Geospatial Analysis of COVID-19 Death Rate and Influencing Factors in the MENA Region
,
Abstract
The COVID-19 pandemic has significantly impacted the Middle East and North Africa (MENA) region, with over twenty-eight million cases and 800,000 deaths reported as of August 2023. Spatial analysis can help identify factors associated with the high death toll and develop targeted interventions to reduce the virus's spread and improve health outcomes. The study uses GIS-based analysis and geostatistical models to analyze the COVID-19 death rate in MENA countries. It identifies demographic, medical, and socioeconomic factors as key factors. The research suggests that hospital bed allocation, unemployment rate, and overall immunizations could be key factors influencing the death rate. The study also highlights the fragility of healthcare infrastructure in developing nations, with poor allocation and insufficient support for vulnerable groups. The findings suggest a positive correlation between death rate, hospital bed allocation, unemployment rate, and vaccination doses, highlighting the importance of social isolation measures. The estimated OLS model, which considers variables like hospital beds, unemployment rate, and total vaccine doses, was found to explain 73.46% of COVID-19 death cases across the Middle East and Africa (MENA). However, the model's spatial autocorrelation was found, requiring the development of spatial lag regression (SLM) and spatial error regression (SEM) models. The geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR) models showed higher and lower AIC than global models, with the GWR model showing a clear pattern of impact in the northwestern area and the MGWR model showing a moderate impact in the northwestern area. Understanding COVID-19 death incidence is crucial for controlling transmission. This work could be valuable in future studies.
1. Ganasegeran, K., et al., Spatial Dynamics and Multiscale Regression Modelling of Population Level Indicators for COVID-19 Spread in Malaysia. International Journal of Environmental Research and Public Health, 2022. 19(4): p. 2082.
2. Bayode, T., et al., Spatial variability of COVID-19 and its risk factors in Nigeria: A spatial regression method. Applied Geography, 2022. 138: p. 102621.
3. Urban, R., C and L. Nakada, Y, K, GIS-based spatial modelling of COVID-19 death incidence in São Paulo, Brazil. Environment and Urbanization, 2021. 33(1): p. 229–238.
4. Kotov, E.A., et al., Spatial modelling of key regional-level factors of Covid-19 mortality in Russia. Geography, Environment, Sustainability, 2022. 15(2): p. 71-83.
5. Mesmar, J. and A. Badran, The Post-COVID Classroom: Lessons from a Pandemic, in Higher Education in the Arab World. 2022, Springer. p. 11-41.
6. Cutler, D.M. and L.H. Summers, The COVID-19 pandemic and the $16 trillion virus. Jama, 2020. 324(15): p. 1495-1496.
7. Denning, M., et al., Determinants of burnout and other aspects of psychological well-being in healthcare workers during the Covid-19 pandemic: A multinational cross-sectional study. Plos one, 2021. 16(4): p. e0238666.
8. Delis, M.D., M. Iosifidi, and M. Tasiou, Efficiency of government policy during the COVID-19 pandemic. Available at SSRN 3821814, 2022.
9. Fay, M., et al., Hitting the Trillion Mark--A Look at How Much Countries Are Spending on Infrastructure. World Bank Policy Research Working Paper, 2019(8730).
10. Aminova, M., S. Mareef, and C. Machado, Entrepreneurship Ecosystem in Arab World: the status quo, impediments and the ways forward. International Journal of Business Ethics and Governance, 2020. 3(3): p. 1-13.
11. Davoodi, M.H.R. and M.G.T. Abed, Challenges of growth and globalization in the Middle East and North Africa. 2003: International Monetary Fund.
12. Ward, M., D and K. Gleditsch, S, Spatial regression models. Vol. 115. 2018, china: Sage Publications.
13. Mollalo, A., et al., Predicting the hotspots of age-adjusted mortality rates of lower respiratory infection across the continental United States: Integration of GIS, spatial statistics and machine learning algorithms. International Journal of Medical Informatics, 2020. 142: p. 104248.
