ENBIS COVID-19 Webinar Series 6

6 July 2020; 12:00 – 12:45

Multi-level Modelling of Early COVID-19 Epidemic Dynamics in French Regions and Estimation of the Lockdown Impact on Infection Rate (Mélanie Prague)

MULTI-LEVEL MODELLING OF EARLY COVID-19 EPIDEMIC DYNAMICS IN FRENCH REGIONS AND ESTIMATION OF THE LOCKDOWN IMPACT ON INFECTION RATE

Date and Time: 06 July 2020, Monday at 12:00 CEST

Speaker: Dr. Mélanie Prague, Inria, University of Bordeaux, France

Moderator: Jean-Michel Poggi

Link to Talk: https://youtu.be/ncpf1Iv9r1A

Abstract:

We propose a multi-level approach to model the beginning of the French COVID-19 epidemic at the regional level. We rely on an extended Susceptible-Exposed-Infectious-Recovered (SEIR) mechanistic model, a simplified representation of the average epidemic process. Combining several French public datasets on the early dynamics of the epidemic, we estimate region-specific key parameters conditionally on this mechanistic model through Stochastic Approximation Expectation Maximization (SAEM) optimization using Monolix software. We thus estimate basic reproductive numbers by region before lockdown 2.81 [2.58; 3.07], the percentage of infected people over time also known as attack rates (between 1.9% and 9.9% as of May 11th, 2020) and the impact of nationwide lockdown on the infection rate (decreasing the transmission rate by 76% toward an effective reproductive number ranging from 0.63 to 0.73 at the end of lockdown). 

This talk is based on a joint work with Linda Wittkop, Dan Dutartre, Annabelle Collin, Quentin Clairon, Philippe Moireau, Rodolphe Thiébaut and Boris Hejblum. 

 

Short bio:

Melanie Prague is a permanent researcher at Inria (University of Bordeaux, France) in the SISTM team (Statistics in Immunology and translational medicine) since October 2016. Since 2013, she holds a PhD in Biostatistics and Public Health from the University of Bordeaux, France. She also was a postdoctoral fellow during almost three years at Harvard School of Public Health (Boston, USA). Her research focuses on the development of statistical methods for treatment and prevention of infectious diseases. She develops both within-host and between-host models to accelerate the development of treatments and vaccines. Her main fundings are centered around applications on HIV, Ebola, Nipah and COVID-19.