 Research
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Estimating effective infection fatality rates during the course of the COVID19 pandemic in Germany
BMC Public Health volume 21, Article number: 1073 (2021)
Abstract
Background
The infection fatality rate (IFR) of the Coronavirus Disease 2019 (COVID19) 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 COVID19 surveillance data and agegroup specific IFR estimates from multiple international studies, this work investigates timedependent variations in effective IFR over the course of the pandemic. Three different methods for estimating (effective) IFRs are presented: (a) populationaveraged 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 timedependent 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 timedependent dark figures. A comparison of effective IFRs with observed CFRs indicates that a substantial fraction of the timedependent 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 COVID19 pandemic in Germany. Further research is warranted to obtain timely agestratified IFR estimates, particularly in light of new variants of the virus.
Background
The ongoing pandemic of the novel coronavirus disease COVID19 provides enormous global challenges for public health, society and economy. An important figure in the context of this pandemic is the infection fatality rate (IFR), defined by the number of COVID19 associated deaths divided by the total number of infections. In contrast to the case fatality rate (CFR), the IFR is not only based on the number of confirmed cases and should therefore not be biased by potential drifts in testing policies. However, as the total number of infections with SARSCoV2 is generally unknown, the IFR can only be estimated based on available surveillance and seroprevalence data.
Many seroprevalence studies have been conducted worldwide with the aim of estimating the true numbers of infections and resulting IFRs. Also in Germany, several local seroprevalence studies are being conducted (e.g. [1]); a completed study from the early phase of the pandemic in the highprevalence region of Gangelt, Heinsberg, reports an estimated populationaveraged IFR of 0.41% (95% confidence interval [0.33%; 0.52%]), based on 8 observed deaths until 20th of April 2020 [2]. Overviews of completed studies from a wide range of countries can for example be found in [3] and [4]. The metaanalysis of MeyerowitzKatz et al. [3] yields an estimated populationaveraged IFR of 0.68% [0.53%; 0.82%], while in the metaanalysis of Ioannidis [4] estimated populationaveraged IFRs range from 0.02% to 0.86% with a median IFR of 0.26%. Although the two metaanalyses differ heavily regarding their results and conclusions, both observe a high heterogeneity in populationaveraged IFR estimates among different studies and emphasize the importance of obtaining reliable agestratified estimates.
More recent metaanalyses [5, 6] investigate agespecific mortality by estimating infection fatality rates for different age groups. As the risk of death from COVID19 is estimated to increase exponentially with age [6], it is crucial that the age distribution of infections is taken into account when interpreting estimated “overall” (populationaveraged) IFRs from different seroprevalence studies in different regions. In fact, a large proportion of the variability in estimated IFRs between studies in different regions may be explained simply by differences in demographics, particularly the age structure of populations. In addition, other factors such as the prevalence of certain comorbidities, the access to intensive medical care and systematic differences in social networks might also contribute to the variability of COVID19 associated mortality in different regions.
However, even when considering only a particular region, the observed mortality of SARSCoV2 may vary over time, as the age distribution of the infected population may be changing throughout the pandemic, e.g. due to different and changing risk behaviours as well as specific preventive measures for highrisk groups. In a recent study regarding “the foreshadow of a second wave” in Germany, Linden et al. [7] estimate the effective IFR as a timedependent measure by considering the infection fatality rate given the changing age distribution of confirmed cases, using agespecific IFR estimates from the metaanalysis of Levin et al. [6]. The effective IFR is estimated based on the assumption that the distribution of confirmed cases reflects the distribution of true infections [7]. This may not necessarily be the case, as e.g. infections with SARSCoV2 may be more likely detected in older age groups since these tend to experience a more serious disease course. Furthermore, as the focus of the study by Linden et al. [7] has been on the analysis of the beginning of a second wave of infections during late summer 2020, effective IFRs have not been reported for the earlier phase of the pandemic in Germany.
Thus, our study aims to investigate timedependent variations in effective IFR over the course of the COVID19 pandemic in Germany, by combining agespecific IFR estimates from multiple studies with publicly available German surveillance data. We compare estimated effective IFRs based on the age distribution of confirmed cases with estimates derived from the age distribution of estimated infections, obtained through estimated age and timedependent dark figures. Results are presented based on agespecific IFR estimates from four different studies, illustrating the remaining uncertainty regarding agespecific mortality.
