Developing a Place–Time-Specific Transmissibility Index to Measure and Examine the Spatiotemporally Varying Transmissibility of COVID-19.
The transmission rate of COVID-19 varies by location and time. A proper measure of the transmissibility of an infectious disease should be place- and time-specific, which is currently unavailable. This research aims to better understand the spatiotemporally changing transmissibility of COVID-19. It...
| Publicado en: | Annals of the American Association of Geographers Vol. 113; no. 6; pp. 1419 - 1444 |
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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=165473612&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 165473612 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 24694452 JRMH jtl: Annals of the American Association of Geographers issn: 24694452 maglogo: N pubinfo: dt: 2023 vid: 113 iid: 6 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 165473612 10.1080/24694452.2023.2182758 ppf: 1419 ppct: 25 formats: tig: atl: Developing a Place–Time-Specific Transmissibility Index to Measure and Examine the Spatiotemporally Varying Transmissibility of COVID-19. aug: au: Yang, Hao Yao, X. Angela Liu, Ruowei Whalen, Christopher C. affil: Department of Geography, University of Georgia, USA Department of Epidemiology and Biostatistics, University of Georgia, USA su: Infectious disease transmission Coronavirus diseases Spatiotemporal processes Spacetime Machine learning sug: subj: Infectious disease transmission Coronavirus diseases Spatiotemporal processes Spacetime Machine learning keyword: COVID-19 human mobility machine learning place–time-specific effective reproduction number Rst spatial heterogeneity spatiotemporal changes aprendizaje automático cambios espaciotemporales heterogeneidad espacial movilidad humana número de reproducción efectiva específico del lugar y tiempo Rst 时空变化。 时空有效复制量Rst 机器学习 流动性 空间异质性 COVID-19 human mobility machine learning place–time-specific effective reproduction number Rst spatial heterogeneity spatiotemporal changes aprendizaje automático cambios espaciotemporales heterogeneidad espacial movilidad humana número de reproducción efectiva específico del lugar y tiempo Rst 时空变化。 时空有效复制量Rst 机器学习 流动性 空间异质性 ab: The transmission rate of COVID-19 varies by location and time. A proper measure of the transmissibility of an infectious disease should be place- and time-specific, which is currently unavailable. This research aims to better understand the spatiotemporally changing transmissibility of COVID-19. It contributes to COVID-19 research in three ways. First, it presents a generally applicable modeling framework to estimate the transmissibility of COVID-19 in a specific place and time based on daily reported case data, called space-time effective reproduction number, denoted as R s t. Then, the developed model is used to create a spatiotemporal data set of R s t values at the county level in the United States. Second, it investigates relationships between R s t and dynamically changing context factors with multiple machine learning and spatial modeling techniques. The research examines the relationships from a cross-sectional perspective and a longitudinal perspective separately. The longitudinal view allows us to understand how local human dynamics and policy factors influence changes in R s t over time in the place, whereas the cross-sectional view sheds light on the demographic, socioeconomic, and environmental factors behind spatial variations of R s t at a specific time slice. Some general trends of the relationships are found, but the level of impact by each context factor varies geographically. Third, the best performing local longitudinal models have promising potential to simulate or forecast future transmissibility. The random forest and the exponential regression models based on time-series data gave the best performances. These models were further evaluated against ground truth data of county-level reported cases. Their good prediction accuracies in the case study prove that these machine learning models are promising in their ability to predict transmissibility in hypothetical or foreseeable scenarios. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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