Time Varying Markov Process with Partially Observed Aggregate Data; An Application to Coronavirus
Methodology
2020-05-12 v1 Populations and Evolution
Applications
Abstract
A major difficulty in the analysis of propagation of the coronavirus is that many infected individuals show no symptoms of Covid-19. This implies a lack of information on the total counts of infected individuals and of recovered and immunized individuals. In this paper, we consider parametric time varying Markov processes of Coronavirus propagation and show how to estimate the model parameters and approximate the unobserved counts from daily numbers of infected and detected individuals and total daily death counts. This model-based approach is illustrated in an application to French data.
Cite
@article{arxiv.2005.04500,
title = {Time Varying Markov Process with Partially Observed Aggregate Data; An Application to Coronavirus},
author = {Christian Gourieroux and Joann Jasiak},
journal= {arXiv preprint arXiv:2005.04500},
year = {2020}
}
Comments
31 pages, 6 figures