Methods
We use the German COVID19 surveillance data provided by the Robert Koch Institute [8], containing information on date of disease onset (or date of confirmation of SARSCoV2 infection if disease onset unknown) and information on deaths associated with COVID19 for individual confirmed cases. Data on age of confirmed cases and deaths are available for the following age groups A={04,514,1534,3559,6079,80+}.
We consider cumulative data for each calendar week, so that potential weekdayspecific fluctuations are eliminated. Let C_{a,t} denote the number of confirmed cases for age group a∈A in calendar week t and let \(C_{t} = \sum _{a\in A} C_{a,t}\) denote the total number of confirmed cases in week t. Similarly, let I_{a,t} and I_{t} denote the number of true infections and let D_{a,t} and D_{t} denote the number of deaths, for age group a and week t. Note that deaths typically occur weeks after the onset of symptoms from COVID19 with an estimated average interval of 16 days [9], while infections with SARSCoV2 occur several days prior to manifestation of disease with an estimated median incubation period of 5 days [10]. However, here the time point t in C_{a,t},I_{a,t} and D_{a,t} always refers to the same week where the infection has been manifested or confirmed. The course of weekly confirmed cases and deaths for the different age groups in Germany is depicted in Fig. 1.
The observed CFR in week t is defined by the number of deaths D_{t} (resulting from infections in week t) divided by the number of confirmed cases C_{t} in week t, i.e. \(\text {CFR}_{t} = \frac {D_{t}}{C_{t}}\). On the other hand, the effective IFR in week t is defined based on the total number of infections, i.e. \(\text {IFR}_{\text {eff},t} = \frac {D_{t}}{I_{t}}\). As the number of infections I_{t} is unknown, we estimate the weekly effective IFR by taking into account the timedependent distribution of infections in the different age groups a∈A as well as agespecific IFR estimates from four different studies. In particular, we consider one modelling study [11] estimating agespecific IFR for China based on individualcase data from the early phase of the pandemic (until 25th of February 2020), one seroprevalence study [12] in Geneva, Switzerland (until 1st of June 2020) specifically designed for agestratified estimation of IFR, as well as two recent metaanalyses [5, 6] combining the evidence from multiple seroprevalence studies worldwide. As the age groups in the four sources of estimated IFR slightly differ from the age groups A considered in the used surveillance data, agespecific IFR estimates are adjusted to match with the age groups A via weighted averaging of estimates, taking into account the age structure of the German population (based on data from [13]).
Let \(\widehat {\text {IFR}}_{a}^{(i)}\) denote the resulting estimated IFR for age group a from study i, with the index i referring to one of the four literature sources (i∈ {O’Driscoll [5], Verity [11], PerezSaez [12], Levin [6]}).
The true effective IFR_{eff,t} can be estimated by a weighted average of agespecific IFR estimates, i.e.
where \(\hat {\omega }_{a,t}\) denotes an estimator for the fraction of infections I_{a,t}/I_{t} in age group a in week t. Note that estimator (1) for the effective IFR_{eff,t} is based on the crucial assumption that agedependent infection fatality rates IFR_{a} do not change over the course of the pandemic and that the estimates of IFR_{a} from the four international studies are applicable to Germany.
In the following, we consider three different estimators for the fraction of infections \(\hat {\omega }_{a,t}\), which are derived under different assumptions regarding the distribution of infections.

(a)
Under the theoretical assumption that the risk of infection is independent of age and time (compare also [7]), the effective IFR – denoted by \(\overline {\text {IFR}}_{\text {DE}}^{(i)}\) – is constant over time and estimated by Eq. 1 with timeindependent weights
$$ \hat{\omega}_{a,t} = \hat{\omega}_{a} = \frac{P_{a}}{P}, $$(2)where the population numbers P_{a} in age groups a∈A and the total population number \(P=\sum _{a\in A} P_{a}\) of Germany are regarded as constant over time.

(b)
In practice, the (nonuniform) age distribution of infections is likely to be changing over the course of the pandemic. Under the assumption that the distribution of confirmed cases approximately reflects the distribution of true infections in the different age groups, i.e.
$$ \frac{C_{a,t}}{C_{t}} \approx \frac{I_{a,t}}{I_{t}}, $$(3)one can estimate the fraction of unknown infections in age group a in week t by the corresponding fraction of confirmed cases
$$ \hat{\omega}_{a,t} = \frac{C_{a,t}}{C_{t}}. $$(4)Assumption (3) and the resulting estimator (4) correspond also to the effective IFR defined in [7]. Note that assumption (3) implies that dark figures of undetected infections are approximately independent of age.

