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

A-Tier
Journal: Journal of Econometrics
Year: 2023
Volume: 232
Issue: 1
Pages: 35-51

Authors (2)

Gourieroux, C. (not in RePEc) Jasiak, J. (York University)

Score contribution per author:

2.011 = (α=2.01 / 2 authors) × 2.0x A-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

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.

Technical Details

RePEc Handle
repec:eee:econom:v:232:y:2023:i:1:p:35-51
Journal Field
Econometrics
Author Count
2
Added to Database
2026-01-25