14. Anselin, L. and D. Arribas-Bel, Spatial fixed effects and spatial dependence in a single cross‐section. Papers in Regional Science, 2013. 92(1): p. 3-17.
15. Sannigrahi, S., et al., Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach. Sustainable cities and society, 2020. 62: p. 102418.
16. Mollalo, A., B. Vahedi, and K.M. Rivera, GIS-based spatial modeling of COVID-19 incidence rate in the continental United States. Science of the total environment, 2020. 728: p. 138884.
17. Rahman, M., A, et al., Data-driven dynamic clustering framework for mitigating the adverse economic impact of Covid-19 lockdown practices. Sustainable Cities and Society, 2020. 62: p. 102372.
18. Monica and R. Mishra, An epidemiological study of cervical and breast screening in India: district-level analysis. BMC women's health, 2020. 20: p. 1-15.
19. Oshan, T.M., et al., mgwr: A Python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale. ISPRS International Journal of Geo-Information, 2019. 8(6): p. 269.
20. Dutta, I., T. Basu, and A. Das, Spatial analysis of COVID-19 incidence and its determinants using spatial modeling: A study on India. Environmental Challenges, 2021. 4: p. 100096.
21. Kissling, W.D. and G. Carl, Spatial autocorrelation and the selection of simultaneous autoregressive models. Global Ecology and Biogeography, 2008. 17(1): p. 59-71.
22. Deilami, K., M. Kamruzzaman, and J.F. Hayes, Correlation or causality between land cover patterns and the urban heat island effect? Evidence from Brisbane, Australia. Remote Sensing, 2016. 8(9): p. 716.
23. Comber, A., et al., A route map for successful applications of geographically weighted regression. Geographical Analysis, 2022: p. 2022.
24. Chien, Y.-M.C., S. Carver, and A. Comber, Using geographically weighted models to explore how crowdsourced landscape perceptions relate to landscape physical characteristics. Landscape and Urban Planning, 2020. 203: p. 103904.
25. Deilami, K. and M. Kamruzzaman, Modelling the urban heat island effect of smart growth policy scenarios in Brisbane. Land use policy. The International Journal Covering All Aspects of Land Use, 2017. 64: p. 38-55.
26. Hamad, F., et al., Viability of Transplanted Organs Based on Donor’s Age. Sch J Phys Math Stat, 2023. 4: p. 97-104.
27. Mansour, S., et al., Sociodemographic determinants of COVID-19 incidence rates in Oman: Geospatial modelling using multiscale geographically weighted regression (MGWR). Sustainable Cities and Society, 2021. 65: p. 102627.
28. Zafri, N.M. and A. Khan, A spatial regression modeling framework for examining relationships between the built environment and pedestrian crash occurrences at macroscopic level: A study in a developing country context. Geography and sustainability, 2022. 3(4): p. 312-324.
29. Fotheringham, A., S, W. Yang, and W. Kang, Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 2017. 107(6): p. 1247-1265.
30. Brunsdon, C., S. Fotheringham, and M. Charlton, Geographically weighted regression. Journal of the Royal Statistical Society: Series D (The Statistician), 1998. 47(3): p. 431-443.
31. Oshan, T., M, et al., mgwr: A Python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale. ISPRS International Journal of Geo-Information, 2019. 8(6): p. 269.
32. Shabrina, Z., B. Buyuklieva, and M.K.M. Ng, Short‐term rental platform in the urban tourism context: A geographically weighted regression (GWR) and a multiscale GWR (MGWR) approaches. Geographical Analysis, 2021. 53(4): p. 686-707.
33. Anselin, L., Exploring spatial data witℎ GeoDaTM: a workbook. Center for spatially integrated social science. https://www. geos. ed. ac. uk/~ gisteac/fspat/geodaworkbook. pdf, 2005.
34. Stimson, J.A., Regression in space and time: A statistical essay. American Journal of Political Science, 1985: p. 914-947.
35. Thompson, C.G., et al., Extracting the variance inflation factor and other multicollinearity diagnostics from typical regression results. Basic and Applied Social Psychology, 2017. 39(2): p. 81-90.
