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Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review

Author

Listed:
  • Nushrat Nazia

    (School of Public Health Sciences, University of Waterloo, 200 University Ave West, Waterloo, ON N2L 3G1, Canada)

  • Zahid Ahmad Butt

    (School of Public Health Sciences, University of Waterloo, 200 University Ave West, Waterloo, ON N2L 3G1, Canada)

  • Melanie Lyn Bedard

    (School of Public Health Sciences, University of Waterloo, 200 University Ave West, Waterloo, ON N2L 3G1, Canada)

  • Wang-Choi Tang

    (School of Public Health Sciences, University of Waterloo, 200 University Ave West, Waterloo, ON N2L 3G1, Canada)

  • Hibah Sehar

    (School of Public Health Sciences, University of Waterloo, 200 University Ave West, Waterloo, ON N2L 3G1, Canada)

  • Jane Law

    (School of Public Health Sciences, University of Waterloo, 200 University Ave West, Waterloo, ON N2L 3G1, Canada
    School of Planning, University of Waterloo, 200 University Ave West, Waterloo, ON N2L 3G1, Canada)

Abstract

The spread of the COVID-19 pandemic was spatially heterogeneous around the world; the transmission of the disease is driven by complex spatial and temporal variations in socioenvironmental factors. Spatial tools are useful in supporting COVID-19 control programs. A substantive review of the merits of the methodological approaches used to understand the spatial epidemiology of the disease is hardly undertaken. In this study, we reviewed the methodological approaches used to identify the spatial and spatiotemporal variations of COVID-19 and the socioeconomic, demographic and climatic drivers of such variations. We conducted a systematic literature search of spatial studies of COVID-19 published in English from Embase, Scopus, Medline, and Web of Science databases from 1 January 2019 to 7 September 2021. Methodological quality assessments were also performed using the Joanna Briggs Institute (JBI) risk of bias tool. A total of 154 studies met the inclusion criteria that used frequentist (85%) and Bayesian (15%) modelling approaches to identify spatial clusters and the associated risk factors. Bayesian models in the studies incorporated various spatial, temporal and spatiotemporal effects into the modelling schemes. This review highlighted the need for more local-level advanced Bayesian spatiotemporal modelling through the multi-level framework for COVID-19 prevention and control strategies.

Suggested Citation

  • Nushrat Nazia & Zahid Ahmad Butt & Melanie Lyn Bedard & Wang-Choi Tang & Hibah Sehar & Jane Law, 2022. "Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review," IJERPH, MDPI, vol. 19(14), pages 1-28, July.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:14:p:8267-:d:857114
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    2. Conceição Leal & Leonel Morgado & Teresa A. Oliveira, 2023. "Mathematical and Statistical Modelling for Assessing COVID-19 Superspreader Contagion: Analysis of Geographical Heterogeneous Impacts from Public Events," Mathematics, MDPI, vol. 11(5), pages 1-18, February.
    3. X. Angela Yao & Andrew Crooks & Bin Jiang & Jukka Krisp & Xintao Liu & Haosheng Huang, 2023. "An overview of urban analytical approaches to combating the Covid-19 pandemic," Environment and Planning B, , vol. 50(5), pages 1133-1143, June.
    4. I Gede Nyoman Mindra Jaya & Farah Kristiani & Yudhie Andriyana & Anna Chadidjah, 2024. "Sensitivity Analysis on Hyperprior Distribution of the Variance Components of Hierarchical Bayesian Spatiotemporal Disease Mapping," Mathematics, MDPI, vol. 12(3), pages 1-16, January.

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