perry , Marc Tennant , Estie Kruger , Stephan Milosavljevic","doi":"10.1016/j.sste.2024.100656","DOIUrl":"https://doi.org/10.1016/j.sste.2024.100656","url":null,"abstract":"<div><p>Disparities in care access for health conditions where physiotherapy can play a major role are abetting health inequities. Spatial analyses can contribute to illuminating inequities in health yet the geographic accessibility to physiotherapy care across New Zealand has not been examined. This population-based study evaluated the accessibility of the New Zealand physiotherapy workforce relative to the population at a local scale. The locations of 5,582 physiotherapists were geocoded and integrated with 2018 Census data to generate 'accessibility scores' for each Statistical Area 2 using the newer 3-step floating catchment area method. For examining the spatial distribution and mapping, accessibility scores were categorized into seven levels, centered around 0.5 SD above and below the mean. New Zealand has an above-average physiotherapy-to-population ratio compared with other OECD countries; however, this workforce is maldistributed. This study identified areas (and locations) where geographic accessibility to physiotherapy care is relatively low.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"49 ","pages":"Article 100656"},"PeriodicalIF":3.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877584524000236/pdfft?md5=a6c3502d7a0a373be233e8f2bfe88175&pid=1-s2.0-S1877584524000236-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140947418","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}

引用次数: 0

Predicting the odds of chronic wasting disease with Habitat Risk software 利用 Habitat Risk 软件预测慢性消耗性疾病的几率

IF 3.4

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-06-01 Epub Date: 2024-04-11 DOI: 10.1016/j.sste.2024.100650

W. David Walter , Brenda Hanley , Cara E. Them , Corey I. Mitchell , James Kelly , Daniel Grove , Nicholas Hollingshead , Rachel C. Abbott , Krysten L. Schuler

{"title":"Predicting the odds of chronic wasting disease with Habitat Risk software","authors":"W. David Walter , Brenda Hanley , Cara E. Them , Corey I. Mitchell , James Kelly , Daniel Grove , Nicholas Hollingshead , Rachel C. Abbott , Krysten L. Schuler","doi":"10.1016/j.sste.2024.100650","DOIUrl":"https://doi.org/10.1016/j.sste.2024.100650","url":null,"abstract":"<div><p>Chronic wasting disease (CWD) is a transmissible spongiform encephalopathy that was first detected in captive cervids in Colorado, United States (US) in 1967, but has since spread into free-ranging white-tailed deer (<em>Odocoileus virginianus</em>) across the US and Canada as well as to Scandinavia and South Korea. In some areas, the disease is considered endemic in wild deer populations, and governmental wildlife agencies have employed epidemiological models to understand long-term environmental risk. However, continued rapid spread of CWD into new regions of the continent has underscored the need for extension of these models into broader tools applicable for wide use by wildlife agencies. Additionally, efforts to semi-automate models will facilitate access of technical scientific methods to broader users. We introduce software (<em>Habitat Risk</em>) designed to link a previously published epidemiological model with spatially referenced environmental and disease testing data to enable agency personnel to make up-to-date, localized, data-driven predictions regarding the odds of CWD detection in surrounding areas after an outbreak is discovered. <em>Habitat Risk</em> requires pre-processing publicly available environmental datasets and standardization of disease testing (surveillance) data, after which an autonomous computational workflow terminates in a user interface that displays an interactive map of disease risk. We demonstrated the use of the <em>Habitat Risk</em> software with surveillance data of white-tailed deer from Tennessee, USA.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"49 ","pages":"Article 100650"},"PeriodicalIF":3.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877584524000170/pdfft?md5=96dd911de16a4c27fc0d85c91dd0d6fe&pid=1-s2.0-S1877584524000170-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140555243","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}

引用次数: 0

Residuals in space: Potential pitfalls and applications from single-institution survival analysis 空间残差：单一机构生存分析的潜在陷阱和应用

IF 3.4

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-06-01 Epub Date: 2024-03-02 DOI: 10.1016/j.sste.2024.100646