36. Tu, J. and Z.-G. Xia, Examining spatially varying relationships between land use and water quality using geographically weighted regression I: Model design and evaluation. Science of the total environment, 2008. 407(1): p. 358-378.
37. Huang, Y., X. Wang, and D. Patton, Examining spatial relationships between crashes and the built environment: A geographically weighted regression approach. Journal of transport geography, 2018. 69: p. 221-233.
38. Pljakić, M., et al., Macro-level accident modeling in Novi Sad: A spatial regression approach. Accident Analysis & Prevention, 2019. 132: p. 105259.
39. Cordes, J. and M.C. Castro, Spatial analysis of COVID-19 clusters and contextual factors in New York City. Spatial and spatio-temporal epidemiology, 2020. 34: p. 100355.
40. Anselin, L., A. Varga, and Z. Acs, Geographical spillovers and university research: A spatial econometricperspective. Growth and change, 2000. 31(4): p. 501-515.
41. Chica-Olmo, J., S. Sari-Hassoun, and P. Moya-Fernández, Spatial relationship between economic growth and renewable energy consumption in 26 European countries. Energy Economics, 2020. 92: p. 104962.
42. Tay, L., et al., Graphical descriptives: A way to improve data transparency and methodological rigor in psychology. Perspectives on Psychological Science, 2016. 11(5): p. 692-701.
43. Pisică, D., et al., Tenets of good practice in regression analysis. a brief tutorial. World neurosurgery, 2022. 161: p. 230-239. e6.
44. Zhang, Z. and S.V. Poucke, Citations for randomized controlled trials in sepsis literature: the halo effect caused by journal impact factor. PLoS One, 2017. 12(1): p. e0169398.
45. Agnihotri, D., Assessing mHealth Motivational Pathways Among Hispanic Individuals Through Technological Affordances. 2022.
46. Anselin, L., Local indicators of spatial association—LISA. Geographical analysis, 1995. 27(2): p. 93-115.
47. Osborne, P.E., G.M. Foody, and S. Suárez‐Seoane, Non‐stationarity and local approaches to modelling the distributions of wildlife. Diversity and Distributions, 2007. 13(3): p. 313-323.
48. Nelson, J.K. and C.A. Brewer, Evaluating data stability in aggregation structures across spatial scales: revisiting the modifiable areal unit problem. Cartography and Geographic Information Science, 2017. 44(1): p. 35-50.
49. Anselin, L., Chapter Eight-The Moran scatterplot as an ESDA tool to Assess Local Instability in Spatial Association. Spatial Analytical, 1996. 4: p. 121.
50. Cellmer, R., A. Cichulska, and M. Bełej, Spatial analysis of housing prices and market activity with the geographically weighted regression. ISPRS International Journal of Geo-Information, 2020. 9(6): p. 380.
51. Bithell, J.F., An application of density estimation to geographical epidemiology. Statistics in medicine, 1990. 9(6): p. 691-701.
52. Kie, J.G., A rule-based ad hoc method for selecting a bandwidth in kernel home-range analyses. Animal Biotelemetry, 2013. 1(1): p. 1-12.
53. Bidanset, P.E. and J.R. Lombard, 7 Optimal kernel and bandwidth specifications for geographically weighted regression. Applied Spatial Modelling and Planning, 2016.
54. Charlton, M., S. Fotheringham, and C. Brunsdon, Geographically weighted regression. White paper. National Centre for Geocomputation. National University of Ireland Maynooth, 2009. 2.
55. Title, P.O. and J.B. Bemmels, ENVIREM: an expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography, 2018. 41(2): p. 291-307.
56. Zin, W.W., et al., Multivariate flood loss estimation of the 2018 Bago flood in Myanmar. Journal of Disaster Research, 2020. 15(3): p. 300-311.
57. Ombelet, W., et al., Infertility and the provision of infertility medical services in developing countries. Human reproduction update, 2008. 14(6): p. 605-621.
