Journal Article

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

Vergu, Elisabeta, Nguyen-Van-Yen, Benjamin, Institut de biologie de l'ENS Paris (IBENS) ; Département de Biologie - ENS-PSL (IBENS) ; École normale supérieure - Paris (ENS-PSL) ; Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL) ; Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université de Bâle = University of Basel = Basel Universität (Unibas), BC and BR are partially supported by a grant ANR Flash Covid-19 from the “Agence Nationale de la Recherche” (DigEpi), Champagne, Clara, Trinity College Dublin, Roche, Benjamin, Comiskey, Cath

2021

Overview

The effective reproduction number R eff 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 R eff 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 R eff (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%).

• Publisher: PLOS,CCSD,Public Library of Science,Public Library of Science (PLoS)
• Subject: Algorithms / Asymptomatic / Basic Reproduction Number - statistics & numerical data / Bayes Theorem / Bayesian analysis / Biology and Life Sciences / Computational Biology / Coronaviruses / COVID-19 / COVID-19 - epidemiology / COVID-19 - transmission / Diffusion rate / Disease control / Disease transmission / Emerging diseases / Epidemics / Epidemics - statistics & numerical data / Epidemiology / Estimates / Etiology / Evolution / France - epidemiology / Health promotion / Human health and pathology / Humans / Infections / Infectious diseases / Ireland - epidemiology / Life Sciences / Markov Chains / Mathematical models / Medicine and Health Sciences / Mitigation / Models, Statistical / Monte Carlo Method / Monte Carlo simulation / Pandemics / Pandemics - statistics & numerical data / Parameter estimation / Pathogens / People and places / Population / Public health / Reproduction / Santé publique et épidémiologie / SARS-CoV-2 / Seroepidemiologic Studies / Severe acute respiratory syndrome coronavirus 2 / Statistical inference / Stochastic models / Stochastic Processes / Stochasticity / Temporal variations / Time Factors / Viral diseases
• ISBN: 0006852145000