Sophia D. Arabadjis, Stuart H. Sweeney

{"title":"Residuals in space: Potential pitfalls and applications from single-institution survival analysis","authors":"Sophia D. Arabadjis, Stuart H. Sweeney","doi":"10.1016/j.sste.2024.100646","DOIUrl":"10.1016/j.sste.2024.100646","url":null,"abstract":"<div><p>In practice, survival analyses appear in pharmaceutical testing, procedural recovery environments, and registry-based epidemiological studies, each reasonably assuming a known patient population. Less commonly discussed is the additional complexity introduced by non-registry and spatially-referenced data with time-dependent covariates in observational settings. In this short report we discuss residual diagnostics and interpretation from an extended Cox proportional hazard model intended to assess the effects of wildfire evacuation on risk of a secondary cardiovascular events for patients of a specific healthcare system on the California’s central coast. We describe how traditional residuals obscure important spatial patterns indicative of true geographical variation, and their impacts on model parameter estimates. We briefly discuss alternative approaches to dealing with spatial correlation in the context of Bayesian hierarchical models. Our findings/experience suggest that careful attention is needed in observational healthcare data and survival analysis contexts, but also highlights potential applications for detecting observed hospital service areas.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"49 ","pages":"Article 100646"},"PeriodicalIF":3.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877584524000133/pdfft?md5=187718cf09593783fdc63ce47348fb41&pid=1-s2.0-S1877584524000133-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140070562","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}

引用次数: 0

Improving the spatial and temporal resolution of burden of disease measures with Bayesian models 利用贝叶斯模型提高疾病负担测量的时空分辨率

IF 3.4

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-06-01 Epub Date: 2024-05-31 DOI: 10.1016/j.sste.2024.100663

James Hogg , Kerry Staples , Alisha Davis , Susanna Cramb , Candice Patterson , Laura Kirkland , Michelle Gourley , Jianguo Xiao , Wendy Sun

{"title":"Improving the spatial and temporal resolution of burden of disease measures with Bayesian models","authors":"James Hogg , Kerry Staples , Alisha Davis , Susanna Cramb , Candice Patterson , Laura Kirkland , Michelle Gourley , Jianguo Xiao , Wendy Sun","doi":"10.1016/j.sste.2024.100663","DOIUrl":"https://doi.org/10.1016/j.sste.2024.100663","url":null,"abstract":"<div><p>This paper contributes to the field by addressing the critical issue of enhancing the spatial and temporal resolution of health data. Although Bayesian methods are frequently employed to address this challenge in various disciplines, the application of Bayesian spatio-temporal models to burden of disease (BOD) studies remains limited. Our novelty lies in the exploration of two existing Bayesian models that we show to be applicable to a wide range of BOD data, including mortality and prevalence, thereby providing evidence to support the adoption of Bayesian modeling in full BOD studies in the future. We illustrate the benefits of Bayesian modeling with an Australian case study involving asthma and coronary heart disease. Our results showcase the effectiveness of Bayesian approaches in increasing the number of small areas for which results are available and improving the reliability and stability of the results compared to using data directly from surveys or administrative sources.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"49 ","pages":"Article 100663"},"PeriodicalIF":3.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877584524000303/pdfft?md5=b3ba22d405646c7a9d07dd0ece1b27c6&pid=1-s2.0-S1877584524000303-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141250461","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}

引用次数: 0

JAGS model specification for spatiotemporal epidemiological modelling 用于时空流行病学建模的 JAGS 模型规范

IF 3.4

Spatial and Spatio-Temporal Epidemiology Pub Date : 2024-06-01 Epub Date: 2024-02-28 DOI: 10.1016/j.sste.2024.100645

Dinah Jane Lope, Haydar Demirhan

{"title":"JAGS model specification for spatiotemporal epidemiological modelling","authors":"Dinah Jane Lope, Haydar Demirhan","doi":"10.1016/j.sste.2024.100645","DOIUrl":"https://doi.org/10.1016/j.sste.2024.100645","url":null,"abstract":"<div><p>Bayesian inference in modelling infectious diseases using Bayesian inference using Gibbs Sampling (<span>BUGS</span>) is notable in the last two decades in parallel with the advancements in computing and model development. The ability of <span>BUGS</span> to easily implement the Markov chain Monte Carlo (MCMC) method brought Bayesian analysis to the mainstream of infectious disease modelling. However, with the existing software that runs MCMC to make Bayesian inferences, it is challenging, especially in terms of computational complexity, when infectious disease models become more complex with spatial and temporal components, in addition to the increasing number of parameters and large datasets. This study investigates two alternative subscripting strategies for creating models in Just Another Gibbs Sampler (<span>JAGS</span>) environment and their performance in terms of run times. Our results are useful for practitioners to ensure the efficiency and timely implementation of Bayesian spatiotemporal infectious disease modelling.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"49 ","pages":"Article 100645"},"PeriodicalIF":3.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877584524000121/pdfft?md5=377cf61a1a26199f88493cbb44914a46&pid=1-s2.0-S1877584524000121-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140016358","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}

引用次数: 0