IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v7y2020i1d10.1057_s41599-020-00553-4.html
   My bibliography  Save this article

Validity and usefulness of COVID-19 models

Author

Listed:
  • Sibel Eker

    (International Institute for Applied Systems Analysis (IIASA))

Abstract

Mathematical models have become central to the public and policy debate about the recent COVID-19 pandemic. On the one hand, they provide guidance to policy-makers about the development of the epidemic and healthcare demand overtime; on the other hand, they are heavily criticized for their lack of credibility. This commentary reflects on three such models from a validity and usefulness perspective. Specifically, it discusses the complexity, validation, and communication of models informing the government decisions in the UK, US and Austria, and concludes that, although these models are useful in many ways, they currently lack a thorough validation and a clear communication of their uncertainties. Therefore, prediction claims of these models should be taken cautiously, and their merits on scenario analysis should be the basis for decision-making. The lessons that can be learned from the COVID models in terms of the communication of uncertainties and assumptions can guide the use of quantitative models in other policy-making areas.

Suggested Citation

  • Sibel Eker, 2020. "Validity and usefulness of COVID-19 models," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-5, December.
    • Handle: RePEc:pal:palcom:v:7:y:2020:i:1:d:10.1057_s41599-020-00553-4
    DOI: 10.1057/s41599-020-00553-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-020-00553-4
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/s41599-020-00553-4?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. James S. Hodges, 1991. "Six (Or So) Things You Can Do with a Bad Model," Operations Research, INFORMS, vol. 39(3), pages 355-365, June.
    2. Andrea Saltelli, 2019. "A short comment on statistical versus mathematical modelling," Nature Communications, Nature, vol. 10(1), pages 1-3, December.
    3. Sibel Eker & Elena Rovenskaya & Michael Obersteiner & Simon Langan, 2018. "Practice and perspectives in the validation of resource management models," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    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. Alba Antequera & Daeria O. Lawson & Stephen G. Noorduyn & Omar Dewidar & Marc Avey & Zulfiqar A. Bhutta & Catherine Chamberlain & Holly Ellingwood & Damian Francis & Sarah Funnell & Elizabeth Ghogomu , 2021. "Improving Social Justice in COVID-19 Health Research: Interim Guidelines for Reporting Health Equity in Observational Studies," IJERPH, MDPI, vol. 18(17), pages 1-12, September.
    2. Jessica Weinkle, 2022. "An evaluation of North Carolina science advice on COVID-19 pandemic response," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-16, December.
    3. Noor Alkhateeb & Farag Sallabi & Saad Harous & Mamoun Awad, 2022. "A Study on Predicting the Outbreak of COVID-19 in the United Arab Emirates: A Monte Carlo Simulation Approach," Mathematics, MDPI, vol. 10(23), pages 1-17, November.
    4. Shirley Gee Hoon Tang & Muhamad Haziq Hasnul Hadi & Siti Rosilah Arsad & Pin Jern Ker & Santhi Ramanathan & Nayli Aliah Mohd Afandi & Madihah Mohd Afzal & Mei Wyin Yaw & Prajindra Sankar Krishnan & Ch, 2022. "Prerequisite for COVID-19 Prediction: A Review on Factors Affecting the Infection Rate," IJERPH, MDPI, vol. 19(20), pages 1-38, October.
    5. Albert Zeyer, 2022. "Teaching Two-Eyed Seeing in Education for Sustainable Development: Inspirations from the Science|Environment|Health Pedagogy in Pandemic Times," Sustainability, MDPI, vol. 14(10), pages 1-12, May.
    6. Rastko Jovanović & Miloš Davidović & Ivan Lazović & Maja Jovanović & Milena Jovašević-Stojanović, 2021. "Modelling Voluntary General Population Vaccination Strategies during COVID-19 Outbreak: Influence of Disease Prevalence," IJERPH, MDPI, vol. 18(12), pages 1-18, June.
    7. Gabriel Recchia & Alexandra L J Freeman & David Spiegelhalter, 2021. "How well did experts and laypeople forecast the size of the COVID-19 pandemic?," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-16, May.

