IDEAS home Printed from https://ideas.repec.org/a/gam/jstats/v6y2023i3p58-941d1242066.html
   My bibliography  Save this article

A Detecting System for Abrupt Changes in Temporal Incidence Rate of COVID-19 and Other Pandemics

Author

Listed:
  • Jiecheng Song

    (Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY 11794, USA
    These authors contributed equally to this work.)

  • Guanchao Tong

    (Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY 11794, USA
    These authors contributed equally to this work.)

  • Wei Zhu

    (Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY 11794, USA)

Abstract

COVID-19 spread dramatically across the world in the beginning of 2020. This paper presents a novel alert system that will detect abrupt changes in the COVID-19 or other pandemic incidence rate through the estimated time-varying reproduction number (Rt). We applied the system to detect abrupt changes in the COVID-19 pandemic incidence rates in thirteen world regions with eight in the US and five across the world. Subsequently, we also evaluated the system with the 2009 H1N1 pandemic in Hong Kong. Our system performs well in detecting both the abrupt increases and decreases. Users of the system can obtain accurate information on the changing trend of the pandemic to avoid being misled by low incidence numbers. The world may face other threatening pandemics in the future; therefore, it is crucial to have a reliable alert system to detect impending abrupt changes in the daily incidence rates. An added benefit of the system is its ability to detect the emergence of viral mutations, as different virus strains are likely to have different infection rates.

Suggested Citation

  • Jiecheng Song & Guanchao Tong & Wei Zhu, 2023. "A Detecting System for Abrupt Changes in Temporal Incidence Rate of COVID-19 and Other Pandemics," Stats, MDPI, vol. 6(3), pages 1-11, September.
    • Handle: RePEc:gam:jstats:v:6:y:2023:i:3:p:58-941:d:1242066
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-905X/6/3/58/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-905X/6/3/58/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Finn Stevenson & Kentaro Hayasi & Nicola Luigi Bragazzi & Jude Dzevela Kong & Ali Asgary & Benjamin Lieberman & Xifeng Ruan & Thuso Mathaha & Salah-Eddine Dahbi & Joshua Choma & Mary Kawonga & Mduduzi, 2021. "Development of an Early Alert System for an Additional Wave of COVID-19 Cases Using a Recurrent Neural Network with Long Short-Term Memory," IJERPH, MDPI, vol. 18(14), pages 1-14, July.
    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.

      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:jstats:v:6:y:2023:i:3:p:58-941:d:1242066. 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.