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• W4200518223 endingPage "13294" @default.
• W4200518223 startingPage "13294" @default.
• W4200518223 abstract "Risk assessments for COVID-19 are the basis for formulating prevention and control strategies, especially at the micro scale. In a previous risk assessment model, various densities were regarded as the decisive driving factors of COVID-19 in the spatial dimension (population density, facility density, trajectory density, etc.). However, this conclusion ignored the fact that the densities were actually an abstract reflection of the contact frequency, which is a more essential determinant of epidemic transmission and lacked any means of corresponding quantitative correction. In this study, based on the facility density (FD), which has often been used in traditional research, a novel micro-scale COVID-19 risk predictor, facility attractiveness (FA, which has a better ability to reflect contact frequency), was proposed for improving the gravity model in combination with the differences in regional population density and mobility levels of an age-hierarchical population. An empirical analysis based on spatiotemporal modeling was carried out using geographically and temporally weighted regression (GTWR) in the Qingdao metropolitan area during the first wave of the pandemic. The spatiotemporally nonstationary relationships between facility density (attractiveness) and micro-risk of COVID-19 were revealed in the modeling results. The new predictors showed that residential areas and health-care facilities had more reasonable impacts than traditional densities. Compared with the model constructed using FDs (0.5159), the global prediction ability (adjusted R2) of the FA model (0.5694) was increased by 10.4%. The improvement in the local-scale prediction ability was more significant, especially in high-risk areas (rate: 107.2%) and densely populated areas (rate in Shinan District: 64.4%; rate in Shibei District: 57.8%) during the outset period. It was proven that the optimized predictors were more suitable for use in spatiotemporal infection risk modeling in the initial stage of regional epidemics than traditional predictors. These findings can provide methodological references and model-optimized ideas for future micro-scale spatiotemporal infection modeling." @default.
• W4200518223 created "2021-12-31" @default.
• W4200518223 creator A5013518754 @default.
• W4200518223 creator A5027283064 @default.
• W4200518223 creator A5034812806 @default.
• W4200518223 creator A5052737766 @default.
• W4200518223 date "2021-12-16" @default.
• W4200518223 modified "2023-10-17" @default.
• W4200518223 title "A Novel Predictor for Micro-Scale COVID-19 Risk Modeling: An Empirical Study from a Spatiotemporal Perspective" @default.
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• W4200518223 doi "https://doi.org/10.3390/ijerph182413294" @default.
• W4200518223 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34948902" @default.
• W4200518223 hasPublicationYear "2021" @default.
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• W4200518223 hasAuthorship W4200518223A5013518754 @default.
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