IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i14p8267-d857114.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/14/8267/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/14/8267/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mohammad Arif & Soumita Sengupta, 2021. "Nexus between population density and novel coronavirus (COVID-19) pandemic in the south Indian states: A geo-statistical approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(7), pages 10246-10274, July.
    2. Roger J. Marshall, 1991. "A Review of Methods for the Statistical Analysis of Spatial Patterns of Disease," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 154(3), pages 421-441, May.
    3. Yoo Min Park & Gregory D. Kearney & Bennett Wall & Katherine Jones & Robert J. Howard & Ray H. Hylock, 2021. "COVID-19 Deaths in the United States: Shifts in Hot Spots over the Three Phases of the Pandemic and the Spatiotemporally Varying Impact of Pandemic Vulnerability," IJERPH, MDPI, vol. 18(17), pages 1-15, August.
    4. Munazza Fatima & Kara J. O’Keefe & Wenjia Wei & Sana Arshad & Oliver Gruebner, 2021. "Geospatial Analysis of COVID-19: A Scoping Review," IJERPH, MDPI, vol. 18(5), pages 1-14, February.
    5. Shirui Liu & Yaochen Qin & Zhixiang Xie & Jingfei Zhang, 2020. "The Spatio-Temporal Characteristics and Influencing Factors of Covid-19 Spread in Shenzhen, China—An Analysis Based on 417 Cases," IJERPH, MDPI, vol. 17(20), pages 1-13, October.
    6. Sarah L. Jackson & Sahar Derakhshan & Leah Blackwood & Logan Lee & Qian Huang & Margot Habets & Susan L. Cutter, 2021. "Spatial Disparities of COVID-19 Cases and Fatalities in United States Counties," IJERPH, MDPI, vol. 18(16), pages 1-21, August.
    7. Mahmoud Torabi, 2012. "Spatial modeling using frequentist approach for disease mapping," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(11), pages 2431-2439, July.
    8. I. Gede Nyoman M. Jaya & Henk Folmer, 2021. "Bayesian spatiotemporal forecasting and mapping of COVID‐19 risk with application to West Java Province, Indonesia," Journal of Regional Science, Wiley Blackwell, vol. 61(4), pages 849-881, September.
    9. Ludwig Fahrmeir & Stefan Lang, 2001. "Bayesian inference for generalized additive mixed models based on Markov random field priors," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(2), pages 201-220.
    10. Amory Martin & Maryia Markhvida & Stéphane Hallegatte & Brian Walsh, 2020. "Socio-Economic Impacts of COVID-19 on Household Consumption and Poverty," Economics of Disasters and Climate Change, Springer, vol. 4(3), pages 453-479, October.
    11. Min Xu & Chunxiang Cao & Xin Zhang & Hui Lin & Zhong Yao & Shaobo Zhong & Zhibin Huang & Robert Shea Duerler, 2021. "Fine-Scale Space-Time Cluster Detection of COVID-19 in Mainland China Using Retrospective Analysis," IJERPH, MDPI, vol. 18(7), pages 1-17, March.
    12. Fuyu Xu & Kate Beard, 2021. "A comparison of prospective space-time scan statistics and spatiotemporal event sequence based clustering for COVID-19 surveillance," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-23, June.
    13. Arif Masrur & Manzhu Yu & Wei Luo & Ashraf Dewan, 2020. "Space-Time Patterns, Change, and Propagation of COVID-19 Risk Relative to the Intervention Scenarios in Bangladesh," IJERPH, MDPI, vol. 17(16), pages 1-22, August.
    14. Tao Hu & Han Yue & Changzhen Wang & Bing She & Xinyue Ye & Regina Liu & Xinyan Zhu & Weihe Wendy Guan & Shuming Bao, 2020. "Racial Segregation, Testing Site Access, and COVID-19 Incidence Rate in Massachusetts, USA," IJERPH, MDPI, vol. 17(24), pages 1-18, December.
    15. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    16. Kate E. Jones & Nikkita G. Patel & Marc A. Levy & Adam Storeygard & Deborah Balk & John L. Gittleman & Peter Daszak, 2008. "Global trends in emerging infectious diseases," Nature, Nature, vol. 451(7181), pages 990-993, February.
    17. Earl W Duncan & Kerrie L Mengersen, 2020. "Comparing Bayesian spatial models: Goodness-of-smoothing criteria for assessing under- and over-smoothing," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-28, May.
    18. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    19. Qiaowen Lin & Guoliang Ou & Renyang Wang & Yanan Li & Yi Zhao & Zijun Dong, 2021. "The Spatiotemporal Characteristics and Climatic Factors of COVID-19 in Wuhan, China," Sustainability, MDPI, vol. 13(14), pages 1-17, July.
    20. Jinting Zhang & Xiu Wu & T. Edwin Chow, 2021. "Space-Time Cluster’s Detection and Geographical Weighted Regression Analysis of COVID-19 Mortality on Texas Counties," IJERPH, MDPI, vol. 18(11), pages 1-21, May.
    21. Xin Li & Lin Zhou & Tao Jia & Ran Peng & Xiongwu Fu & Yuliang Zou, 2020. "Associating COVID-19 Severity with Urban Factors: A Case Study of Wuhan," IJERPH, MDPI, vol. 17(18), pages 1-20, September.
    22. Heyuan You & Xin Wu & Xuxu Guo, 2020. "Distribution of COVID-19 Morbidity Rate in Association with Social and Economic Factors in Wuhan, China: Implications for Urban Development," IJERPH, MDPI, vol. 17(10), pages 1-14, May.
    23. Yongzhu Xiong & Yunpeng Wang & Feng Chen & Mingyong Zhu, 2020. "Spatial Statistics and Influencing Factors of the COVID-19 Epidemic at Both Prefecture and County Levels in Hubei Province, China," IJERPH, MDPI, vol. 17(11), pages 1-26, May.
