ArticleRoyal Society open science2021
Estimating the state of the COVID-19 epidemic in France using a model with memory.
Article in Royal Society open science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Overcoming bias in estimating epidemiological parameters with realistic history-dependent disease spread dynamics.Nature communications · 2024Article
- An Epidemic Model with Infection Age and Vaccination Age Structure.Infectious disease reports · 2024Article
- Extending susceptible-infectious-recovered-susceptible epidemics to allow for gradual waning of immunity.Journal of the Royal Society, Interface · 2023Article
- Functional Law of Large Numbers and PDEs for Epidemic Models with Infection-Age Dependent Infectivity.Applied mathematics and optimization · 2023Article
- From individual-based epidemic models to McKendrick-von Foerster PDEs: a guide to modeling and inferring COVID-19 dynamics.Journal of mathematical biology · 2022Article
- Estimating data-driven coronavirus disease 2019 mitigation strategies for safe university reopening.Journal of the Royal Society, Interface · 2022Article
- The stochastic dynamics of early epidemics: probability of establishment, initial growth rate, and infection cluster size at first detection.Journal of the Royal Society, Interface · 2021Review
- A COVID-19 Epidemic Model Predicting the Effectiveness of Vaccination in the US.Infectious disease reports · 2021Article
- A two diffusion stochastic model for the spread of the new corona virus SARS-CoV-2.Chaos, solitons, and fractals · 2021Article
- Memory is key in capturing COVID-19 epidemiological dynamics.Epidemics · 2021Article
- Estimating the state of the COVID-19 epidemic in France using a model with memory.Royal Society open science · 2021Article
- A parsimonious approach for spatial transmission and heterogeneity in the COVID-19 propagation.Royal Society open science · 2020Article
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Authors and funding
3 authors.
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Abstract
In this paper, we use a deterministic epidemic model with memory to estimate the state of the COVID-19 epidemic in France, from early March until mid-December 2020. Our model is in the SEIR class, which means that when a susceptible individual (S) becomes infected, he/she is first exposed (E), i.e. not yet contagious. Then he/she becomes infectious (I) for a certain length of time, during which he/she may infect susceptible individuals around him/her, and finally becomes removed (R), that is, either immune or dead. The specificity of our model is that it assumes a very general probability distribution for the pair of exposed and infectious periods. The law of large numbers limit of such a model is a model with memory (the future evolution of the model depends not only upon its present state, but also upon its past). We present theoretical results linking the (unobserved) parameters of the model to various quantities which are more easily measured during the early stages of an epidemic. We then apply these results to estimate the state of the COVID-19 epidemic in France, using available information on the infection fatality ratio and on the distribution of the exposed and infectious periods. Using the hospital data published daily by Santé Publique France, we gather some information on the delay between infection and hospital admission, intensive care unit (ICU) admission and hospital deaths, and on the proportion of people who have been infected up to the end of 2020.
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