58. Requia, W.J., et al., Risk of the Brazilian health care system over 5572 municipalities to exceed health care capacity due to the 2019 novel coronavirus (COVID-19). Science of the Total Environment, 2020. 730: p. 139144.
59. Nakada, L.Y.K. and R.C. Urban, COVID-19 pandemic: environmental and social factors influencing the spread of SARS-CoV-2 in São Paulo, Brazil. Environmental Science and Pollution Research, 2021. 28: p. 40322-40328.
60. Harris, R., Exploring the neighbourhood-level correlates of Covid-19 deaths in London using a difference across spatial boundaries method. Health and Place, 2020. 66: p. 102446.
2. Bayode, T., et al., Spatial variability of COVID-19 and its risk factors in Nigeria: A spatial regression method. Applied Geography, 2022. 138: p. 102621.
3. Urban, R., C and L. Nakada, Y, K, GIS-based spatial modelling of COVID-19 death incidence in São Paulo, Brazil. Environment and Urbanization, 2021. 33(1): p. 229–238.
4. Kotov, E.A., et al., Spatial modelling of key regional-level factors of Covid-19 mortality in Russia. Geography, Environment, Sustainability, 2022. 15(2): p. 71-83.
5. Mesmar, J. and A. Badran, The Post-COVID Classroom: Lessons from a Pandemic, in Higher Education in the Arab World. 2022, Springer. p. 11-41.
6. Cutler, D.M. and L.H. Summers, The COVID-19 pandemic and the $16 trillion virus. Jama, 2020. 324(15): p. 1495-1496.
7. Denning, M., et al., Determinants of burnout and other aspects of psychological well-being in healthcare workers during the Covid-19 pandemic: A multinational cross-sectional study. Plos one, 2021. 16(4): p. e0238666.
8. Delis, M.D., M. Iosifidi, and M. Tasiou, Efficiency of government policy during the COVID-19 pandemic. Available at SSRN 3821814, 2022.
9. Fay, M., et al., Hitting the Trillion Mark--A Look at How Much Countries Are Spending on Infrastructure. World Bank Policy Research Working Paper, 2019(8730).
10. Aminova, M., S. Mareef, and C. Machado, Entrepreneurship Ecosystem in Arab World: the status quo, impediments and the ways forward. International Journal of Business Ethics and Governance, 2020. 3(3): p. 1-13.
11. Davoodi, M.H.R. and M.G.T. Abed, Challenges of growth and globalization in the Middle East and North Africa. 2003: International Monetary Fund.
12. Ward, M., D and K. Gleditsch, S, Spatial regression models. Vol. 115. 2018, china: Sage Publications.
13. Mollalo, A., et al., Predicting the hotspots of age-adjusted mortality rates of lower respiratory infection across the continental United States: Integration of GIS, spatial statistics and machine learning algorithms. International Journal of Medical Informatics, 2020. 142: p. 104248.
14. Anselin, L. and D. Arribas-Bel, Spatial fixed effects and spatial dependence in a single cross‐section. Papers in Regional Science, 2013. 92(1): p. 3-17.
15. Sannigrahi, S., et al., Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach. Sustainable cities and society, 2020. 62: p. 102418.
16. Mollalo, A., B. Vahedi, and K.M. Rivera, GIS-based spatial modeling of COVID-19 incidence rate in the continental United States. Science of the total environment, 2020. 728: p. 138884.
17. Rahman, M., A, et al., Data-driven dynamic clustering framework for mitigating the adverse economic impact of Covid-19 lockdown practices. Sustainable Cities and Society, 2020. 62: p. 102372.
18. Monica and R. Mishra, An epidemiological study of cervical and breast screening in India: district-level analysis. BMC women's health, 2020. 20: p. 1-15.
19. Oshan, T.M., et al., mgwr: A Python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale. ISPRS International Journal of Geo-Information, 2019. 8(6): p. 269.
20. Dutta, I., T. Basu, and A. Das, Spatial analysis of COVID-19 incidence and its determinants using spatial modeling: A study on India. Environmental Challenges, 2021. 4: p. 100096.
