IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0252990.html
   My bibliography  Save this article

A comparison of prospective space-time scan statistics and spatiotemporal event sequence based clustering for COVID-19 surveillance

Author

Listed:
  • Fuyu Xu
  • Kate Beard

Abstract

The outbreak of the COVID-19 disease was first reported in Wuhan, China, in December 2019. Cases in the United States began appearing in late January. On March 11, the World Health Organization (WHO) declared a pandemic. By mid-March COVID-19 cases were spreading across the US with several hotspots appearing by April. Health officials point to the importance of surveillance of COVID-19 to better inform decision makers at various levels and efficiently manage distribution of human and technical resources to areas of need. The prospective space-time scan statistic has been used to help identify emerging COVID-19 disease clusters, but results from this approach can encounter strategic limitations imposed by constraints of the scanning window. This paper presents a different approach to COVID-19 surveillance based on a spatiotemporal event sequence (STES) similarity. In this STES based approach, adapted for this pandemic context we compute the similarity of evolving daily COVID-19 incidence rates by county and then cluster these sequences to identify counties with similarly trending COVID-19 case loads. We analyze four study periods and compare the sequence similarity-based clusters to prospective space-time scan statistic-based clusters. The sequence similarity-based clusters provide an alternate surveillance perspective by identifying locations that may not be spatially proximate but share a similar disease progression pattern. Results of the two approaches taken together can aid in tracking the progression of the pandemic to aid local or regional public health responses and policy actions taken to control or moderate the disease spread.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0252990
    DOI: 10.1371/journal.pone.0252990
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0252990
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0252990&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0252990?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Shuang-Bo Sun & Zhi-Heng Zhang & Xin-Ling Dong & Heng-Ru Zhang & Tong-Jun Li & Lin Zhang & Fan Min, 2017. "Integrating Triangle and Jaccard similarities for recommendation," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-16, August.
    2. Martin Kulldorff, 2001. "Prospective time periodic geographical disease surveillance using a scan statistic," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 61-72.
    3. Dionissi Aliprantis & Kristen Tauber, 2020. "Measuring Deaths from COVID-19," Economic Commentary, Federal Reserve Bank of Cleveland, vol. 2020(18), pages 1-7, July.
    4. Jennifer Beam Dowd & Liliana Andriano & David M. Brazel & Valentina Rotondi & Per Block & Xuejie Ding & Yan Liu & Melinda C. Mills, 2020. "Demographic science aids in understanding the spread and fatality rates of COVID-19," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(18), pages 9696-9698, May.
    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. 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.