(c)
Without specific assumptions as in (a) and (b), the number of infections I_{a,t} can be alternatively estimated by considering age and timedependent dark figures via
$$\begin{array}{*{20}l} &\hat{I}_{a,t}^{(i)} = \hat{f}_{a,t}^{(i)} \cdot C_{a,t}, \\ &~~~~\text{with}~ \hat{f}_{a,t}^{(i)} = \frac{\text{CFR}_{a,t}}{\widehat{\text{IFR}}_{a}^{(i)}} = \frac{ \frac{D_{a,t}}{C_{a,t}}}{ \widehat{\text{IFR}}_{a}^{(i)}}, \end{array} $$(5)where \(\hat {f}_{a,t}^{(i)}\) denotes the estimated factor for the dark figure in age group a in week t based on study i. Thus, an alternative estimator for the fraction of all infections in age group a in week t is given by
$$ \hat{\omega}_{a,t}^{(i)} = \frac{\hat{I}^{(i)}_{a,t}}{\sum_{a^{\prime}\in A}\hat{I}^{(i)}_{a^{\prime},t}} = \frac{\hat{f}_{a,t}^{(i)} \cdot C_{a,t}}{ \sum_{a^{\prime}\in A} \hat{f}_{a^{\prime},t}^{(i)} \cdot C_{a^{\prime},t}}. $$(6)As the number of true infections is at least as high as the number of confirmed cases (I_{a,t}≥C_{a,t}), the corresponding factor f_{a,t}=I_{a,t}/C_{a,t} for the dark figure should be lower bounded by 1. Thus, in the following we use the estimator \( \hat {f}_{a,t}^{(i)} = \max \{1, \text {CFR}_{a,t} / \widehat {\text {IFR}}_{a}^{(i)} \}\). Note that, after some algebraic manipulations, inserting the weights \(\hat {\omega }_{a,t}^{(i)}\) of Eq. (6) into the weighted arithmetic mean (1) shows that the resulting estimated effective IFR can also be viewed as a weighted harmonic mean of the agespecific IFR estimates with weights proportional to \(\max \{D_{a,t}, C_{a,t} \cdot \widehat {\text {IFR}}_{a}^{(i)} \}\). Due to relatively small numbers of observed deaths in younger age groups, we combine the age groups a∈{04,514,1534,3559} yielding joint estimates \(\hat {f}_{059,t}^{(i)}\) of timedependent dark figures for ages 0 to 59. To further stabilize the procedure, we estimate monthly (instead of weekly) dark figures based on agespecific CFRs observed for each month.
In this study we hence compare three different estimators for the effective IFR, each depending on different assumptions. Estimator (a) leads to a timeconstant effective \(\overline {\text {IFR}}_{\text {DE}}^{(i)}\), while estimators (b) and (c) actually take into account the changing age distribution of confirmed cases and estimated infections, respectively. All three estimators depend on the four available agespecific estimates \(\widehat {\text {IFR}}_{a}^{(i)}\) from the literature (Table 1).
Results
The risk of death among persons infected with SARSCoV2 is estimated to increase substantially with increasing age by each of the four considered studies (Table 1), which is also supported by the number of observed deaths D_{a,t} per age group in Germany (see Fig. 1). However, estimates from the literature show larger discrepancies; as for example in age group 80+, the IFR estimate from [12] is given by 5.60% [4.30%; 7.40%], while the corresponding IFR estimate from [6] is as large as 15.61% [12.20%; 19.50%]. On the other hand, for the age group 6079 the IFR estimate from [5] is approximately 1%, while the other studies yield larger estimates for this age group ranging from 2.49% in [6] to 3.89% in [12]. Furthermore, Table 1 gives estimates of resulting populationaveraged infection fatality rates \(\overline {\text {IFR}}_{\text {DE}}^{(i)}\) for Germany, which are derived under the assumption that the risk of infection with SARSCoV2 is independent of age and time (see assumption (a)). Populationaveraged estimates \(\overline {\text {IFR}}_{\text {DE}}^{(i)}\) for Germany range from 0.756% [0.717%; 0.796%] by [5] to 1.687% [1.407%; 2.139%] by [6], reflecting the uncertainty regarding agespecific IFR.
