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

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...

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Detalles Bibliográficos
Autores principales: Gourieroux, C., Jasiak, J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V. 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7698670/
https://www.ncbi.nlm.nih.gov/pubmed/33281272
http://dx.doi.org/10.1016/j.jeconom.2020.09.007
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author Gourieroux, C.
Jasiak, J.
author_facet Gourieroux, C.
Jasiak, J.
author_sort Gourieroux, C.
collection PubMed
description 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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spelling pubmed-76986702020-12-01 Time varying Markov process with partially observed aggregate data: An application to coronavirus() Gourieroux, C. Jasiak, J. J Econom Article 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. Elsevier B.V. 2023-01 2020-11-28 /pmc/articles/PMC7698670/ /pubmed/33281272 http://dx.doi.org/10.1016/j.jeconom.2020.09.007 Text en © 2020 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Gourieroux, C.
Jasiak, J.
Time varying Markov process with partially observed aggregate data: An application to coronavirus()
title Time varying Markov process with partially observed aggregate data: An application to coronavirus()
title_full Time varying Markov process with partially observed aggregate data: An application to coronavirus()
title_fullStr Time varying Markov process with partially observed aggregate data: An application to coronavirus()
title_full_unstemmed Time varying Markov process with partially observed aggregate data: An application to coronavirus()
title_short Time varying Markov process with partially observed aggregate data: An application to coronavirus()
title_sort time varying markov process with partially observed aggregate data: an application to coronavirus()
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7698670/
https://www.ncbi.nlm.nih.gov/pubmed/33281272
http://dx.doi.org/10.1016/j.jeconom.2020.09.007
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