Spatiotemporal modelling of the association between neighborhood factors and COVID-19 incidence rates in Scotland

Wang, R., Clemens, T., Douglas, M., Keller, M. and van der Horst, D. (2023) Spatiotemporal modelling of the association between neighborhood factors and COVID-19 incidence rates in Scotland. Professional Geographer, (doi: 10.1080/00330124.2023.2194363) (Early Online Publication)

Abstract

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.

• Item Type: Articles
• Status: Early Online Publication
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Douglas, Dr Margaret
• Authors: Wang, R., Clemens, T., Douglas, M., Keller, M., and van der Horst, D.
• College/School: College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > Public Health
• Journal Name: Professional Geographer
• Publisher: Taylor & Francis
• ISSN: 0033-0124
• ISSN (Online): 1467-9272
• Published Online: 30 May 2023
• Copyright Holders: Copyright © 2023 by American Association of Geographers
• First Published: First published in Professional Geographer 2023
• Publisher Policy: Reproduced in accordance with the publisher copyright policy