Spatiotemporal Modeling of the Association between Neighborhood Factors and COVID-19 Incidence Rates in Scotland

Ruoyu Wang, Tom Clemens, Margaret Douglas, Markéta Keller, Dan van der Horst

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)803-815
Number of pages13
JournalProfessional Geographer
Volume75
Issue number5
DOIs
Publication statusPublished - 30 May 2023

Keywords / Materials (for Non-textual outputs)

  • COVID-19
  • geographical random forest model
  • neighborhood factors
  • Scotland
  • spatial-temporal pattern

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