IDEAS home Printed from https://ideas.repec.org/a/kap/hcarem/v26y2023i1d10.1007_s10729-022-09619-y.html
   My bibliography  Save this article

Tracking Covid-19 cases and deaths in the United States: metrics of pandemic progression derived from a queueing framework

Author

Listed:
  • Randolph Hall

    (University of Southern California)

  • Andrew Moore

    (University of Southern California)

  • Mingdong Lyu

    (University of Southern California)

Abstract

We analyze the progression of COVID-19 in the United States over a nearly one-year period beginning March 1, 2020 with a novel metric motivated by queueing models, tracking partial-average day-of-event and cumulative probability distributions for events, where events are points in time when new cases and new deaths are reported. The partial average represents the average day of all events preceding a point of time, and is an indicator as to whether the pandemic is accelerating or decelerating in the context of the entire history of the pandemic. The measure supplements traditional metrics, and also enables direct comparisons of case and death histories on a common scale. We also compare methods for estimating actual infections and deaths to assess the timing and dynamics of the pandemic by location. Three example states are graphically compared as functions of date, as well as Hong Kong as an example that experienced a pronounced recent wave of the pandemic. In addition, statistics are compared for all 50 states. Over the period studied, average case day and average death day varied by two to five months among the 50 states, depending on data source, with the earliest averages in New York and surrounding states, as well as Louisiana.

Suggested Citation

  • Randolph Hall & Andrew Moore & Mingdong Lyu, 2023. "Tracking Covid-19 cases and deaths in the United States: metrics of pandemic progression derived from a queueing framework," Health Care Management Science, Springer, vol. 26(1), pages 79-92, March.
    • Handle: RePEc:kap:hcarem:v:26:y:2023:i:1:d:10.1007_s10729-022-09619-y
    DOI: 10.1007/s10729-022-09619-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10729-022-09619-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10729-022-09619-y?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kaxiras, Efthimios & Neofotistos, Georgios & Angelaki, Eleni, 2020. "The first 100 days: Modeling the evolution of the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    2. Wim Naudé & Ricardo Vinuesa, 2020. "Data, global development, and COVID-19: Lessons and consequences," WIDER Working Paper Series wp-2020-109, World Institute for Development Economic Research (UNU-WIDER).
    Full references (including those not matched with items on IDEAS)

    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. Fokas, A.S. & Cuevas-Maraver, J. & Kevrekidis, P.G., 2020. "A quantitative framework for exploring exit strategies from the COVID-19 lockdown," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    2. Naudé, Wim, 2020. "Industrialization under Medieval Conditions? Global Development after COVID-19," IZA Discussion Papers 13829, Institute of Labor Economics (IZA).
    3. Raghu Raman & Krishnashree Achuthan & Ricardo Vinuesa & Prema Nedungadi, 2021. "COVIDTAS COVID-19 Tracing App Scale—An Evaluation Framework," Sustainability, MDPI, vol. 13(5), pages 1-19, March.
    4. Raghu Raman & Ricardo Vinuesa & Prema Nedungadi, 2021. "Bibliometric Analysis of SARS, MERS, and COVID-19 Studies from India and Connection to Sustainable Development Goals," Sustainability, MDPI, vol. 13(14), pages 1-20, July.
    5. Gandzha, I.S. & Kliushnichenko, O.V. & Lukyanets, S.P., 2021. "Modeling and controlling the spread of epidemic with various social and economic scenarios," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    6. Matouk, A.E., 2020. "Complex dynamics in susceptible-infected models for COVID-19 with multi-drug resistance," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    7. Ballı, Serkan, 2021. "Data analysis of Covid-19 pandemic and short-term cumulative case forecasting using machine learning time series methods," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    8. Noha S. Alghamdi & Saeed M. Alghamdi, 2022. "The Role of Digital Technology in Curbing COVID-19," IJERPH, MDPI, vol. 19(14), pages 1-12, July.

    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:kap:hcarem:v:26:y:2023:i:1:d:10.1007_s10729-022-09619-y. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.