IDEAS home Printed from https://ideas.repec.org/a/fip/fedcec/90731.html

Two Approaches to Predicting the Path of the COVID-19 Pandemic: Is One Better?

Author

Listed:
  • Ben R. Craig
  • Tom Phelan
  • Jan-Peter Siedlarek
  • Jared Steinberg

Abstract

We compare two types of models used to predict the spread of the coronavirus, both of which have been used by government officials and agencies. We describe the nature of the difference between the two approaches and their advantages and limitations. We compare examples of each type of model—the University of Washington IHME or “Murray” model, which follows a curve-fitting approach, and the Ohio State University model, which follows a structural approach.

Suggested Citation

  • Ben R. Craig & Tom Phelan & Jan-Peter Siedlarek & Jared Steinberg, 2021. "Two Approaches to Predicting the Path of the COVID-19 Pandemic: Is One Better?," Economic Commentary, Federal Reserve Bank of Cleveland, vol. 2021(10), pages 1-8, April.
    • Handle: RePEc:fip:fedcec:90731
    DOI: 10.26509/frbc-ec-202110
    as

    Download full text from publisher

    File URL: https://doi.org/10.26509/frbc-ec-202110
    File Function: Full Text
    Download Restriction: no
    File URL: https://libkey.io/10.26509/frbc-ec-202110?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
    ---><---

