Using early data to estimate the actual infection fatality ratio from COVID-19 in France (Running title: Infection fatality ratio from COVID-19)

TL;DR

This study develops a mechanistic-statistical model combining epidemiological and data acquisition processes to estimate the true COVID-19 infection fatality ratio in France, revealing significantly higher infection numbers than observed.

Contribution

It introduces a novel coupling of SIR modeling with probabilistic data models to accurately estimate infection and mortality rates from limited testing data.

Findings

Estimated actual infected cases are about 15 times higher than observed.

Infection fatality rate is approximately 5.2 per 1000.

Basic reproduction number R0 is estimated at 4.8.

Abstract

The first cases of COVID-19 in France were detected on January 24, 2020. The number of screening tests carried out and the methodology used to target the patients tested do not allow for a direct computation of the real number of cases and the mortality rate.In this report, we develop a 'mechanistic-statistical' approach coupling a SIR ODE model describing the unobserved epidemiological dynamics, a probabilistic model describing the data acquisition process and a statistical inference method. The objective of this model is not to make forecasts but to estimate the real number of people infected with COVID-19 during the observation window in France and to deduce the mortality rate associated with the epidemic.Main results. The actual number of infected cases in France is probably much higher than the observations: we find here a factor x 15 (95%-CI: 4-33), which leads to a 5.2/1000 mortality rate (95%-CI: 1.5 / 1000-11.7/ 1000) at the end of the observation period. We find a R0 of 4.8, a high value which may be linked to the long viral shedding period of 20 days.