    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. Jorge Paz, 2020. "Notas sobre la demografía del COVID-19 en Argentina," Working Papers 22, Instituto de Estudios Laborales y del Desarrollo Económico (IELDE) - Universidad Nacional de Salta - Facultad de Ciencias Económicas, Jurídicas y Sociales.
    2. Linus Schiöler & Marianne Fris�n, 2012. "Multivariate outbreak detection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(2), pages 223-242, April.
    3. Dong Ding & Axel Gandy & Georg Hahn, 2020. "A simple method for implementing Monte Carlo tests," Computational Statistics, Springer, vol. 35(3), pages 1373-1392, September.
    4. Battiston, Pietro & Gamba, Simona, 2021. "COVID-19: R0 is lower where outbreak is larger," Health Policy, Elsevier, vol. 125(2), pages 141-147.
    5. Laliotis, Ioannis & Minos, Dimitrios, 2022. "Religion, social interactions, and COVID-19 incidence in Western Germany," European Economic Review, Elsevier, vol. 141(C).
    6. Sami Ullah & Hanita Daud & Sarat C. Dass & Hadi Fanaee-T & Husnul Kausarian & Alamgir, 2020. "Space-Time Clustering Characteristics of Tuberculosis in Khyber Pakhtunkhwa Province, Pakistan, 2015–2019," IJERPH, MDPI, vol. 17(4), pages 1-10, February.
    7. Nepomuceno, Marília, 2020. "Vulnerable groups at increased risk of COVID-19 in sub-Saharan Africa: The case of the HIV population," OSF Preprints uyzjv, Center for Open Science.
    8. Sánchez-Romero, Miguel & Di Lego, Vanessa & Fürnkranz-Prskawetz, Alexia & Queiroz, Bernardo Lanza, 2020. "How many lives can be saved? A global view on the impact of testing, herd immunity and demographics on COVID-19 fatality rates," ECON WPS - Working Papers in Economic Theory and Policy 05/2020, TU Wien, Institute of Statistics and Mathematical Methods in Economics, Economics Research Unit.
    9. Alexander Dietrich & Edward S. Knotek & Michael McMain & Kristian Ove R. Myrseth & Raphael Schoenle & Michael Weber, 2021. "Expected Post-Pandemic Consumption and Scarred Expectations from COVID-19," Economic Commentary, Federal Reserve Bank of Cleveland, vol. 2021(11), pages 1-8, April.
    10. Naudé, Wim & Cameron, Martin, 2020. "Failing to Pull Together: South Africa's Troubled Response to COVID-19," IZA Discussion Papers 13649, Institute of Labor Economics (IZA).
    11. Costa, Marcelo Azevedo & Ruiz-Cárdenas, Ramiro & Mineti, Leandro Brioschi & Prates, Marcos Oliveira, 2021. "Dynamic time scan forecasting for multi-step wind speed prediction," Renewable Energy, Elsevier, vol. 177(C), pages 584-595.
    12. Hensel, Lukas & Witte, Marc & Caria, A. Stefano & Fetzer, Thiemo & Fiorin, Stefano & Götz, Friedrich M. & Gomez, Margarita & Haushofer, Johannes & Ivchenko, Andriy & Kraft-Todd, Gordon & Reutskaja, El, 2022. "Global Behaviors, Perceptions, and the Emergence of Social Norms at the Onset of the COVID-19 Pandemic," Journal of Economic Behavior & Organization, Elsevier, vol. 193(C), pages 473-496.
    13. Aydogan, Yigit, 2020. "Are Covid-19 Cases Independent of the City Sizes?," MPRA Paper 99697, University Library of Munich, Germany.
    14. Anthony Medford & Sergi Trias-Llimós, 2020. "Population age structure only partially explains the large number of COVID-19 deaths at the oldest ages," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 43(19), pages 533-544.
    15. Yingqi Zhao & Donglin Zeng & Amy H. Herring & Amy Ising & Anna Waller & David Richardson & Michael R. Kosorok, 2011. "Detecting Disease Outbreaks Using Local Spatiotemporal Methods," Biometrics, The International Biometric Society, vol. 67(4), pages 1508-1517, December.
    16. Ruth Benson & Jan Rigby & Christopher Brunsdon & Grace Cully & Lay San Too & Ella Arensman, 2022. "Quantitative Methods to Detect Suicide and Self-Harm Clusters: A Systematic Review," IJERPH, MDPI, vol. 19(9), pages 1-13, April.
    17. Nicola Borri & Francesco Drago & Chiara Santantonio & Francesco Sobbrio, 2021. "The “Great Lockdown”: Inactive workers and mortality by Covid‐19," Health Economics, John Wiley & Sons, Ltd., vol. 30(10), pages 2367-2382, September.
    18. Chenghe Guan & Junjie Tan & Brian Hall & Chao Liu & Ying Li & Zhichang Cai, 2022. "The Effect of the Built Environment on the COVID-19 Pandemic at the Initial Stage: A County-Level Study of the USA," Sustainability, MDPI, vol. 14(6), pages 1-17, March.
    19. Hakan Yilmazkuday, 2020. "COVID‐19 and unequal social distancing across demographic groups," Regional Science Policy & Practice, Wiley Blackwell, vol. 12(6), pages 1235-1248, December.
    20. Gerritse, Michiel, 2022. "COVID-19 transmission in cities," European Economic Review, Elsevier, vol. 150(C).

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0252990. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.