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

C. Gourieroux; J. Jasiak

doi:10.1016/j.jeconom.2020.09.007

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

C. Gourieroux, J. Jasiak

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	35-51
Number of pages	17
Journal	Journal of Econometrics
Volume	232
Issue number	1
DOIs	https://doi.org/10.1016/j.jeconom.2020.09.007
Publication status	Published - 1 Jan 2023
Externally published	Yes

Keywords

Coronavirus
Estimating equations
Infection rate
Information recovery
Markov process
Partial observability
SIR model

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10.1016/j.jeconom.2020.09.007

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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 = "Coronavirus, Estimating equations, Infection rate, Information recovery, Markov process, Partial observability, SIR model",

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AB - 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.

KW - Coronavirus

KW - Estimating equations

KW - Infection rate

KW - Information recovery

KW - Markov process

KW - Partial observability

KW - SIR model

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DO - 10.1016/j.jeconom.2020.09.007

M3 - Article

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SP - 35

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JO - Journal of Econometrics

JF - Journal of Econometrics

IS - 1

ER -

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

Abstract

Keywords

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