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


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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Abdi H, Williams LJ, 2010. Principal component analysis. Wiley Interdiscip Rev Comput Stat 2:433-59. DOI:

Acharya R, Porwal A, 2020. A vulnerability index for the management of and response to the COVID-19 epidemic in India: an ecological study. Lancet Glob Health 8:e1142-51. DOI:

Ahasan R, Alam MS, Chakraborty T, Hossain MM, 2020. Applications of GIS and geospatial analyses in COVID-19 research: a systematic review. F1000Res 9:1379. DOI:

Al-Kindi KM, Alkharusi A, Alshukaili D, Al Nasiri N, Al-Awadhi T, Charabi Y, El Kenawy AM, 2020. Spatiotemporal Assessment of COVID-19 Spread over Oman Using GIS Techniques. Earth Syst Environ 4:1083-98. DOI:

Al Fannah J, Al Harthy H, Al Salmi Q, 2020. COVID-19 pandemic: learning lessons and a vision for a better health system. Oman Med J 35:e169. DOI:

Arora P, Kumar H, Panigrahi BK, 2020. Prediction and analysis of COVID-19 positive cases using deep learning models: a descriptive case study of India. Chaos Solitons & Fractals 139:110017. DOI:

Asante J, Kreamer D, 2015. A new approach to identify recharge areas in the Lower Virgin River Basin and surrounding basins by multivariate statistics. Math Geosci 47:819-42. DOI:

Asante LA, Mills RO, 2020. Exploring the socio-economic impact of COVID-19 pandemic in marketplaces in urban Ghana. Africa Spectr 55:170-81. DOI:

Bagal DK, Rath A, Barua A, Patnaik D, 2020. Estimating the parameters of susceptible-infected-recovered model of COVID-19 cases in India during lockdown periods. Chaos Solitons & Fractals 140:110154. DOI:

Bager A, Roman M, Algedih M, Mohammed B, 2017. Addressing multicollinearity in regression models: a ridge regression application. J Soc Econom Stat 6:30-45.

Bashir MF, Benjiang M, Shahzad L, 2020. A brief review of socio-economic and environmental impact of Covid-19. Air Qual Atmos Health 13:1403-9. DOI:

Belvisi D, Pellicciari R, Fabbrini A, Costanzo M, Pietracupa S, De Lucia M, Modugno N, Magrinelli F, Dallocchio C, Ercoli TJN, 2020. Risk factors of Parkinson disease: Simultaneous assessment, interactions, and etiologic subtypes. Neurology 95:e2500-8. DOI:

Bonaccorsi G, Pierri F, Cinelli M, Flori A, Galeazzi A, Porcelli F, Schmidt AL, Valensise CM, Scala A, Quattrociocchi W, 2020. Economic and social consequences of human mobility restrictions under COVID-19. Proc Natl Acad Sci U S A 117:15530-5. DOI:

Bray I, Gibson A, White J, 2020. Coronavirus disease 2019 mortality: a multivariate ecological analysis in relation to ethnicity, population density, obesity, deprivation and pollution. Publ Health 185:261-3. DOI:

Cao Y, Hiyoshi A, Montgomery S, 2020. COVID-19 case-fatality rate and demographic and socioeconomic influencers: worldwide spatial regression analysis based on country-level data. BMJ Open 10:e043560. DOI:

Ciotti M, Angeletti S, Minieri M, Giovannetti M, Benvenuto D, Pascarella S, Sagnelli C, Bianchi M, Bernardini S, Ciccozzi MJC, 2019. COVID-19 outbreak: an overview. Chemotherapy 64:215-23. DOI:

Franch-Pardo I, Napoletano BM, Rosete-Verges F, Billa L, 2020. Spatial analysis and GIS in the study of COVID-19. A review. Sci Total Environ 739:140033. DOI:

Garcia AAdSG, 2020. Clustering of longitudinal data: application to COVID-19 data. Dissertation 20120-10.2. University of Porto, Portugal. Available from:

Gattinoni L, Chiumello D, Caironi P, Busana M, Romitti F, Brazzi L, Camporota L. 2020. COVID-19 pneumonia: different respiratory treatments for different phenotypes? Intensive Care Med 46:1099-102. DOI:

Guo D, 2010. Local entropy map: a nonparametric approach to detecting spatially varying multivariate relationships. International J Geogr Inf Sci 24:1367-89. DOI:

Gupta A, Banerjee S, Das S, 2020. Significance of geographical factors to the COVID-19 outbreak in India. Model Earth Syst Environ 6:2645-53. DOI:

Iyanda AE, Adeleke R, Lu Y, Osayomi T, Adaralegbe A, Lasode M, Chima-Adaralegbe NJ, Osundina AM. 2020. A retrospective cross-national examination of COVID-19 outbreak in 175 countries: a multiscale geographically weighted regression analysis (January 11-June 28, 2020). J Infect Public Health 13:1438-45. DOI:

Kadi N, Khelfaoui M, 2020. Population density, a factor in the spread of COVID-19 in Algeria: statistic study. Bull Natl Res Cent 44:1-7. DOI:

Karaye IM, Horney JA. 2020. The impact of social vulnerability on COVID-19 in the US: an analysis of spatially varying relationships. Am J Prev Med 59:317-25. DOI:

Khalatbari-Soltani S, Cumming RG, Delpierre C, Kelly-Irving M. 2020. Importance of collecting data on socioeconomic determinants from the early stage of the COVID-19 outbreak onwards. J Epidemiol Community Health 74:620-3. DOI:

Kwok CYT, Wong MS, Chan KL, Kwan M-P, Nichol JE, Liu CH, Wong JYH, Wai AKC, Chan LWC, Xu Y, 2020. Spatial analysis of the impact of urban geometry and socio-demographic characteristics on COVID-19, a study in Hong Kong. Sci Total Environ 144455 [Epub ahead of print]. DOI:

Li Z, Fotheringham AS, Li W, Oshan T, 2019. Fast Geographically Weighted Regression (FastGWR): a scalable algorithm to investigate spatial process heterogeneity in millions of observations. Int J Geogr Inf Sci 33:155-75. DOI:

Li Z, Wang W, Liu P, Bigham JM, Ragland DR, 2013. Using geographically weighted Poisson regression for county-level crash modeling in California. Saf Sci 58:89-97. DOI:

Lin Y, Zhong P, Chen T, 2020. Association between socioeconomic factors and the COVID-19 outbreak in the 39 well-developed cities of China. Front Public Health 8:546637. DOI:

Liu J, Zhou J, Yao J, Zhang X, Li L, Xu X, He X, Wang B, Fu S, Niu T, 2020a. Impact of meteorological factors on the COVID-19 transmission: A multi-city study in China. Sci Total Environ 138513 [Epub ahead of print]. DOI:

Liu Q, Sha D, Liu W, Houser P, Zhang L, Hou R, Lan H, Flynn C, Lu M, and Hu T. 2020b. Spatiotemporal patterns of COVID-19 impact on human activities and environment in mainland China using nighttime light and air quality data. Remote Sens 12:1576. DOI:

Ludvigsson JF, 2020. Systematic review of COVID‐19 in children shows milder cases and a better prognosis than adults. Acta Paediatr 109:1088-95. DOI:

Mansour S, Al Kindi A, Al-Said A, Al-Said A, Atkinson P, 2020. Sociodemographic determinants of COVID-19 incidence rates in Oman: geospatial modelling using multiscale geographically weighted regression (MGWR). Sustain Cities Soc 102627 [Epub ahead of print]. DOI:

Ming LC, Untong N, Aliudin NA, Osili N, Kifli N, Tan CS, Goh KW, Ng PW, Al-Worafi YM, Lee KS, 2020. Mobile health apps on COVID-19 launched in the early days of the pandemic: content analysis and review. JMIR mHealth uHealth 8:e19796. DOI:

Mollalo A, Vahedi B, Rivera KM. 2020a. GIS-based spatial modeling of COVID-19 incidence rate in the continental United States. Sci Total Environ 728:138884. DOI:

Nakada LYK, Urban RC, 2020. COVID-19 pandemic: environmental and social factors influencing the spread of SARS-CoV-2 in S o, Brazil. Environ Sci Pollut Res Int 28:1-7. DOI:

Nipperess DA, Andersen AN, Pik AJ, Bramble R, Wilson P, Beattie AJ, 2008. The influence of spatial scale on the congruence of classifications circumscribing morphological units of biodiversity. Divers Distrib 14:917-24. DOI:

Pászto V, Burian J, Macků K, 2020. COVID-19 data sources: evaluation of map applications and analysis of behaviour changes in Europe’s population. Geografie (Utrecht) 125:171-209. DOI:

Peeters A, Zude M, Käthner J, Ünlü M, Kanber R, Hetzroni A, Gebbers R, Ben-Gal A, 2015. Getis-Ord’s hot-and cold-spot statistics as a basis for multivariate spatial clustering of orchard tree data. Comput Electron Agric 111:140-50. DOI:

Pick JB, Nishida T, 2015. Digital divides in the world and its regions: a spatial and multivariate analysis of technological utilization. Technol Forecast Soc Change 91:1-17. DOI:

