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Article

Socio-economic and demographic determinants of COVID-19 infections and spread at household level: case study from Nigeria

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Pages 222-241 | Received 14 Aug 2021, Accepted 04 May 2022, Published online: 06 Jun 2022

References

  • Abate, B. B., Kassie, A. M., Kassaw, M. W., Aragie, T. G., & Masresha, S. A. (2020). Sex difference in coronavirus disease (COVID-19): A systematic review and meta-analysis. BMJ Open, 10(10), e040129. https://doi.org/10.1136/bmjopen-2020-040129
  • Ahmadi, M., Sharifi, A., Dorosti, S., Ghoushchi, S. J., & Ghanbari, N. (2020). Investigation of effective climatology parameters on COVID-19 outbreak in Iran. Science of the Total Environment, 729 , 138705. https://doi.org/10.1016/j.scitotenv.2020.138705
  • Alexander, J. T., & Steidl, A. (2012). Gender and the “laws of migration”: A reconsideration of nineteenth-century patterns. Social Science History, 36(2), 223–241 https://doi.org/10.1017/S0145553200011779
  • Baqui, P., Bica, I., Marra, V., Ercole, A., & van Der Schaar, M. (2020). Ethnic and regional variations in hospital mortality from COVID-19 in Brazil: A cross-sectional observational study. The Lancet Global Health, 8(8), e1018–e1026. PMID: 32622400. https://doi.org/10.1016/S2214-109X(20)30285-0
  • Burchett, H. E., Mayhew, S. H., Lavis, J. N., & Dobrow, M. J. (2015). The usefulness of different types of health research: Perspectives from a low-income country. Evidence & Policy: A Journal of Research, Debate and Practice, 11(1), 19–33. https://doi.org/10.1332/174426514X13990430410723
  • CDC. (2021) Retrieved April 23, 2021, from https://africacdc.org/download/outbreak-brief-38-coronavirus-disease-2019-covid-19-pandemic/
  • Conticini, E., Frediani, B., & Caro, D. (2020). Can atmospheric pollution be considered a co-factor in extremely high level of SARS-CoV-2 lethality in northern Italy? Environmental Pollution, 261, 114465 . https://doi.org/10.1016/j.envpol.2020.114465
  • Cordes, J., & Castro, M. C. (2020). Spatial analysis of COVID-19 clusters and contextual factors in New York City. Spatial and Spatio-Temporal Epidemiology, 34, 21. https://doi.org/10.1016/j.sste.2020.100355
  • Dadar, M., Fakhri, Y., Bjørklund, G., & Shahali, Y. (2020). The association between the incidence of COVID-19 and the distance from the virus epicenter in Iran. Archives of Virology, 165(11), 2555–2560. https://doi.org/10.1007/s00705-020-04774-5
  • Davis, J. C. (1986). Statistics and data analysis in geology. John Wiley & Sons.
  • de Smith, M. J., Goodchild, M. F., & Longley, P. A. (2018). Geospatial analysis: A comprehensive guide to principles techniques and software tools (sixth ed.). Winchelsea Press.
  • Dhama, K., Patel, S. K., Kumar, R., Rana, J., Yatoo, M., Kumar, A., Tiwari, R., Dhama, J., Natesan, S., Singh, R., & Harapan, H. (2020). Geriatric population during the COVID-19 pandemic: Problems, considerations, exigencies, and beyond. Frontiers in Public Health, 8, 574198. https://doi.org/10.3389/fpubh.2020.574198
  • Dowd, J. M., Andriano, L., Brazel, D. M., Rotondi, V., Block, P., Ding, X., Liu, Y., & Mills, M. C. (2020). Demographic science aids in understanding the spread and fatality rates of COVID-19. Proceedings of the National Academy of Sciences of the United States of America, 117(18), 9696–9698. https://doi.org/10.1073/pnas.2004911117
