https://doi.org/10.4081/gh.2021.985
Demographic and socioeconomic determinants of COVID-19 across Oman - A geospatial modelling approach
Submitted: 6 February 2021
Accepted: 14 April 2021
Published: 14 May 2021
Accepted: 14 April 2021
Abstract Views: 3647
PDF: 1450
Appendices: 72
HTML: 119
Appendices: 72
HTML: 119
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
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.
Abdi H, Williams LJ, 2010. Principal component analysis. Wiley Interdiscip Rev Comput Stat 2:433-59.
DOI: https://doi.org/10.1002/wics.101
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: https://doi.org/10.1016/S2214-109X(20)30300-4
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: https://doi.org/10.12688/f1000research.27544.1
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: https://doi.org/10.1007/s41748-020-00194-2
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: https://doi.org/10.5001/omj.2020.111
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: https://doi.org/10.1016/j.chaos.2020.110017
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: https://doi.org/10.1007/s11004-015-9583-0
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: https://doi.org/10.1177/0002039720943612
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: https://doi.org/10.1016/j.chaos.2020.110154
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: https://doi.org/10.1007/s11869-020-00894-8
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: https://doi.org/10.1212/WNL.0000000000010813
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: https://doi.org/10.1073/pnas.2007658117
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: https://doi.org/10.1016/j.puhe.2020.06.056
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: https://doi.org/10.1136/bmjopen-2020-043560
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: https://doi.org/10.1159/000507423
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: https://doi.org/10.1016/j.scitotenv.2020.140033
Garcia AAdSG, 2020. Clustering of longitudinal data: application to COVID-19 data. Dissertation 20120-10.2. University of Porto, Portugal. Available from: https://repositorio-aberto.up.pt/handle/10216/129753
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: https://doi.org/10.1007/s00134-020-06033-2
Guo D, 2010. Local entropy map: a nonparametric approach to detecting spatially varying multivariate relationships. International J Geogr Inf Sci 24:1367-89.
DOI: https://doi.org/10.1080/13658811003619143
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: https://doi.org/10.1007/s40808-020-00838-2
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: https://doi.org/10.1016/j.jiph.2020.07.006
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: https://doi.org/10.1186/s42269-020-00393-x
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: https://doi.org/10.1016/j.amepre.2020.06.006
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: https://doi.org/10.1136/jech-2020-214297
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: https://doi.org/10.1016/j.scitotenv.2020.144455
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: https://doi.org/10.1080/13658816.2018.1521523
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: https://doi.org/10.1016/j.ssci.2013.04.005
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: https://doi.org/10.3389/fpubh.2020.546637
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: https://doi.org/10.1016/j.scitotenv.2020.138513
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: https://doi.org/10.3390/rs12101576
Ludvigsson JF, 2020. Systematic review of COVIDâ€19 in children shows milder cases and a better prognosis than adults. Acta Paediatr 109:1088-95.
DOI: https://doi.org/10.1111/apa.15270
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: https://doi.org/10.1016/j.scs.2020.102627
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: https://doi.org/10.2196/19796
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: https://doi.org/10.1016/j.scitotenv.2020.138884
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: https://doi.org/10.1007/s11356-020-10930-w
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: https://doi.org/10.1111/j.1472-4642.2008.00492.x
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: https://doi.org/10.37040/geografie2020125020171
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: https://doi.org/10.1016/j.compag.2014.12.011
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: https://doi.org/10.1016/j.techfore.2013.12.026
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: https://doi.org/10.1061/(ASCE)TE.1943-5436.0000680
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: https://doi.org/10.1016/j.ijid.2020.06.058
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: https://doi.org/10.1080/09603123.2020.1793921
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: https://doi.org/10.1007/s40808-020-00890-y
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: https://doi.org/10.1016/j.scs.2020.102418
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: https://doi.org/10.1007/978-3-319-44093-4_18
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: https://doi.org/10.1016/j.scitotenv.2020.141022
Singhal A, Seborg DE, 2005. Clustering multivariate timeâ€series data. J Chemom 19:427-38.
DOI: https://doi.org/10.1002/cem.945
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: https://doi.org/10.1007/s10661-019-7250-5
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: https://doi.org/10.1016/j.resconrec.2020.104880
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: https://doi.org/10.1007/s11205-016-1390-6
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: https://doi.org/10.1016/j.annepidem.2020.07.014
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: https://doi.org/10.1016/j.scitotenv.2020.141347
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: https://doi.org/10.1109/I-SMAC49090.2020.9243444
Weissleder R, Lee H, Ko J, Pittet MJ, 2020. COVID-19 diagnostics in context. Sci Transl Med 12:eabc1931.
DOI: https://doi.org/10.1126/scitranslmed.abc1931
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: https://doi.org/10.1007/978-3-642-03647-7_22
Wheeler D, Tiefelsdorf M, 2005. Multicollinearity and correlation among local regression coefficients in geographically weighted regression. J Geogr Syst 7:161-87.
DOI: https://doi.org/10.1007/s10109-005-0155-6
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: https://doi.org/10.1093/icesjms/fsp224
Wiwanitkit V, 2020. COVID-19 infection in Oman. Oman Med J 35:e186.
DOI: https://doi.org/10.5001/omj.2020.127
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: https://doi.org/10.1007/BF03354885
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: https://doi.org/10.1016/j.jtrangeo.2019.01.004
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
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Addisu Jember Zeleke, Rossella Miglio, Pierpaolo Palumbo, Paolo Tubertini, Lorenzo Chiari, Bologna MODELS4COVID Study Group of the University of Bologna and the National Institute for Nuclear Physics (INFN), Spatiotemporal heterogeneity of SARS-CoV-2 diffusion at the city level using geographically weighted Poisson regression model: The case of Bologna, Italy , Geospatial Health: Vol. 17 No. 2 (2022)
You may also start an advanced similarity search for this article.
