ArticleJournal of econometrics2023
Time varying Markov process with partially observed aggregate data: An application to coronavirus.
Article in Journal of econometrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Article
- Towards predicting COVID-19 infection waves: A random-walk Monte Carlo simulation approach.Chaos, solitons, and fractals · 2022Article
- Transition model for coronavirus management.The Canadian journal of economics. Revue canadienne d'economique · 2022Article
- A new logistic growth model applied to COVID-19 fatality data.Epidemics · 2021Article
- Estimated reproduction ratios in the SIR model.The Canadian journal of statistics = Revue canadienne de statistique · 2021Article
- Generalized logistic growth modeling of the COVID-19 outbreak: comparing the dynamics in the 29 provinces in China and in the rest of the world.Nonlinear dynamics · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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