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How To Go Viral: A COVID-19 Model with Endogenously Time-Varying Parameters

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Abstract

This paper estimates a panel model with endogenously time-varying parameters for COVID-19 cases and deaths in U.S. states. The functional form for infections incorporates important features of epidemiological models but is flexibly parameterized to capture different trajectories of the pandemic. Daily deaths are modeled as a spike-and-slab regression on lagged cases. The paper's Bayesian estimation reveals that social distancing and testing have significant effects on the parameters. For example, a 10 percentage point increase in the positive test rate is associated with a 2 percentage point increase in the death rate among reported cases. The model forecasts perform well, even relative to models from epidemiology and statistics.

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  • Paul Ho & Thomas A. Lubik & Christian Matthes, 2020. "How To Go Viral: A COVID-19 Model with Endogenously Time-Varying Parameters," Working Paper 20-10, Federal Reserve Bank of Richmond.
    • Handle: RePEc:fip:fedrwp:88807
    DOI: 10.21144/wp20-10
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    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 > Statistical Modelling

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    Cited by:

    1. Baum, Christopher F. & García-Suaza, Andrés & Henry, Miguel & Otero, Jesús, 2025. "Drivers of COVID-19 in U.S. counties: A wave-level analysis," Economics & Human Biology, Elsevier, vol. 58(C).
    2. Difang Huang & Ying Liang & Boyao Wu & Yanyi Ye, 2024. "Estimating the Impact of Social Distance Policy in Mitigating COVID-19 Spread with Factor-Based Imputation Approach," Papers 2405.12180, arXiv.org.
    3. Bognanni, Mark & Hanley, Doug & Kolliner, Daniel & Mitman, Kurt, 2020. "Economics and Epidemics: Evidence from an Estimated Spatial Econ-SIR Model," IZA Discussion Papers 13797, IZA Network @ LISER.
    4. Difang Huang & Ying Liang & Boyao Wu & Yanyi Ye, 2025. "Estimating the impact of social distance policy in mitigating COVID-19 spread with factor-based imputation approach," Empirical Economics, Springer, vol. 68(2), pages 585-601, February.
    5. Paul Ho, 2023. "Forecasting in the absence of precedent," Journal of Economic Surveys, Wiley Blackwell, vol. 37(3), pages 1033-1058, July.
    6. Andrei Zubarev & Maria Kirillova, 2022. "Modeling COVID-19 spread in the Russian Federation using global VAR approach," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 65, pages 117-138.

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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