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Time varying Markov process with partially observed aggregate data: An application to coronavirus

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  • Gourieroux, C.
  • Jasiak, J.

Abstract

A major difficulty in the analysis of Covid-19 transmission is that many infected individuals are asymptomatic. For this reason, the total counts of infected individuals and of recovered immunized individuals are unknown, especially during the early phase of the epidemic. In this paper, we consider a parametric time varying Markov process of Coronavirus transmission and show how to estimate the model parameters and approximate the unobserved counts from daily data on infected and detected individuals and the total daily death counts. This model-based approach is illustrated in an application to French data, performed on April 6, 2020.

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  • Gourieroux, C. & Jasiak, J., 2023. "Time varying Markov process with partially observed aggregate data: An application to coronavirus," Journal of Econometrics, Elsevier, vol. 232(1), pages 35-51.
    • Handle: RePEc:eee:econom:v:232:y:2023:i:1:p:35-51
    DOI: 10.1016/j.jeconom.2020.09.007
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    1. Manski, Charles F. & Molinari, Francesca, 2021. "Estimating the COVID-19 infection rate: Anatomy of an inference problem," Journal of Econometrics, Elsevier, vol. 220(1), pages 181-192.
    2. McFadden, Daniel L., 1984. "Econometric analysis of qualitative response models," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 24, pages 1395-1457, Elsevier.
    3. Fernando Alvarez & David Argente, 2020. "A Simple Planning Problem for COVID-19 Lockdown," Working Papers 2020-34, Becker Friedman Institute for Research In Economics.
    4. Alexander Chudik & M. Hashem Pesaran & Alessandro Rebucci, 2020. "Voluntary and Mandatory Social Distancing: Evidence on COVID-19 Exposure Rates from Chinese Provinces and Selected Countries," Globalization Institute Working Papers 382, Federal Reserve Bank of Dallas.
    5. Hortaçsu, Ali & Liu, Jiarui & Schwieg, Timothy, 2021. "Estimating the fraction of unreported infections in epidemics with a known epicenter: An application to COVID-19," Journal of Econometrics, Elsevier, vol. 220(1), pages 106-129.
    6. Christian Gourieroux & Joann Jasiak, 2020. "Analysis of Virus Transmission: A Stochastic Transition Model Representation of Epidemiological Models," Annals of Economics and Statistics, GENES, issue 140, pages 1-26.
    7. Douglas J. Miller & George Judge, 2015. "Information Recovery in a Dynamic Statistical Markov Model," Econometrics, MDPI, vol. 3(2), pages 1-12, March.
    8. Dureau, Joseph & Kalogeropoulos, Konstantinos & Baguelin, Marc, 2013. "Capturing the time-varying drivers of an epidemic using stochastic dynamical systems," LSE Research Online Documents on Economics 41749, London School of Economics and Political Science, LSE Library.
    9. Ali Hortaçsu & Jiarui Liu & Timothy Schwieg, 2020. "Estimating the Fraction of Unreported Infections in Epidemics with a Known Epicenter: An Application to COVID-19," Working Papers 2020-37, Becker Friedman Institute for Research In Economics.
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    Cited by:

    1. Sean ELLIOTT & Christian GOURIEROUX, 2020. "Uncertainty on the Reproduction Ratio in the SIR Model," Working Papers 2020-31, Center for Research in Economics and Statistics.
    2. Mahapatra, D.P. & Triambak, S., 2022. "Towards predicting COVID-19 infection waves: A random-walk Monte Carlo simulation approach," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    3. Antoine Djogbenou & Christian Gourieroux & Joann Jasiak & Paul Rilstone & Maygol Bandehali, 2022. "Transition model for coronavirus management," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 55(S1), pages 665-704, February.
    4. Sean Elliott & Christian Gourieroux, 2020. "Uncertainty on the Reproduction Ratio in the SIR Model," Papers 2012.11542, arXiv.org.

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