IDEAS home Printed from https://ideas.repec.org/a/wsi/apjorx/v37y2020i03ns0217595920500281.html
   My bibliography  Save this article

Predictive Modeling for Epidemic Outbreaks: A New Approach and COVID-19 Case Study

Author

Listed:
  • Jian Chen

    (CreditWise Technology Co., Ltd., Lujiazui Road No. 166, Mirae Asset Tower Building, 16 Block D, Shanghai 200120, China)

  • Michael C. Fu

    (Smith Chair of Management Science Department of Decision, Operations and Information Technologies, Robert H. Smith School of Business & Institute for Systems, Research Van Munching Hall University of Maryland, College Park, Maryland 20742, USA)

  • Wenhong Zhang

    (Department of Infectious Diseases, Huashan Hospital, Fudan University, 12 Wulumuqi Zhong Road, Shanghai 200040, China)

  • Junhua Zheng

    (Shanghai General Hospital, School of Medicine in Shanghai, Jiao Tong University, 86 Wujing Road, Shanghai 200080, China)

Abstract

Since the onset of the COVID-19 outbreak in Wuhan, China, numerous forecasting models have been proposed to project the trajectory of coronavirus infection cases. Most of these forecasts are based on epidemiology models that utilize deterministic differential equations and have resulted in widely varying predictions. We propose a new discrete-time Markov chain model that directly incorporates stochastic behavior and for which parameter estimation is straightforward from available data. Using such data from China’s Hubei province (for which Wuhan is the provincial capital city and which accounted for approximately 82% of the total reported COVID-19 cases in the entire country), the model is shown to be flexible, robust, and accurate. As a result, it has been adopted by the first Shanghai assistance medical team in Wuhan’s Jinyintan Hospital, which was the first designated hospital to take COVID-19 patients in the world. The forecast has been used for preparing medical staff, intensive care unit (ICU) beds, ventilators, and other critical care medical resources and for supporting real-time medical management decisions.

Suggested Citation

  • Jian Chen & Michael C. Fu & Wenhong Zhang & Junhua Zheng, 2020. "Predictive Modeling for Epidemic Outbreaks: A New Approach and COVID-19 Case Study," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 37(03), pages 1-21, May.
    • Handle: RePEc:wsi:apjorx:v:37:y:2020:i:03:n:s0217595920500281
    DOI: 10.1142/S0217595920500281
    as

    Download full text from publisher

    File URL: https://www.worldscientific.com/doi/abs/10.1142/S0217595920500281
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0217595920500281?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.

    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:wsi:apjorx:v:37:y:2020:i:03:n:s0217595920500281. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/apjor/apjor.shtml .

    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.