The estimated populationaveraged infection fatality rates \(\overline {\text {IFR}}_{\text {DE}}^{(i)}\), based on different agespecific IFR estimates, can be interpreted as reference mortality figures for the general German population in order to compare them to other countries. They have a rather theoretical meaning as they do not reflect the actual age distribution of the infected population. Figure 2 depicts the changing age distribution of weekly confirmed cases (central plot) in comparison to the age distribution of the general population (left plot). It can be observed that the age distribution of confirmed cases shifted considerably towards older age groups during the first wave in Germany in March and April 2020. During summer with a relatively low incidence of COVID19, confirmed cases were predominantly observed in younger age groups. However, since September 2020, percentages of confirmed cases among the elderly have been continuously rising again. In the end of December 2020, the age distribution of confirmed cases is remarkably similar to the distribution of confirmed cases in April during the first wave of infections.
The described trend in the distribution of confirmed cases over time is directly reflected in the corresponding development of estimated effective IFR (based on method (b)). The left side in Fig. 3 shows that the estimated effective IFR sharply increases from values between 0.5% and 1% in March to values between 1.5% and 3.5% in April. After this peak, the estimated effective IFR has been declining to values between 0.2% and 0.5% in the end of August, corresponding to a relatively young age distribution of confirmed cases. This observation may be partly explained by an increased mobility of younger age groups during the summer holiday period. Since September 2020, as the distribution of confirmed cases has been shifting more towards older age groups, effective IFR estimates have been rising again up to similar levels as in the peak during the first wave of infections. This indicates that with larger SARSCoV2 incidences (see Fig. 1) it may become increasingly difficult to effectively protect vulnerable risk groups and to prevent the spread of the virus from younger to older age groups (see also [7]).
As the age distribution of confirmed cases may not generally reflect the age distribution of true infections, in a further analysis we account for age and timedependent dark figures (see method (c)). The right hand side of Fig. 2 depicts the development of estimated true infections based on IFR estimates from Levin et al. [6]. It can be seen that the development of estimated infections is similar in shape to the observed development of confirmed cases. However, in particular following the high phase of the first wave of infections in April (compare Fig. 1), the estimated distribution of infections is shifted towards younger age groups in comparison to the distribution of confirmed cases. This shift results from dark figures of infections which are estimated to be larger in younger age groups in comparison to the age group 80+ during this particular time. A plausible explanation for this observation might be that in times of limited testing capacities, preferential testing of individuals in age group 80+ has been more pronounced, as these patients are more likely to show (severe) symptoms from COVID19 requiring medical intervention. Similar effects on estimated infections during the first wave are also observed when using agespecific IFR estimates from O’Driscoll et al. [5] and PerezSaez et al. [12], whereas numbers of infections in age group 80+ are estimated to be comparatively larger based on Verity et al. [11] (detailed results on estimated infections not shown). During summer 2020, there seems to be a close alignment of estimated infections with confirmed cases, as agedependent factors for dark figures are estimated to be close to 1 (and would have partly been even below 1). This may indicate that a large proportion of infections has been detected with the implemented testing policies during the summer period.
The right hand side of Fig. 3 depicts the resulting development of estimated effective IFR when accounting for age and timedependent dark figures. It can be seen that the adjustment for dark figures has a particular effect during the first wave of the pandemic in Germany, where estimated effective IFRs tend to be smaller in comparison to the unadjusted estimates based on confirmed cases (compare to left hand side of Fig. 3). However, even when adjusting for agedependent dark figures, there still remains a pronounced increase in estimated effective IFRs during the first wave of infections; this indicates that the increase in mortality cannot exclusively be explained by preferential testing, but that there has been an actual change in the age distribution of the infected population. During summer 2020, the age distribution of estimated infections more closely aligns with the age distribution of confirmed cases and thus the estimates of effective IFR adjusted for dark figures are very similar to the unadjusted estimates. In contrast to the first wave of infections in spring 2020, during the second wave the adjusted estimates are not systematically lower and partly even larger than the unadjusted ones.
