In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 17, No. 7 ( 2021-7-26), p. e1009211-
Abstract:
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%).
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1009211
DOI:
10.1371/journal.pcbi.1009211.g001
DOI:
10.1371/journal.pcbi.1009211.g002
DOI:
10.1371/journal.pcbi.1009211.g003
DOI:
10.1371/journal.pcbi.1009211.g004
DOI:
10.1371/journal.pcbi.1009211.g005
DOI:
10.1371/journal.pcbi.1009211.t001
DOI:
10.1371/journal.pcbi.1009211.t002
DOI:
10.1371/journal.pcbi.1009211.s001
DOI:
10.1371/journal.pcbi.1009211.s002
DOI:
10.1371/journal.pcbi.1009211.s003
DOI:
10.1371/journal.pcbi.1009211.s004
DOI:
10.1371/journal.pcbi.1009211.s005
DOI:
10.1371/journal.pcbi.1009211.s006
DOI:
10.1371/journal.pcbi.1009211.s007
DOI:
10.1371/journal.pcbi.1009211.s008
DOI:
10.1371/journal.pcbi.1009211.s009
DOI:
10.1371/journal.pcbi.1009211.s010
DOI:
10.1371/journal.pcbi.1009211.s011
DOI:
10.1371/journal.pcbi.1009211.s012
DOI:
10.1371/journal.pcbi.1009211.s013
DOI:
10.1371/journal.pcbi.1009211.s014
DOI:
10.1371/journal.pcbi.1009211.s015
DOI:
10.1371/journal.pcbi.1009211.s016
DOI:
10.1371/journal.pcbi.1009211.s017
DOI:
10.1371/journal.pcbi.1009211.s018
DOI:
10.1371/journal.pcbi.1009211.s019
DOI:
10.1371/journal.pcbi.1009211.s020
DOI:
10.1371/journal.pcbi.1009211.s021
DOI:
10.1371/journal.pcbi.1009211.s022
DOI:
10.1371/journal.pcbi.1009211.s023
DOI:
10.1371/journal.pcbi.1009211.s024
DOI:
10.1371/journal.pcbi.1009211.s025
DOI:
10.1371/journal.pcbi.1009211.s026
DOI:
10.1371/journal.pcbi.1009211.s027
DOI:
10.1371/journal.pcbi.1009211.s028
DOI:
10.1371/journal.pcbi.1009211.r001
DOI:
10.1371/journal.pcbi.1009211.r002
DOI:
10.1371/journal.pcbi.1009211.r003
DOI:
10.1371/journal.pcbi.1009211.r004
DOI:
10.1371/journal.pcbi.1009211.r005
DOI:
10.1371/journal.pcbi.1009211.r006
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2021
detail.hit.zdb_id:
2193340-6
Permalink