Using early data to estimate the actual infection fatality ratio from COVID-19 in France

Abstract

Background. The number of screening tests carried out in France and the methodology used to target the patients tested do not allow for a direct computation of the actual number of cases and the infection fatality ratio (IFR). The main objective of this work is to estimate the actual number of people infected with COVID-19 and to deduce the IFR during the observation window in France.Methods. We develop a 'mechanistic-statistical' approach coupling a SIR epidemiological model describing the unobserved epidemiological dynamics, a probabilistic model describing the data acquisition process and a statistical inference method.Results. The actual number of infected cases in France is probably higher than the observations: we find here a factor ×8 (95%-CI: 5-12) which leads to an IFR in France of 0.5% (95%-CI: 0.3−0.8) based on hospital death counting data. Adjusting for the number of deaths in nursing homes, we obtain an IFR of 0.8% (95%-CI: 0.45 − 1.25).Conclusions. This IFR is consistent with previous findings in China (0.66%) and in the UK (0.9%) and lower than the value previously computed on the Diamond Princess cruse ship data (1.3%).

Multi-level modeling of early COVID-19 epidemic dynamics in French regions and estimation of the lockdown impact on infection rate

“…Under-reporting or over-estimation of COVID-19 deaths According to Roques et al (2020), the Infection Fatality Ratio (IFR) for COVID-19 is 0.5% (95%-CI: 0.3; 0.8). Figure S2 shows that using this IFR over-estimate compared to the death currently reported by Santé Publique France.…”

“…We assume that ICU (t, ξ i ) = 0.25 × H(t, ξ i ) which is consistent with the prevalence of ICU cases among hospitalized cases at the French national level. Based on the estimation of the Infection Fatality Ratio (IFR) from Roques et al (2020), we get a rough estimation of D(t, ξ i ) as 0.5% of R(t, ξ i ). Roques et al (2020) conclude that COVID-19 fatalities are under-reported, and using their IFR estimate we adequately fit the trend of the observed COVID-19 deaths but with an offset due to this assumed higher IFR, see Appendix A1.…”

“…Moreover, the data collection yields noisy observations, that we deal with a statistical modeling of the observation process, rather than altering the data by e.g. smoothing such as in Roques et al (2020).…”

Effect of a one-month lockdown on the epidemic dynamics of COVID-19 in France

“…In a previous study [12], we applied this framework to the data corresponding to the beginning of the epidemic in France (from February 29 to March 17), with a SIR model. Our primary objective was to assess the infection fatality ratio (IFR), defined as the number of deaths divided by the number of infected cases.…”

Estimating population immunity without serological testing

“…There is a wide spectrum of estimates of µ in the literature, see e.g. [3], [4], [6], based on complete tests conducted on small closed populations exposed to COVID-19, but there appears to be a consensus that the actual range should be between 0.1% and 1.0%. The difficulty lies of course in the fact that the IFR is based not only on the observed cases, but also on undiagnosed and asymptomatic cases.…”