Figure 3 also shows the development of estimated effective IFR in comparison with the development of observed CFR in Germany. It can be seen that trends in observed CFR closely resemble trends in effective IFR estimated based on the age distribution of confirmed cases (as well as true infections). This implies that the age distribution of infections is a major determinant (and predictor) for the resulting mortality associated with COVID19. Despite this, it can be observed that the gap between CFR and IFR has been declining after the first wave in Germany; in fact, observed CFRs in August and September are even lower than some estimates of effective IFR. There may be multiple possible reasons for the decline in CFR, which are independent of the age distribution of infections: The first and probably largest contribution to the observed decline in CFR is the steady and considerable increase in conducted SARSCoV2 testing in Germany [14]. For example, the number of conducted SARSCoV2 tests increased from 586,620 in calendar week 31 (end of July) to 1,121,214 tests in calendar week 35 (end of August) [15], reflecting a doubling in weekly conducted tests in Germany, partly due to increased testing in the context of the summer holiday season. Another plausible reason for a further decline in observed CFR may be due to other factors leading to a decrease in agespecific IFR, such as improvements in treatment of COVID19 [16], as well as more targeted and timely initiation of therapy [17], which may also be positively affected by an increased awareness of the public. During the second wave of infections in autumn/winter 2020, the gap between CFR and IFR tends to be increasing again, but the CFR is still at lower levels in comparison to the first wave, as dark figures of infections during the second wave are estimated to be smaller than during the first.
The course of the pandemic should also be viewed in light of mitigation efforts in Germany (cf. [18, 19]). Due to the German federal structure there have been specific differences in implemented measures between the 16 federal states, although a uniform procedure has been sought by the federal government and local states. Table 2 provides an overview of important mitigation measures which have been applied to most regions of Germany. The effectiveness of interventions during the first infection wave has been investigated by Dehning et al. [20], concluding that the entirety of measures in context of the “first lockdown”, implemented in three consecutive weeks (cf. Table 2), effectively reduced the spread of the virus (cf. Fig. 1).
In addition, our results (Fig. 3) show a timedelayed decline of effective IFR in May 2020 after the incidence peak in March and April, indicating that the spread of the virus to older age groups could be reduced after incidences reached more controllable levels. During summer 2020, with relaxed mitigating measures still in place, such as the requirement of wearing masks in stores and public transport, incidences of COVID19 remained relatively low. In this time, there was a particularly young age distribution of cases, resulting in small estimated effective IFRs and a reduced disease burden for highrisk groups. As the second wave of infections in Germany has been ongoing at the time of writing, it is too early for a conclusive evaluation of mitigation measures, including the “lockdown light” and the “second lockdown” (see Table 2 and Fig. 1); however, our results show that estimated effective IFRs (and observed CFRs) have been continuously rising with increasing numbers of cases until December 2020.
Limitations of this study include that the analysis is based on the assumptions that agespecific IFRs are constant over time and that IFR estimates from the four international studies are applicable to Germany. Furthermore, in this work we have focused on estimating the effective IFR based on the changing age distribution of infections; however, in practice many other factors may also contribute to the variability in mortality of COVID19, such as the distribution of different comorbidities as well as the sex distribution of infected individuals. Another limitation of this study is that, due to relatively small numbers of observed deaths in young age groups, monthly dark figures are estimated jointly for the wide age group of 0 to 59 years, even though true dark figures may further differentiate in practice (see e.g. [21] for a recent study of dark figures for children in Germany). Finally, for the estimation of age and timedependent dark figures we assumed that there are no systematic biases in reported agespecific deaths, which may not necessarily be the case; for example, Michelozzi et al. [22] investigate the temporal dynamics in excess mortality in Italian cities and observe an underestimation of COVID19 deaths for older age groups.
Conclusions
We have illustrated that the effective IFR in Germany is estimated to vary during the course of the pandemic, as the age distribution of infections is changing over time. In fact, it can be observed that a large fraction of the timedependent variability in CFR can be explained by changes in the age distribution of infections. The additionally observed trends in the gap between the CFR and effective IFR require further investigation in order to disentangle the contributions of shifts in testing policies and of other factors that may induce changes in mortality. Particularly in light of new variants of the virus, future research should be targeted at obtaining timely agespecific IFR estimates for Germany.
Availability of data and materials
The dataset supporting the conclusions of this article is publicly available from the Robert Koch Institute [8].
Abbreviations
 SARSCoV2:

Severe acute respiratory syndrome coronavirus 2
 COVID19:

Coronavirus Disease 2019
 IFR:

Infection fatality rate
 CFR:

Case fatality rate
References
Radon K, Saathoff E, Pritsch M, Guggenbuehl Noller J, Kroidl I, Olbrich L, Hoelscher M, et al. Protocol of a populationbased prospective COVID19 cohort study Munich, Germany (koco19). BMC Public Health. 2020; 20(1036). https://doi.org/10.1186/s12889020091649.
