Estimating effective infection fatality rates during the course of the COVID-19 pandemic in Germany

Abstract

Background The infection fatality rate (IFR) of the Coronavirus Disease 2019 (COVID-19) is one of the most discussed figures in the context of this pandemic. In contrast to the case fatality rate (CFR), the IFR depends on the total number of infected individuals – not just on the number of confirmed cases. In order to estimate the IFR, several seroprevalence studies have been or are currently conducted. Methods Using German COVID-19 surveillance data and age-group specific IFR estimates from multiple international studies, this work investigates time-dependent variations in effective IFR over the course of the pandemic. Three different methods for estimating (effective) IFRs are presented: (a) population-averaged IFRs based on the assumption that the infection risk is independent of age and time, (b) effective IFRs based on the assumption that the age distribution of confirmed cases approximately reflects the age distribution of infected individuals, and (c) effective IFRs accounting for age- and time-dependent dark figures of infections. Results Effective IFRs in Germany are estimated to vary over time, as the age distributions of confirmed cases and estimated infections are changing during the course of the pandemic. In particular during the first and second waves of infections in spring and autumn/winter 2020, there has been a pronounced shift in the age distribution of confirmed cases towards older age groups, resulting in larger effective IFR estimates. The temporary increase in effective IFR during the first wave is estimated to be smaller but still remains when adjusting for age- and time-dependent dark figures. A comparison of effective IFRs with observed CFRs indicates that a substantial fraction of the time-dependent variability in observed mortality can be explained by changes in the age distribution of infections. Furthermore, a vanishing gap between effective IFRs and observed CFRs is apparent after the first infection wave, while an increasing gap can be observed during the second wave. Conclusions The development of estimated effective IFR and observed CFR reflects the changing age distribution of infections over the course of the COVID-19 pandemic in Germany. Further research is warranted to obtain timely age-stratified IFR estimates, particularly in light of new variants of the virus.

Estimating the Course of the COVID-19 Pandemic in Germany via Spline-based Hierarchical Modelling of Death Counts

“…While our approach shows parallels with the Bayesian model developed in Wood [9], which uses smoothing splines to estimate effective reproduction numbers in the United Kingdom, an important additional characteristic of our work is the adjustment for time-varying effective infection fatality rates (IFRs), which are estimated to change substantially over the course of the pandemic as a result of varying age distributions of infections (cf. [14]).…”

“…The IFR is an important link when trying to infer total numbers of infections from death counts. As the IFR of COVID-19 largely depends on the age of the infected individuals [12,13,18], it is important to take the age structure of infections into account, which has been shown to vary over the course of the pandemic in Germany [14]. Thus, in contrast to the original model of Flaxman et al [5] using time-constant averaged IFRs, we consider time-dependent effective IFRs which reflect the changing age distribution of infections.…”

“…Here, we estimate weekly effective IFRs based on the assumption that the age distributions of true infections can be approximated by the age distributions of confirmed cases (cf. [14]). In particular, based on data from the RKI [19], let C a,w denote the number of confirmed cases in calendar week w for agegroup a ∈ {0−9, 10−19, .…”

“…Another limitation of the original model of Flaxman et al [5] is that the infection fatality rate (IFR) is assumed to be constant over time. However, the IFR of COVID-19 increases largely with increasing age [12,13] infections changes substantially during the course of the pandemic [14], the effective IFR should not be regarded as constant over time when modelling the number of infections based on deaths data.…”

COVID-19: Estimation of the transmission dynamics in Spain using a stochastic simulator and black-box optimization techniques

“…Several studies have investigated which factors influence the value of the . The primary sources of variations are age and sex [76] . If the distribution of new infections is uniform along time regarding these variables, we can assume constants and statics values for and in different time-spans.…”