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Replicating and Projecting the Path of COVID-19 with a Model-Implied Reproduction Number

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Abstract

We fit a simple epidemiology model to daily data on the number of currently-infected cases of COVID-19 in China, Italy, the United States, and Brazil. These four countries can be viewed as representing different stages, from late to early, of a COVID-19 epidemic cycle. We solve for a model-implied effective reproduction number Rt each day so that the model closely replicates the daily number of currently infected cases in each country. Using the model-implied time series of Rt, we construct a smoothed version of the in-sample trajectory which is used to project the future evolution of Rt and the out-of-sample number of infected and closed cases (recovered or deceased). For the United States, the number of infected cases is projected to peak around July 19. For Brazil, the number of infected cases is projected to peak around July 24. We show that declines in measures of population mobility tend to precede declines in the model-implied reproduction numbers for each country. This pattern suggests that mandatory and voluntary stay-at-home behavior and social distancing in recent months has helped to reduce the effective reproduction number and reduce the spread of COVID-19.

Suggested Citation

  • Shelby R. Buckman & Reuven Glick & Kevin J. Lansing & Nicolas Petrosky-Nadeau & Lily Seitelman, 2020. "Replicating and Projecting the Path of COVID-19 with a Model-Implied Reproduction Number," Working Paper Series 2020-24, Federal Reserve Bank of San Francisco.
    • Handle: RePEc:fip:fedfwp:88335
    DOI: 10.24148/wp2020-24
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    RePEc Biblio mentions

    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Covid-19 > Modelling

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    Cited by:

    1. Galasso, Joseph & Cao, Duy M. & Hochberg, Robert, 2022. "A random forest model for forecasting regional COVID-19 cases utilizing reproduction number estimates and demographic data," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    2. Gilgur, Alexander & Ramirez-Marquez, Jose Emmanuel, 2022. "Modeling mobility, risk, and pandemic severity during the first year of COVID," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    3. Ho, Paul & Lubik, Thomas A. & Matthes, Christian, 2023. "How to go viral: A COVID-19 model with endogenously time-varying parameters," Journal of Econometrics, Elsevier, vol. 232(1), pages 70-86.

    More about this item

    Keywords

    Coronavirus; Reproduction number; Epidemics; SEIR Model; COVID-19;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior

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