Spatiotemporal Modeling of the Association between Neighborhood Factors and COVID-19 Incidence Rates in Scotland.
This study aims to investigate the association between neighborhood-level factors and COVID-19 incidence in Scotland from a spatiotemporal perspective. The outcome variable is the COVID-19 incidence in Scotland. Based on the identification of the wave peaks for COVID-19 cases between 2020 and 2021,...
| Publicado en: | Professional Geographer Vol. 75; no. 5; pp. 803 - 816 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
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Taylor & Francis Ltd
2023
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=172403828&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 172403828 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00330124 PGG jtl: Professional Geographer issn: 00330124 maglogo: Y pubinfo: dt: 2023 vid: 75 iid: 5 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 172403828 10.1080/00330124.2023.2194363 ppf: 803 ppct: 13 formats: tig: atl: Spatiotemporal Modeling of the Association between Neighborhood Factors and COVID-19 Incidence Rates in Scotland. aug: au: Wang, Ruoyu Clemens, Tom Douglas, Margaret Keller, Markéta van der Horst, Dan affil: Queen's University Belfast, UK University of Edinburgh, UK University of Glasgow, UK, and Public Health Scotland, UK Public Health Scotland su: Scotland COVID-19 pandemic Neighborhoods COVID-19 Random forest algorithms Particulate matter sug: subj: COVID-19 pandemic Neighborhoods Scotland COVID-19 Random forest algorithms Particulate matter keyword: geographical random forest model neighborhood factors spatial-temporal pattern Escocia factores de vecindad modelo geográfico de bosque aleatorio patrón espaciotemporal 地理随机森林模型 新冠肺炎 时空模式。 社区因素 苏格兰 geographical random forest model neighborhood factors spatial-temporal pattern Escocia factores de vecindad modelo geográfico de bosque aleatorio patrón espaciotemporal 地理随机森林模型 新冠肺炎 时空模式。 社区因素 苏格兰 ab: This study aims to investigate the association between neighborhood-level factors and COVID-19 incidence in Scotland from a spatiotemporal perspective. The outcome variable is the COVID-19 incidence in Scotland. Based on the identification of the wave peaks for COVID-19 cases between 2020 and 2021, confirmed COVID-19 cases in Scotland can be divided into four phases. To model the COVID-19 incidence, sixteen neighborhood factors are chosen as the predictors. Geographical random forest models are used to examine spatiotemporal variation in major determinants of COVID-19 incidence. The spatial analysis indicates that proportion of religious people is the most strongly associated with COVID-19 incidence in southern Scotland, whereas particulate matter is the most strongly associated with COVID-19 incidence in northern Scotland. Also, crowded households, prepandemic emergency admission rates, and health and social workers are the most strongly associated with COVID-19 incidence in eastern and central Scotland, respectively. A possible explanation is that the association between predictors and COVID-19 incidence might be influenced by local context (e.g., people's lifestyles), which is spatially variant across Scotland. The temporal analysis indicates that dominant factors associated with COVID-19 incidence also vary across different phases, suggesting that pandemic-related policy should take spatiotemporal variations into account. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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