Time Varying Markov Process with Partially Observed Aggregate Data; An\n Application to Coronavirus

2020/05/09 by Christian Gouriéroux, Gourieroux, Christian, Joann Jasiak +1
Computer Science · Mathematics · #62M10 #62P10 #65C20 #Applications (stat.AP) #Bayesian Methods and Mixture Models #COVID-19 epidemiological studies #FOS: Biological sciences #FOS: Computer and information sciences #Methodology (stat.ME) #Populations and Evolution (q-bio.PE) #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.2005.04500

openalex publication_date 2020/05/09 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28

Abstract

A major difficulty in the analysis of propagation of the coronavirus is that\nmany infected individuals show no symptoms of Covid-19. This implies a lack of\ninformation on the total counts of infected individuals and of recovered and\nimmunized individuals. In this paper, we consider parametric time varying\nMarkov processes of Coronavirus propagation and show how to estimate the model\nparameters and approximate the unobserved counts from daily numbers of infected\nand detected individuals and total daily death counts. This model-based\napproach is illustrated in an application to French data.\n

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