    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. Andrea Saltelli & Arnald Puy, 2023. "What can mathematical modelling contribute to a sociology of quantification?," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-8, December.
    2. Siddharth Sareen & Andrea Saltelli & Kjetil Rommetveit, 2020. "Ethics of quantification: illumination, obfuscation and performative legitimation," Palgrave Communications, Palgrave Macmillan, vol. 6(1), pages 1-5, December.
    3. Jack P. C. Kleijnen & Susan M. Sanchez & Thomas W. Lucas & Thomas M. Cioppa, 2005. "State-of-the-Art Review: A User’s Guide to the Brave New World of Designing Simulation Experiments," INFORMS Journal on Computing, INFORMS, vol. 17(3), pages 263-289, August.
    4. Robert Huber & Hang Xiong & Kevin Keller & Robert Finger, 2022. "Bridging behavioural factors and standard bio‐economic modelling in an agent‐based modelling framework," Journal of Agricultural Economics, Wiley Blackwell, vol. 73(1), pages 35-63, February.
    5. Wen, Xin & Heinisch, Verena & Müller, Jonas & Sasse, Jan-Philipp & Trutnevyte, Evelina, 2023. "Comparison of statistical and optimization models for projecting future PV installations at a sub-national scale," Energy, Elsevier, vol. 285(C).
    6. Katzin, David & van Henten, Eldert J. & van Mourik, Simon, 2022. "Process-based greenhouse climate models: Genealogy, current status, and future directions," Agricultural Systems, Elsevier, vol. 198(C).
    7. Frederic H. Murphy, 2005. "ASP, The Art and Science of Practice: Elements of a Theory of the Practice of Operations Research: Expertise in Practice," Interfaces, INFORMS, vol. 35(4), pages 313-322, August.
    8. Michael Pidd, 1999. "Just Modeling Through: A Rough Guide to Modeling," Interfaces, INFORMS, vol. 29(2), pages 118-132, April.
    9. Juana Castro & Stefan Drews & Filippos Exadaktylos & Joël Foramitti & Franziska Klein & Théo Konc & Ivan Savin & Jeroen van den Bergh, 2020. "A review of agent‐based modeling of climate‐energy policy," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 11(4), July.
    10. Andrea Saltelli & Monica Fiore, 2020. "From sociology of quantification to ethics of quantification," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-8, December.
    11. Marjolijn Haasnoot & Hans Middelkoop & Astrid Offermans & Eelco Beek & Willem Deursen, 2012. "Exploring pathways for sustainable water management in river deltas in a changing environment," Climatic Change, Springer, vol. 115(3), pages 795-819, December.
    12. Kleijnen, Jack P. C., 1995. "Verification and validation of simulation models," European Journal of Operational Research, Elsevier, vol. 82(1), pages 145-162, April.
    13. Btool H. Mohamed & Ibrahim Ari & Mohammed bin Saleh Al-Sada & Muammer Koç, 2021. "Strategizing Human Development for a Country in Transition from a Resource-Based to a Knowledge-Based Economy," Sustainability, MDPI, vol. 13(24), pages 1-27, December.
    14. Samuele Lo Piano, 2020. "Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-7, December.
    15. Charlie Wilson & Céline Guivarch & Elmar Kriegler & Bas Ruijven & Detlef P. Vuuren & Volker Krey & Valeria Jana Schwanitz & Erica L. Thompson, 2021. "Evaluating process-based integrated assessment models of climate change mitigation," Climatic Change, Springer, vol. 166(1), pages 1-22, May.
    16. Brian L. Morgan & Harrison C. Schramm & Jerry R. Smith, Jr. & Thomas W. Lucas & Mary L. McDonald & Paul J. Sánchez & Susan M. Sanchez & Stephen C. Upton, 2018. "Improving U.S. Navy Campaign Analyses with Big Data," Interfaces, INFORMS, vol. 48(2), pages 130-146, April.
    17. Moallemi, Enayat A. & Elsawah, Sondoss & Ryan, Michael J., 2020. "Strengthening ‘good’ modelling practices in robust decision support: A reporting guideline for combining multiple model-based methods," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 175(C), pages 3-24.
    18. Harper, Alison & Mustafee, Navonil & Yearworth, Mike, 2021. "Facets of trust in simulation studies," European Journal of Operational Research, Elsevier, vol. 289(1), pages 197-213.
    19. Marta Kuc-Czarnecka & Samuele Lo Piano & Andrea Saltelli, 2020. "Quantitative Storytelling in the Making of a Composite Indicator," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 149(3), pages 775-802, June.
    20. Walker, Warren E., 2009. "Does the best practice of rational-style model-based policy analysis already include ethical considerations?," Omega, Elsevier, vol. 37(6), pages 1051-1062, December.

    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:pal:palcom:v:7:y:2020:i:1:d:10.1057_s41599-020-00553-4. 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: https://www.nature.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.