Pirdavani A, Bellemans T, Brijs T, Wets G, 2014. Application of geographically weighted regression technique in spatial analysis of fatal and injury crashes. J Transport Eng 140:04014032. DOI:

Pourghasemi HR, Pouyan S, Heidari B, Farajzadeh Z, Shamsi SRF, Babaei S, Khosravi R, Etemadi M, Ghanbarian G, Farhadi A, 2020. Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020). Int J Infect Dis 98:90-108. DOI:

Pramanik M, Udmale P, Bisht P, Chowdhury K, Szabo S, Pal I, 2020. Climatic factors influence the spread of COVID-19 in Russia. Int J Environ Health Resh [Epub ahead of print]. DOI:

Roy S, Bhunia GS, Shit PK, 2020. Spatial prediction of COVID-19 epidemic using ARIMA techniques in India. Model Earth Syst Environ [Epub ahead of print]. DOI:

Sannigrahi S, Pilla F, Basu B, Basu AS, Molter A. 2020. Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach. Sustain Cities Soc 62:102418. DOI:

Sarra AL, Nissi E, 2016. Geographically weighted regression analysis of cardiovascular diseases: evidence from Canada Health Data. In: Di Battista T., Moreno E., Racugno W. (Eds.), Topics on methodological and applied statistical inference. Studies in Theoretical and Applied Statistics. Springer, Cham., Berlin, Germany, pp 191-203. DOI:

Shakil MH, Munim ZH, Tasnia M, Sarowar S, 2020. COVID-19 and the environment: A critical review and research agenda. Sci Total Environ [Epub ahead of print]. DOI:

Singhal A, Seborg DE, 2005. Clustering multivariate time‐series data. J Chemom 19:427-38. DOI:

Song L, Cao Y, Zhou W, Kuang X, Luo G, 2019. Study on the variation of arable land use and management countermeasures under rapid urbanization: the application of a gravity model in a regional perspective. Environ Monit Assess 191:120. DOI:

Song W, Wang C, Chen W, Zhang X, Li H, Li J, 2020. Unlocking the spatial heterogeneous relationship between Per Capita GDP and nearby air quality using bivariate local indicator of spatial association. Resour Conserv Recycl 160:104880. DOI:

Su S, Gong Y, Tan B, Pi J, Weng M, Cai Z, 2017. Area social deprivation and public health: Analyzing the spatial non-stationary associations using geographically weighed regression. Soc Indic Res 133:819-32. DOI:

Sun F, Matthews SA, Yang T-C, Hu M-H, 2020a. A spatial analysis of the COVID-19 period prevalence in US counties through June 28, 2020: where geography matters? Ann Epidemiol 52:54-59.e1. DOI:

Sun Z, Zhang H, Yang Y, Wan H, Wang Y, 2020b. Impacts of geographic factors and population density on the COVID-19 spreading under the lockdown policies of China. Sci Total Environ 746:141347. DOI:

Verma A, Prakash S, 2020. Impact of covid-19 on environment and society. J Glob Biosci 9:7352-63.

Vishwakarma S, Nair PS, Rao DS, 2017. A Comparative Study of K-means and K-medoid Clustering for Social Media Text Mining. Int J Adv Sci Res Engineering Trends 2:297-301.

Waheed A, Shafi J, 2020. Successful role of smart technology to combat COVID-19. pp 772-777 in 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC): IEEE. DOI:

Weissleder R, Lee H, Ko J, Pittet MJ, 2020. COVID-19 diagnostics in context. Sci Transl Med 12:eabc1931. DOI:

Wheeler DC, Páez A, 2010. Geographically weighted regression. In: Fischer M., Getis A. (Eds.), Handbook of applied spatial analysis. Springer, Berlin, Heidelberg, Germany, pp 461-486. DOI:

Wheeler D, Tiefelsdorf M, 2005. Multicollinearity and correlation among local regression coefficients in geographically weighted regression. J Geogr Syst 7:161-87. DOI:

Windle MJ, Rose GA, Devillers R, Fortin M-J, 2010. Exploring spatial non-stationarity of fisheries survey data using geographically weighted regression (GWR): an example from the Northwest Atlantic. ICES J Mar Sci 67:145-54. DOI:

Wiwanitkit V, 2020. COVID-19 infection in Oman. Oman Med J 35:e186. DOI:

Yang T-C, Shoff C, Matthews SA, 2013. Examining the spatially non-stationary associations between the second demographic transition and infant mortality: a Poisson GWR approach. Spatial Demogr 1:17-40. DOI:

Yu H, Peng Z-R, 2019. Exploring the spatial variation of ridesourcing demand and its relationship to built environment and socioeconomic factors with the geographically weighted Poisson regression. J Transport Geogr 75:147-63. DOI:

Original Articles
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).