  • Ekiti State Government, EKSG (2021 March 8). Ekiti COVID-19 survey (EKCOVS) 2020: State-wide household survey of prevalence, risk perception, adoption of preventive measures and people’s understanding of COVID-19 in Ekiti State, Nigeria. Technical Report. ISRN EKSG/EKSUTH–2021/02+NG. 88pp.
  • Emanuel, E. J., Persad, G., Upshur, R., Thome, B., Parker, M., Glickman, A., Zhang, C., Boyle, C., Smith, M., & Phillips, J. P. (2020). Fair allocation of scarce medical resources in the time of COVID-19. New England Journal of Medicine, 382(21), 2049–2055. https://doi.org/10.1056/NEJMsb2005114
  • Fatima, M., O’Keefe, K. J., Wei, W., Arshad, S., & Gruebner, O. (2021). Geospatial analysis of COVID-19: A scoping review. International Journal of Environmental Research and Public Health, 18(5), 2336. https://doi.org/10.3390/ijerph18052336
  • Fattorini, D., & Regoli, F. (2020). Role of the chronic air pollution levels in the COVID-19 outbreak risk in Italy. Environmental Pollution, 264, 114732 https://doi.org/10.1016/j.envpol.2020.114732 .
  • Garcia, M. A., Homan, P. A., García, C., & Brown, T. H. (2020). The color of COVID-19: Structural racism and the pandemic’s disproportionate impact on older racial and ethnic minorities. The Journals of Gerontology: Series B, 76(3), e75- e80 doi:10.1093/geronb/gbaa114 . https://digitalcommons.unl.edu/sociologyfacpub/723
  • Hammer, Ø., Harper, D. A. T., & Ryan, P. D. (2001). PAST: Paleontological statistics software package for education and data analysis. Palaeontologia Electronica, 4(1), 1–9 https://palaeo-electronica.org/2001_1/past/issue1_01.htm.
  • Henning, A., McLaughlin, C., Armen, S., & Allen, S. (2021). Socio-spatial influences on the prevalence of COVID-19 in central Pennsylvania. Spatial and Spatio-temporal Epidemiology, 37(2021), 100411. https://doi.org/10.1016/j.sste.2021.100411
  • Kang, D., Choi, H., Kim, J.-H., & Choi, J. (2020). Spatial epidemic dynamics of the COVID-19 outbreak in China. International Journal of Infectious Diseases, 94, 96–102. https://doi.org/10.1016/j.ijid.2020.03.076
  • Karaye, I. M., & Horney, J. A. (2020). The impact of social vulnerability on COVID-19 in the U.S.: An analysis of spatially varying relationships. American Journal of Preventive Medicine, 59(3), 317–325. https://doi.org/10.1016/j.amepre.2020.06.006
  • Karmakar, M., Lantz, P. M., & Tipirneni, R. (2021). Association of social and demographic factors with COVID-19 incidence and death rates in the US. JAMA Network Open, 4(1), e2036462. https://doi.org/10.1001/jamanetworkopen.2020.36462
  • Khazanchi, R., Beiter, E. R., Gondi, S., Beckman, A. L., Bilinski, A., & Ganguli, I. (2020). County-level association of social vulnerability with COVID-19 cases and deaths in the USA. Journal of General Internal Medicine, 35(9), 2784–2787. https://doi.org/10.1007/s11606-020-05882-3
  • Lai, C. C., Wang, C. Y., Wang, Y. H., Hsueh, S. C., Ko, W. C., & Hsueh, P.-R. (2020). Global epidemiology of coronavirus disease 2019 (COVID-19): Disease incidence, daily cumulative index, mortality, and their association with country healthcare resources and economic status. International Journal of Antimicrobial Agents, 55(4), 105946. https://doi.org/10.1016/j.ijantimicag.2020.105946
  • Macharia, P. M., Joseph, N. K., & Okiro, E. A. (2020). A vulnerability index for COVID-19: Spatial analysis at the subnational level in Kenya. BMJ Global Health, 5(8), e003014. https://doi.org/10.1136/bmjgh-2020-003014
  • Matthew, O. J., Eludoyin, A. O., & Oluwadiya, S. K. (2021). Spatio-temporal variations in COVID-19 cases in relation to the global climate distribution and fluctuations. Spatial and Spatio-temporal Epidemiology, 37, 100417. https://doi.org/10.1016/j.sste.2021.100417