21. Kissling, W.D. and G. Carl, Spatial autocorrelation and the selection of simultaneous autoregressive models. Global Ecology and Biogeography, 2008. 17(1): p. 59-71.
22. Deilami, K., M. Kamruzzaman, and J.F. Hayes, Correlation or causality between land cover patterns and the urban heat island effect? Evidence from Brisbane, Australia. Remote Sensing, 2016. 8(9): p. 716.
23. Comber, A., et al., A route map for successful applications of geographically weighted regression. Geographical Analysis, 2022: p. 2022.
24. Chien, Y.-M.C., S. Carver, and A. Comber, Using geographically weighted models to explore how crowdsourced landscape perceptions relate to landscape physical characteristics. Landscape and Urban Planning, 2020. 203: p. 103904.
25. Deilami, K. and M. Kamruzzaman, Modelling the urban heat island effect of smart growth policy scenarios in Brisbane. Land use policy. The International Journal Covering All Aspects of Land Use, 2017. 64: p. 38-55.
26. Hamad, F., et al., Viability of Transplanted Organs Based on Donor’s Age. Sch J Phys Math Stat, 2023. 4: p. 97-104.
27. Mansour, S., et al., Sociodemographic determinants of COVID-19 incidence rates in Oman: Geospatial modelling using multiscale geographically weighted regression (MGWR). Sustainable Cities and Society, 2021. 65: p. 102627.
28. Zafri, N.M. and A. Khan, A spatial regression modeling framework for examining relationships between the built environment and pedestrian crash occurrences at macroscopic level: A study in a developing country context. Geography and sustainability, 2022. 3(4): p. 312-324.
29. Fotheringham, A., S, W. Yang, and W. Kang, Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 2017. 107(6): p. 1247-1265.
30. Brunsdon, C., S. Fotheringham, and M. Charlton, Geographically weighted regression. Journal of the Royal Statistical Society: Series D (The Statistician), 1998. 47(3): p. 431-443.
31. Oshan, T., M, et al., mgwr: A Python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale. ISPRS International Journal of Geo-Information, 2019. 8(6): p. 269.
32. Shabrina, Z., B. Buyuklieva, and M.K.M. Ng, Short‐term rental platform in the urban tourism context: A geographically weighted regression (GWR) and a multiscale GWR (MGWR) approaches. Geographical Analysis, 2021. 53(4): p. 686-707.
33. Anselin, L., Exploring spatial data witℎ GeoDaTM: a workbook. Center for spatially integrated social science. https://www. geos. ed. ac. uk/~ gisteac/fspat/geodaworkbook. pdf, 2005.
34. Stimson, J.A., Regression in space and time: A statistical essay. American Journal of Political Science, 1985: p. 914-947.
35. Thompson, C.G., et al., Extracting the variance inflation factor and other multicollinearity diagnostics from typical regression results. Basic and Applied Social Psychology, 2017. 39(2): p. 81-90.
36. Tu, J. and Z.-G. Xia, Examining spatially varying relationships between land use and water quality using geographically weighted regression I: Model design and evaluation. Science of the total environment, 2008. 407(1): p. 358-378.
37. Huang, Y., X. Wang, and D. Patton, Examining spatial relationships between crashes and the built environment: A geographically weighted regression approach. Journal of transport geography, 2018. 69: p. 221-233.
38. Pljakić, M., et al., Macro-level accident modeling in Novi Sad: A spatial regression approach. Accident Analysis & Prevention, 2019. 132: p. 105259.
39. Cordes, J. and M.C. Castro, Spatial analysis of COVID-19 clusters and contextual factors in New York City. Spatial and spatio-temporal epidemiology, 2020. 34: p. 100355.
40. Anselin, L., A. Varga, and Z. Acs, Geographical spillovers and university research: A spatial econometricperspective. Growth and change, 2000. 31(4): p. 501-515.
41. Chica-Olmo, J., S. Sari-Hassoun, and P. Moya-Fernández, Spatial relationship between economic growth and renewable energy consumption in 26 European countries. Energy Economics, 2020. 92: p. 104962.
