TY - JOUR
T1 - Sociodemographic determinants of COVID-19 incidence rates in Oman
T2 - Geospatial modelling using multiscale geographically weighted regression (MGWR)
AU - Mansour, Shawky
AU - Al Kindi, Abdullah
AU - Al-Said, Alkhattab
AU - Al-Said, Adham
AU - Atkinson, Peter
N1 - Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2021/2
Y1 - 2021/2
N2 - The current COVID-19 pandemic is evolving rapidly into one of the most devastating public health crises in recent history. By mid-July 2020, reported cases exceeded 13 million worldwide, with at least 575,000 deaths and 7.33 million people recovered. In Oman, over 61,200 confirmed cases have been reported with an infection rate of 1.3. Spatial modeling of disease transmission is important to guide the response to the epidemic at the subnational level. Sociodemographic and healthcare factors such as age structure, population density, long-term illness, hospital beds and nurse practitioners can be used to explain and predict the spatial transmission of COVID-19. Therefore, this research aimed to examine whether the relationships between the incidence rates and these covariates vary spatially across Oman. Global Ordinary Least Squares (OLS), spatial lag and spatial error regression models (SLM, SEM), as well as two distinct local regression models (Geographically Weighted Regression (GWR) and multiscale geographically weighted regression MGWR), were applied to explore the spatially non-stationary relationships. As the relationships between these covariates and COVID-19 incidence rates vary geographically, the local models were able to express the non-stationary relationships among variables. Furthermore, among the eleven selected regressors, elderly population aged 65 and above, population density, hospital beds, and diabetes rates were found to be statistically significant determinants of COVID-19 incidence rates. In conclusion, spatial information derived from this modeling provides valuable insights regarding the spatially varying relationship of COVID-19 infection with these possible drivers to help establish preventative measures to reduce the community incidence rate.
AB - The current COVID-19 pandemic is evolving rapidly into one of the most devastating public health crises in recent history. By mid-July 2020, reported cases exceeded 13 million worldwide, with at least 575,000 deaths and 7.33 million people recovered. In Oman, over 61,200 confirmed cases have been reported with an infection rate of 1.3. Spatial modeling of disease transmission is important to guide the response to the epidemic at the subnational level. Sociodemographic and healthcare factors such as age structure, population density, long-term illness, hospital beds and nurse practitioners can be used to explain and predict the spatial transmission of COVID-19. Therefore, this research aimed to examine whether the relationships between the incidence rates and these covariates vary spatially across Oman. Global Ordinary Least Squares (OLS), spatial lag and spatial error regression models (SLM, SEM), as well as two distinct local regression models (Geographically Weighted Regression (GWR) and multiscale geographically weighted regression MGWR), were applied to explore the spatially non-stationary relationships. As the relationships between these covariates and COVID-19 incidence rates vary geographically, the local models were able to express the non-stationary relationships among variables. Furthermore, among the eleven selected regressors, elderly population aged 65 and above, population density, hospital beds, and diabetes rates were found to be statistically significant determinants of COVID-19 incidence rates. In conclusion, spatial information derived from this modeling provides valuable insights regarding the spatially varying relationship of COVID-19 infection with these possible drivers to help establish preventative measures to reduce the community incidence rate.
KW - COVID-19
KW - GIS
KW - GWR
KW - Geospatial modeling
KW - MGWR
KW - Oman
KW - Sociodemographic determinants
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U2 - 10.1016/j.scs.2020.102627
DO - 10.1016/j.scs.2020.102627
M3 - Article
C2 - 33288993
AN - SCOPUS:85097720062
SN - 2210-6707
VL - 65
JO - Sustainable Cities and Society
JF - Sustainable Cities and Society
M1 - 102627
ER -