    24. Ivan J. Ramírez & Jieun Lee, 2020. "COVID-19 Emergence and Social and Health Determinants in Colorado: A Rapid Spatial Analysis," IJERPH, MDPI, vol. 17(11), pages 1-15, May.
    25. Wei Zhai & Mengyang Liu & Xinyu Fu & Zhong-Ren Peng, 2021. "American Inequality Meets COVID-19: Uneven Spread of the Disease across Communities," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 111(7), pages 2023-2043, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Pavlov, Konstantin & Timiryanova, Venera & Yusupov, Kasim & Krasnoselskaya, Dina, 2022. "Анализ волн распространения Covid-19 в России [Analysis of Covid-19 wave distribution in Russia]," MPRA Paper 114637, University Library of Munich, Germany.
    2. 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.
    3. Ming Sun & Tiange Xu, 2024. "Identification and Management of Epidemic Hazard Areas for Urban Sustainability: A Case Study of Tongzhou, China," Sustainability, MDPI, vol. 16(18), pages 1-25, September.
    4. 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.
    5. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Volker Schmid & Leonhard Held, 2004. "Bayesian Extrapolation of Space–Time Trends in Cancer Registry Data," Biometrics, The International Biometric Society, vol. 60(4), pages 1034-1042, December.
    2. Klein, Nadja & Herwartz, Helmut & Kneib, Thomas, 2020. "Modelling regional patterns of inefficiency: A Bayesian approach to geoadditive panel stochastic frontier analysis with an application to cereal production in England and Wales," Journal of Econometrics, Elsevier, vol. 214(2), pages 513-539.
    3. Tamvada, Jagannadha Pawan, 2010. "The Dynamics of Self-employment in a Developing Country: Evidence from India," MPRA Paper 20042, University Library of Munich, Germany.
    4. Congdon, Peter, 2006. "A model for non-parametric spatially varying regression effects," Computational Statistics & Data Analysis, Elsevier, vol. 50(2), pages 422-445, January.
    5. Peter Congdon, 2010. "A Multilevel Model for Comorbid Outcomes: Obesity and Diabetes in the US," IJERPH, MDPI, vol. 7(2), pages 1-20, January.
    6. Areti Boulieri & Silvia Liverani & Kees Hoogh & Marta Blangiardo, 2017. "A space–time multivariate Bayesian model to analyse road traffic accidents by severity," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(1), pages 119-139, January.
    7. Zhang Hongmei & Gan Jianjun, 2012. "A Reproducing Kernel-Based Spatial Model in Poisson Regressions," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-26, October.
    8. Jingjing Wang & Xueying Wu & Ruoyu Wang & Dongsheng He & Dongying Li & Linchuan Yang & Yiyang Yang & Yi Lu, 2021. "Review of Associations between Built Environment Characteristics and Severe Acute Respiratory Syndrome Coronavirus 2 Infection Risk," IJERPH, MDPI, vol. 18(14), pages 1-16, July.
    9. Gamerman, Dani & Moreira, Ajax R. B., 2004. "Multivariate spatial regression models," Journal of Multivariate Analysis, Elsevier, vol. 91(2), pages 262-281, November.
    10. Leonhard Knorr-Held & Günter Raßer & Nikolaus Becker, 2002. "Disease Mapping of Stage-Specific Cancer Incidence Data," Biometrics, The International Biometric Society, vol. 58(3), pages 492-501, September.
    11. Alexander Razen & Stefan Lang & Judith Santer, 2016. "Estimation of Spatially Correlated Random Scaling Factors based on Markov Random Field Priors," Working Papers 2016-33, Faculty of Economics and Statistics, Universität Innsbruck.
    12. Brezger, Andreas & Lang, Stefan, 2006. "Generalized structured additive regression based on Bayesian P-splines," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 967-991, February.
    13. Eibich, Peter & Ziebarth, Nicolas, 2014. "Examining the Structure of Spatial Health Effects in Germany Using Hierarchical Bayes Models," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 49, pages 305-320.
    14. Mayer Alvo & Jingrui Mu, 2023. "COVID-19 Data Analysis Using Bayesian Models and Nonparametric Geostatistical Models," Mathematics, MDPI, vol. 11(6), pages 1-13, March.
    15. Louise Choo & Stephen G. Walker, 2008. "A new approach to investigating spatial variations of disease," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(2), pages 395-405, April.
    16. Massimo Bilancia & Giacomo Demarinis, 2014. "Bayesian scanning of spatial disease rates with integrated nested Laplace approximation (INLA)," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(1), pages 71-94, March.
    17. Douglas R. M. Azevedo & Marcos O. Prates & Dipankar Bandyopadhyay, 2021. "MSPOCK: Alleviating Spatial Confounding in Multivariate Disease Mapping Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 464-491, September.
    18. Munazza Fatima & Kara J. O’Keefe & Wenjia Wei & Sana Arshad & Oliver Gruebner, 2021. "Geospatial Analysis of COVID-19: A Scoping Review," IJERPH, MDPI, vol. 18(5), pages 1-14, February.
    19. Francisca Corpas-Burgos & Miguel A. Martinez-Beneito, 2021. "An Autoregressive Disease Mapping Model for Spatio-Temporal Forecasting," Mathematics, MDPI, vol. 9(4), pages 1-17, February.
    20. Li Xu & Qingshan Jiang & David R. Lairson, 2019. "Spatio-Temporal Variation of Gender-Specific Hypertension Risk: Evidence from China," IJERPH, MDPI, vol. 16(22), pages 1-26, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:14:p:8267-:d:857114. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.