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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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    Cited by:

    1. KOUAKOU, Thiédjé Gaudens-Omer, 2025. "Coûts de la Covid-19, Tropicalisation de modèle épidémiologique et Arbitrage santé-économie en Afrique [Costs of Covid-19, Tropicalization of the epidemiological model and Health-economic trade-off," MPRA Paper 123467, University Library of Munich, Germany.
    2. Fu, Xinjie & Wang, JinRong, 2024. "Dynamic behaviors and non-instantaneous impulsive vaccination of an SAIQR model on complex networks," Applied Mathematics and Computation, Elsevier, vol. 465(C).
    3. 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.
    4. Chengliang Wang & Sohaib Mustafa, 2023. "A data-driven Markov process for infectious disease transmission," PLOS ONE, Public Library of Science, vol. 18(8), pages 1-20, August.
    5. Otilia Boldea & Adriana Cornea-Madeira & João Madeira, 2023. "Disentangling the effect of measures, variants, and vaccines on SARS-CoV-2 infections in England: a dynamic intensity model," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 444-466.
    6. 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).
    7. 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.
    8. Sean Elliott & Christian Gourieroux, 2020. "Uncertainty on the Reproduction Ratio in the SIR Model," Papers 2012.11542, arXiv.org.

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