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

C. Gourieroux and Joann Jasiak

Journal of Econometrics, 2023, vol. 232, issue 1, 35-51

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.

Keywords: Markov process; Partial observability; Information recovery; Estimating equations; SIR model; Coronavirus; Infection rate (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Working Paper: Time Varying Markov Process with Partially Observed Aggregate Data; An Application to Coronavirus (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:232:y:2023:i:1:p:35-51

DOI: 10.1016/j.jeconom.2020.09.007

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