Demographic and socioeconomic determinants of COVID-19 across Oman - A geospatial modelling approach

Geospatial Health

TalalAl-Awadhi¹  AdhraAl-Mawali²  Noura Alnasiri³  Ahmed M.El Kenawy³  Yassine Charabi⁴  Khalifa M. Al Kindi⁴  Amira Akharusi⁵  DuhaiAlshukaili⁶ 

[1] Center for Environmental Studies & Research, Muscat;Director/Centre of Studies & Research, Ministry of Health, Muscat;Geography Department, College of Arts & Social Sciences, Sultan Qaboos University, Muscat, Oman;Geography Department, College of Arts & Social Sciences, Sultan Qaboos University, Muscat;Physiology Department, College of Medicine & Health Sciences, Sultan Qaboos University, Muscat;University of Technology & Applied Sciences, Nizwa;

关键词: COVID-19;    geographically weighted regression;    generalized linear model;    geographical information systems;    Oman;    socioeconomic determinants.;   

DOI  :  10.4081/gh.2021.985

来源: DOAJ

【 摘 要 】

Local, bivariate relationships between coronavirus 2019 (COVID-19) infection rates and a set of demographic and socioeconomic variables were explored at the district level in Oman. To limit multicollinearity a principal component analysis was conducted, the results of which showed that three components together could explain 65% of the total variance that were therefore subjected to further study. Comparison of a generalized linear model (GLM) and geographically weighted regression (GWR) indicated an improvement in model performance using GWR (goodness of fit=93%) compared to GLM (goodness of fit=86%). The local coefficient of determination (R2) showed a significant influence of specific demographic and socioeconomic factors on COVID-19, including percentages of Omani and non-Omani population at various age levels; spatial interaction; population density; number of hospital beds; total number of households; purchasing power; and purchasing power per km2. No direct correlation between COVID- 19 rates and health facilities distribution or tobacco usage. This study suggests that Poisson regression using GWR and GLM can address unobserved spatial non-stationary relationships. Findings of this study can promote current understanding of the demographic and socioeconomic variables impacting the spatial patterns of COVID-19 in Oman, allowing local and national authorities to adopt more appropriate strategies to cope with this pandemic in the future and also to allocate more effective prevention resources.

【 授权许可】

Unknown