42. Tay, L., et al., Graphical descriptives: A way to improve data transparency and methodological rigor in psychology. Perspectives on Psychological Science, 2016. 11(5): p. 692-701.
43. Pisică, D., et al., Tenets of good practice in regression analysis. a brief tutorial. World neurosurgery, 2022. 161: p. 230-239. e6.
44. Zhang, Z. and S.V. Poucke, Citations for randomized controlled trials in sepsis literature: the halo effect caused by journal impact factor. PLoS One, 2017. 12(1): p. e0169398.
45. Agnihotri, D., Assessing mHealth Motivational Pathways Among Hispanic Individuals Through Technological Affordances. 2022.
46. Anselin, L., Local indicators of spatial association—LISA. Geographical analysis, 1995. 27(2): p. 93-115.
47. Osborne, P.E., G.M. Foody, and S. Suárez‐Seoane, Non‐stationarity and local approaches to modelling the distributions of wildlife. Diversity and Distributions, 2007. 13(3): p. 313-323.
48. Nelson, J.K. and C.A. Brewer, Evaluating data stability in aggregation structures across spatial scales: revisiting the modifiable areal unit problem. Cartography and Geographic Information Science, 2017. 44(1): p. 35-50.
49. Anselin, L., Chapter Eight-The Moran scatterplot as an ESDA tool to Assess Local Instability in Spatial Association. Spatial Analytical, 1996. 4: p. 121.
50. Cellmer, R., A. Cichulska, and M. Bełej, Spatial analysis of housing prices and market activity with the geographically weighted regression. ISPRS International Journal of Geo-Information, 2020. 9(6): p. 380.
51. Bithell, J.F., An application of density estimation to geographical epidemiology. Statistics in medicine, 1990. 9(6): p. 691-701.
52. Kie, J.G., A rule-based ad hoc method for selecting a bandwidth in kernel home-range analyses. Animal Biotelemetry, 2013. 1(1): p. 1-12.
53. Bidanset, P.E. and J.R. Lombard, 7 Optimal kernel and bandwidth specifications for geographically weighted regression. Applied Spatial Modelling and Planning, 2016.
54. Charlton, M., S. Fotheringham, and C. Brunsdon, Geographically weighted regression. White paper. National Centre for Geocomputation. National University of Ireland Maynooth, 2009. 2.
55. Title, P.O. and J.B. Bemmels, ENVIREM: an expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography, 2018. 41(2): p. 291-307.
56. Zin, W.W., et al., Multivariate flood loss estimation of the 2018 Bago flood in Myanmar. Journal of Disaster Research, 2020. 15(3): p. 300-311.
57. Ombelet, W., et al., Infertility and the provision of infertility medical services in developing countries. Human reproduction update, 2008. 14(6): p. 605-621.
58. Requia, W.J., et al., Risk of the Brazilian health care system over 5572 municipalities to exceed health care capacity due to the 2019 novel coronavirus (COVID-19). Science of the Total Environment, 2020. 730: p. 139144.
59. Nakada, L.Y.K. and R.C. Urban, COVID-19 pandemic: environmental and social factors influencing the spread of SARS-CoV-2 in São Paulo, Brazil. Environmental Science and Pollution Research, 2021. 28: p. 40322-40328.
60. Harris, R., Exploring the neighbourhood-level correlates of Covid-19 deaths in London using a difference across spatial boundaries method. Health and Place, 2020. 66: p. 102446.
| Files | ||
| Issue | Vol 10 No 1 (2024) | |
| Section | Articles | |
| DOI | https://doi.org/10.18502/jbe.v10i1.17152 | |
| Keywords | ||
| GIS modeling spatial modeling Multiscale GWR geostatistical models MENA region COVID-19 global models. | ||
| Rights and permissions | |
|
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
SHEBANI ABOALYEM M, Ismail MT. Geospatial Analysis of COVID-19 Death Rate and Influencing Factors in the MENA Region. JBE. 2024;10(1):33-52.