  • NBS. (2017). Demographic statistics bulletin. National Bureau of Statistics.
  • NCDC. (2020). https://africacdc.org/covid-19/#, retrieved April 23, 2021
  • NPC (National Population Commission). (2006). National population commission of Nigeria- 2006. Population Census.
  • Qiu, Y., Chen, X., & Shi, W. (2020). Impacts of social and economic factors on the transmission of coronavirus disease 2019 (COVID-19) in China. Journal of Population Economics, 33(4), 1127–1172. https://doi.org/10.1007/s00148-020-00778-2
  • Rafael, R. D.-M. R., Neto, M., Depret, D. G., Gil, A. C., Fonseca, M. H. S., & Souza-Santos, R. (2020). Effect of income on the cumulative incidence of COVID-19: An ecological study. Revista Latino-Americana de Enfermagem, 28, e3344. https://doi.org/10.1590/1518-8345.4475.3344
  • Ravenstein, E. G. (1885). The laws of migration. Journal of the Statistical Society of London, 48(2), 167–235. https://doi.org/10.2307/2979181
  • Raymundo, C. E., Oliveira, M. C., Eleuterio, T. D., André, S. R., da Silva, M. G., Queiroz, E. R., & Medronho, R. D. (2021). Spatial analysis of COVID-19 incidence and the sociodemographic context in Brazil. Plos One, 16(3), e0247794. https://doi.org/10.1371/journal.pone.0247794
  • Safavi, P. E., Rahimian, K., Doustmohammadi, A., Dastjerdei, M. S., Rasouli, A., & Zahiri, J. (2021) A prediction model for COVID-19 prevalence based on demographic and healthcare parameters in Iran. medRxiv preprint. 19. https://doi.org/10.1101/2021.01.27.21250551
  • Sannigrahi, S., Pilla, F., Basu, B., Basu, A. S., & Molter, A. (2020). Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach. Sustainable Cities and Society, 62(2020), 102418. https://doi.org/10.1016/j.scs.2020.102418
  • Sun, Z., Zhang, H., Yang, Y., Wan, H., & Wang, Y. (2020). Impacts of geographic factors and population density on the COVID-19 spreading under the lockdown policies of China. Science of the Total Environment, 746(666), 141347. https://doi.org/10.1016/j.scitotenv.2020.141347
  • Toledo, S. L. D.-O., Nogueira, L. S., Carvalho, M. D.-G., Rios, D. R. A., & Pinheiro, M. D.-B. (2020). COVID-19: Review and hematologic impact. Clinica Chimica Acta, 510, 170–176. https://doi.org/10.1016/j.cca.2020.07.016
  • Tönshoff, B., Müller, B., Elling, R., Renk, H., Meer, P., Hengel, H., Garbade, S. F., Kieser, M., Jeltsch, K., Grulich-Henn, J., Euler, J., Stich, M., Chobanyan-Jürgens, K., Zernickel, M., Janda, A., Wölfle, L., Stamminger, T., Iftner, T., Ganzenmueller, T., … Kräusslich, H.-G. (2021). Prevalence of SARS-CoV-2 infection in children and their parents in southwest Germany. JAMA Pediatrics, 175(6), 586–593. https://doi.org/10.1001/jamapediatrics.2021.0001
  • Waleed, R. M., Sehar, I., Iftikhar, W., & Khan, H. S. (2020). Hematologic parameters in coronavirus infection (COVID-19) and their clinical implications. Discoveries, 8(4), e117. https://doi.org/10.15190/d.2020.14
  • Wang, L., Xu, C., Wang, J., Qiao, J., Yan, M., & Zhu, Q. (2021). Spatiotemporal heterogeneity and its determinants of COVID-19 transmission in typical labor export provinces of China. BMC Infectious Diseases, 21(1), 242. https://doi.org/10.1186/s12879-021-05926-x
  • Zhu, Y., Xie, J., Huang, F., & Cao, L. (2020). Association between short-term exposure to air pollution and COVID-19 infection: Evidence from China. Science of the Total Environment, 727, 138704. https://doi.org/10.1016/j.scitotenv.2020.138704

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