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

Abstract

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.

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Published
2021-05-14
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Original Articles
Keywords:
COVID-19, geographically weighted regression, generalized linear model, geographical information systems, Oman, socioeconomic determinants.
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How to Cite
Al Kindi, K. M., Al-Mawali, A., Akharusi, A., Alshukaili, D., Alnasiri, N., Al-Awadhi, T., Charabi, Y., & El Kenawy, A. M. (2021). Demographic and socioeconomic determinants of COVID-19 across Oman - A geospatial modelling approach. Geospatial Health, 16(1). https://doi.org/10.4081/gh.2021.985