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A major difficulty in the analysis of propagation of the coronavirus is thatmany infected individuals show no symptoms of Covid-19. This implies a lack ofinformation on the total counts of infected individuals and of recovered andimmunized individuals. In this paper, we consider parametric time varyingMarkov processes of Coronavirus propagation and show how to estimate the modelparameters and approximate the unobserved counts from daily numbers of infectedand detected individuals and total daily death counts. This model-basedapproach is illustrated in an application to French data.

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