@article{fdi:010082705,
  title = {{A} mechanistic and data-driven reconstruction of the time-varying reproduction number : application to the {COVID}-19 epidemic},
  author = {{C}azelles, {B}. and {C}hampagne, {C}. and {N}guyen-{V}an-{Y}en, {B}. and {C}omiskey, {C}. and {V}ergu, {E}. and {R}oche, {B}enjamin},
  editor = {},
  language = {{ENG}},
  abstract = {{A}uthor summary {I}n the early stages of any new epidemic, one of the first steps to design a control strategy is to estimate pathogen transmissibility in order to provide information on its potential to spread in the population. {A}mong the different epidemiological indicators that characterize the transmissibility of a pathogen, the effective reproduction number {R}-eff is commonly used for measuring time-varying transmissibility. {I}t measures how many additional people can be infected by an infected individual during the course of an epidemic. {H}owever, {R}-eff is difficult to estimate in the presence of silent transmission and/or significant temporal variation in case reporting. {T}his is exactly the situation we are confronted with during this {COVID}-19 pandemic. {T}he statistical methods classically used for the estimation of {R}-eff have some shortcomings in the rigorous consideration of the transmission characteristics of {SARS}-{C}o{V}-2. {W}e propose here to use an original approach based on a stochastic model whose parameters vary in time and are inferred in a {B}ayesian framework from reliable hospital data. {T}his enables us to reconstruct both the {COVID}-19 epidemic and its {R}-eff. {T}he {R}-eff time evolution allows us to get information regarding the potential effects of mitigation measures taken during and between epidemics waves. {T}his approach, based on a stochastic model that realistically describes the hospital multiple datasets and which overcomes many of the biases associated with {R}-eff estimates, appears to have some advantage over previously developed methods. {T}he effective reproduction number {R}-eff is a critical epidemiological parameter that characterizes the transmissibility of a pathogen. {H}owever, this parameter is difficult to estimate in the presence of silent transmission and/or significant temporal variation in case reporting. {T}his variation can occur due to the lack of timely or appropriate testing, public health interventions and/or changes in human behavior during an epidemic. {T}his is exactly the situation we are confronted with during this {COVID}-19 pandemic. {I}n this work, we propose to estimate {R}-eff for the {SARS}-{C}o{V}-2 (the etiological agent of the {COVID}-19), based on a model of its propagation considering a time-varying transmission rate. {T}his rate is modeled by a {B}rownian diffusion process embedded in a stochastic model. {T}he model is then fitted by {B}ayesian inference (particle {M}arkov {C}hain {M}onte {C}arlo method) using multiple well-documented hospital datasets from several regions in {F}rance and in {I}reland. {T}his mechanistic modeling framework enables us to reconstruct the temporal evolution of the transmission rate of the {COVID}-19 based only on the available data. {E}xcept for the specific model structure, it is non-specifically assumed that the transmission rate follows a basic stochastic process constrained by the observations. {T}his approach allows us to follow both the course of the {COVID}-19 epidemic and the temporal evolution of its {R}-eff(t). {B}esides, it allows to assess and to interpret the evolution of transmission with respect to the mitigation strategies implemented to control the epidemic waves in {F}rance and in {I}reland. {W}e 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 {F}rance but just 20% in {I}reland. {F}or the third wave in {I}reland the reduction was again significant (>70%).},
  keywords = {},
  booktitle = {},
  journal = {{PL}o{S} {C}omputational {B}iology},
  volume = {17},
  numero = {7},
  pages = {e1009211 [20 p.]},
  ISSN = {1553-734{X}},
  year = {2021},
  DOI = {10.1371/journal.pcbi.1009211},
  URL = {https://www.documentation.ird.fr/hor/fdi:010082705},
}