IDEAS home Printed from https://ideas.repec.org/a/taf/jriskr/v25y2022i10p1223-1238.html
   My bibliography  Save this article

COVID-19 has illuminated the need for clearer AI-based risk management strategies

Author

Listed:
  • Tessa Swanson
  • Jon Zelner
  • Seth Guikema

Abstract

Machine learning methods offer opportunities improve pandemic response and risk management by supplementing mechanistic modeling approaches to pandemic planning and response based on diverse sources of data at every level from the local to global scale. However, such solutions rely on the availability of appropriate data as well as communication and dissemination of that data to develop tools and guidance for decision making. A lack of consistency in the reporting and availability of disaggregated, detailed data on COVID-19 in the US has limited the application of artificial intelligence methods and the effectiveness of those methods for projecting the spread and subsequent impacts of this disease in communities. These limitations are missed opportunities for AI methods to make a positive contribution, and they introduce the possibility of inappropriate use of AI methods when not acknowledged. Going forward, governing bodies should develop data collection and sharing standards in collaboration with AI researchers and industry experts to facilitate preparedness for pandemics and other disasters in the future.

Suggested Citation

  • Tessa Swanson & Jon Zelner & Seth Guikema, 2022. "COVID-19 has illuminated the need for clearer AI-based risk management strategies," Journal of Risk Research, Taylor & Francis Journals, vol. 25(10), pages 1223-1238, October.
    • Handle: RePEc:taf:jriskr:v:25:y:2022:i:10:p:1223-1238
    DOI: 10.1080/13669877.2022.2077411
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/13669877.2022.2077411
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/13669877.2022.2077411?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.

    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:taf:jriskr:v:25:y:2022:i:10:p:1223-1238. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RJRR20 .

    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.