    References listed on IDEAS

    as
    1. Farboodi, Maryam & Jarosch, Gregor & Shimer, Robert, 2021. "Internal and external effects of social distancing in a pandemic," Journal of Economic Theory, Elsevier, vol. 196(C).
    2. Thomas House & Matt J Keeling, 2010. "The Impact of Contact Tracing in Clustered Populations," PLOS Computational Biology, Public Library of Science, vol. 6(3), pages 1-9, March.
    3. Carol Y. Lin, 2008. "Modeling Infectious Diseases in Humans and Animals by KEELING, M. J. and ROHANI, P," Biometrics, The International Biometric Society, vol. 64(3), pages 993-993, September.
    4. Wang, Peipei & Zheng, Xinqi & Li, Jiayang & Zhu, Bangren, 2020. "Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    5. Ben R. Craig & Tom Phelan & Jan-Peter Siedlarek & Jared Steinberg, 2020. "Improving Epidemic Modeling with Networks," Economic Commentary, Federal Reserve Bank of Cleveland, vol. 2020(23), pages 1-8, September.
    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. Carnehl, Christoph & Fukuda, Satoshi & Kos, Nenad, 2023. "Epidemics with behavior," Journal of Economic Theory, Elsevier, vol. 207(C).
    2. Ben R. Craig & Tom Phelan & Jan-Peter Siedlarek, 2021. "Modeling Behavioral Responses to COVID-19," Economic Commentary, Federal Reserve Bank of Cleveland, vol. 2021(05), pages 1-6, March.
    3. Nemati Fard, Lorenzo Amir & Bisin, Alberto & Starnini, Michele & Tizzoni, Michele, 2025. "Modeling adaptive forward-looking behavior in epidemics on networks," Journal of Economic Behavior & Organization, Elsevier, vol. 232(C).
    4. Ben R. Craig & Tom Phelan & Jan-Peter Siedlarek & Jared Steinberg, 2020. "Improving Epidemic Modeling with Networks," Economic Commentary, Federal Reserve Bank of Cleveland, vol. 2020(23), pages 1-8, September.
    5. Boucekkine, Raouf & Chakraborty, Shankha & Goenka, Aditya & Liu, Lin, 2024. "Economic epidemiological modelling: A progress report," Journal of Mathematical Economics, Elsevier, vol. 113(C).
    6. Vinicius Albani & Matheus Grasselli & Weijie Pang & Jorge P. Zubelli, 2022. "The Interplay between COVID-19 and the Economy in Canada," JRFM, MDPI, vol. 15(10), pages 1-26, October.
    7. Wang, Peipei & Zheng, Xinqi & Ai, Gang & Liu, Dongya & Zhu, Bangren, 2020. "Time series prediction for the epidemic trends of COVID-19 using the improved LSTM deep learning method: Case studies in Russia, Peru and Iran," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    8. Ichino, Andrea & Favero, Carlo A. & Rustichini, Aldo, 2020. "Restarting the economy while saving lives under Covid-19," CEPR Discussion Papers 14664, C.E.P.R. Discussion Papers.
    9. Tyagi, Swati & Martha, Subash C. & Abbas, Syed & Debbouche, Amar, 2021. "Mathematical modeling and analysis for controlling the spread of infectious diseases," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    10. Kimberly M. Thompson, 2016. "Evolution and Use of Dynamic Transmission Models for Measles and Rubella Risk and Policy Analysis," Risk Analysis, John Wiley & Sons, vol. 36(7), pages 1383-1403, July.
    11. McKinley Trevelyan & Cook Alex R & Deardon Robert, 2009. "Inference in Epidemic Models without Likelihoods," The International Journal of Biostatistics, De Gruyter, vol. 5(1), pages 1-40, July.
    12. Essam A. Rashed & Akimasa Hirata, 2021. "One-Year Lesson: Machine Learning Prediction of COVID-19 Positive Cases with Meteorological Data and Mobility Estimate in Japan," IJERPH, MDPI, vol. 18(11), pages 1-16, May.
    13. Wei Zhong, 2017. "Simulating influenza pandemic dynamics with public risk communication and individual responsive behavior," Computational and Mathematical Organization Theory, Springer, vol. 23(4), pages 475-495, December.
    14. De Martino, Giuseppe & Spina, Serena, 2015. "Exploiting the time-dynamics of news diffusion on the Internet through a generalized Susceptible–Infected model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 634-644.
    15. Caulkins, J.P. & Grass, D. & Feichtinger, G. & Hartl, R.F. & Kort, P.M. & Kuhn, M. & Prskawetz, A. & Sanchez-Romero, M. & Seidl, A. & Wrzaczek, S., 2023. "The hammer and the jab: Are COVID-19 lockdowns and vaccinations complements or substitutes?," European Journal of Operational Research, Elsevier, vol. 311(1), pages 233-250.
    16. Martin-Lapoirie, Dylan & McColl, Kathleen & Gallopel-Morvan, Karine & Arwidson, Pierre & Raude, Jocelyn, 2024. "Health protective behaviours during the COVID-19 pandemic: Risk adaptation or habituation?," Social Science & Medicine, Elsevier, vol. 342(C).
    17. Matthew Goodkin-Gold & Michael Kremer & Christopher M. Snyder & Heidi Williams, 2024. "Optimal Vaccine Subsidies for Epidemic Diseases," The Review of Economics and Statistics, MIT Press, vol. 106(4), pages 895-909, July.
    18. Gabrick, Enrique C. & Sayari, Elaheh & Protachevicz, Paulo R. & Szezech, José D. & Iarosz, Kelly C. & de Souza, Silvio L.T. & Almeida, Alexandre C.L. & Viana, Ricardo L. & Caldas, Iberê L. & Batista, , 2023. "Unpredictability in seasonal infectious diseases spread," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    19. Hasin Md. Muhtasim Taqi & Humaira Nafisa Ahmed & Sumit Paul & Maryam Garshasbi & Syed Mithun Ali & Golam Kabir & Sanjoy Kumar Paul, 2020. "Strategies to Manage the Impacts of the COVID-19 Pandemic in the Supply Chain: Implications for Improving Economic and Social Sustainability," Sustainability, MDPI, vol. 12(22), pages 1-25, November.
    20. Joshua V Ross & Thomas House & Matt J Keeling, 2010. "Calculation of Disease Dynamics in a Population of Households," PLOS ONE, Public Library of Science, vol. 5(3), pages 1-9, March.

    More about this item

    Keywords

    ;

    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:fip:fedcec:90731. 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: 4D Library (email available below). General contact details of provider: https://edirc.repec.org/data/frbclus.html .

    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.