TY - JOUR
AU - Wang, Ruoyu
AU - Clemens, Tom
AU - Douglas, Margaret
AU - Keller, Markéta
AU - van der Horst, D.
PY - 2023
DA - May
TI - Spatiotemporal modelling of the association between neighborhood factors and COVID-19 incidence rates in Scotland
JO - The Professional Geographer
DO - https://doi.org/10.1080/00330124.2023.2194363
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.
PB - Taylor and Francis
UR - https://eprints.gla.ac.uk/287649/
KW - Coronavirus (COVID-19)
KW - Environment, community and place
ER