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Treffer: A mechanistic and data-driven reconstruction of the time-varying reproduction number: Application to the COVID-19 epidemic.

Title:
A mechanistic and data-driven reconstruction of the time-varying reproduction number: Application to the COVID-19 epidemic.
Authors:
Cazelles B; Sorbonne Université, UMMISCO, Paris, France.; INRAE, Université Paris-Saclay, MaIAGE, Jouy-en-Josas, France.; Eco-Evolution Mathématique, IBENS, UMR 8197, CNRS, Ecole Normale Supérieure, Paris, France., Champagne C; Swiss Tropical and Public Health Institute, Basel, Switzerland.; Universty of Basel, Basel, Switzerland., Nguyen-Van-Yen B; Eco-Evolution Mathématique, IBENS, UMR 8197, CNRS, Ecole Normale Supérieure, Paris, France.; Institut Pasteur, Unité de Génétique Fonctionnelle des Maladies Infectieuses, Paris, France., Comiskey C; School of Nursing and Midwifery, Trinity College Dublin, The University of Dublin, Dublin, Ireland., Vergu E; INRAE, Université Paris-Saclay, MaIAGE, Jouy-en-Josas, France., Roche B; MIVEGEC, IRD, CNRS and Université de Montpellier, Montpellier, France.
Source:
PLoS computational biology [PLoS Comput Biol] 2021 Jul 26; Vol. 17 (7), pp. e1009211. Date of Electronic Publication: 2021 Jul 26 (Print Publication: 2021).
Publication Type:
Journal Article; Research Support, Non-U.S. Gov't
Language:
English
Journal Info:
Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7358 (Electronic) Linking ISSN: 1553734X NLM ISO Abbreviation: PLoS Comput Biol Subsets: MEDLINE
Imprint Name(s):
Original Publication: San Francisco, CA : Public Library of Science, [2005]-
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Entry Date(s):
Date Created: 20210726 Date Completed: 20210812 Latest Revision: 20240813
Update Code:
20250114
PubMed Central ID:
PMC8341713
DOI:
10.1371/journal.pcbi.1009211
PMID:
34310593
Database:
MEDLINE

Weitere Informationen

The effective reproduction number Reff is a critical epidemiological parameter that characterizes the transmissibility of a pathogen. However, this parameter is difficult to estimate in the presence of silent transmission and/or significant temporal variation in case reporting. This variation can occur due to the lack of timely or appropriate testing, public health interventions and/or changes in human behavior during an epidemic. This is exactly the situation we are confronted with during this COVID-19 pandemic. In this work, we propose to estimate Reff for the SARS-CoV-2 (the etiological agent of the COVID-19), based on a model of its propagation considering a time-varying transmission rate. This rate is modeled by a Brownian diffusion process embedded in a stochastic model. The model is then fitted by Bayesian inference (particle Markov Chain Monte Carlo method) using multiple well-documented hospital datasets from several regions in France and in Ireland. This mechanistic modeling framework enables us to reconstruct the temporal evolution of the transmission rate of the COVID-19 based only on the available data. Except for the specific model structure, it is non-specifically assumed that the transmission rate follows a basic stochastic process constrained by the observations. This approach allows us to follow both the course of the COVID-19 epidemic and the temporal evolution of its Reff(t). Besides, it allows to assess and to interpret the evolution of transmission with respect to the mitigation strategies implemented to control the epidemic waves in France and in Ireland. We can thus estimate a reduction of more than 80% for the first wave in all the studied regions but a smaller reduction for the second wave when the epidemic was less active, around 45% in France but just 20% in Ireland. For the third wave in Ireland the reduction was again significant (>70%).

The authors have declared that no competing interests exist.