Streeck H, Schulte B, Kümmerer BM, Richter E, Höller T, Fuhrmann C, Bartok E, DolscheidPommerich R, Berger M, Wessendorf L, et al. Infection fatality rate of SARSCoV2 in a superspreading event in Germany. Nat Commun. 2020; 11(1):1–12.
MeyerowitzKatz G, Merone L. A systematic review and metaanalysis of published research data on covid19 infection fatality rates. Int J Infect Dis. 2020; 101:138–48. https://doi.org/10.1016/j.ijid.2020.09.1464.
Ioannidis J. The infection fatality rate of COVID19 inferred from seroprevalence data. medRxiv. 2020. https://doi.org/10.1101/2020.05.13.20101253.
O’Driscoll M, Dos Santos GR, Wang L, Cummings DA, Azman AS, Paireau J, Fontanet A, Cauchemez S, Salje H. Agespecific mortality and immunity patterns of SARSCoV2. Nature. 2020. https://doi.org/10.1038/s4158602029180.
Levin AT, Hanage WP, OwusuBoaitey N, Cochran KB, Walsh SP, MeyerowitzKatz G. Assessing the age specificity of infection fatality rates for COVID19: systematic review, metaanalysis, and public policy implications. Eur J Epidemiol. 2020; 35(12):1123–38.
Linden M, Dehning J, Mohr SB, Mohring J, MeyerHermann M, Pigeot I, Schöbel A, Priesemann V. The foreshadow of a second wave: An analysis of current COVID19 fatalities in Germany. 2020. https://arxiv.org/pdf/2010.05850.pdf.
RKI. COVID19 data. https://www.arcgis.com/sharing/rest/content/items/f10774f1c63e40168479a1feb6c7ca74/data. Accessed: 22 April 2021.
Khalili M, Karamouzian M, Nasiri N, Javadi S, Mirzazadeh A, Sharifi H. Epidemiological characteristics of COVID19: a systematic review and metaanalysis. Epidemiol Infect. 2020; 148(e130). https://doi.org/10.1017/s0950268820001430.
Lauer SA, Grantz KH, Bi Q, Jones FK, Zheng Q, Meredith HR, Azman AS, Reich NG, Lessler J. The incubation period of coronavirus disease 2019 (COVID19) from publicly reported confirmed cases: estimation and application. Annals Intern Med. 2020; 172(9):577–82.
Verity R, Okell LC, Dorigatti I, Winskill P, Whittaker C, Imai N, CuomoDannenburg G, Thompson H, Walker PG, Fu H, et al. Estimates of the severity of coronavirus disease 2019: a modelbased analysis. Lancet Infect Dis. 2020; 20(6):669–77.
PerezSaez J, Lauer SA, Kaiser L, Regard S, Delaporte E, Guessous I, Stringhini S, Azman AS, Group SPS. Serologyinformed estimates of SARSCOV2 infection fatality risk in Geneva, Switzerland. Lancet Infect Dis. 2020. https://doi.org/10.1101/2020.06.10.20127423.
Statistisches Bundesamt. Vorausberechneter Bevölkerungsstand: Bundesländer, Stichtag, Varianten der Bevölkerungsvorausberechnung, Geschlecht, Altersjahre. https://wwwgenesis.destatis.de/genesis//online?operation=table&code=124210004&bypass=true&levelindex=0&levelid=1603892461345#abreadcrumb. Accessed: 03 Nov 2020.
RKI. Erfassung der SARSCoV2Testzahlen in Deutschland. https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Testzahl.html. Accessed: 03 Nov 2020.
RKI. Coronavirus Disease 2019 (COVID19) Daily Situation Report of the Robert Koch Institute, 14/10/2020. https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Situationsberichte/Okt_2020/20201014en.pdf?__blob=publicationFile. Accessed: 03 Nov 2020.
Horwitz L, Jones SA, Cerfolio RJ, Francois F, Greco J, Rudy B, Petrilli CM. Trends in Covid19 riskadjusted mortality rates in a single health system. J Hosp Med. 2020. https://doi.org/10.12788/jhm.3552.
Wu J, Li W, Shi X, Chen Z, Jiang B, Liu J, Wang D, Liu C, Meng Y, Cui L, et al. Early antiviral treatment contributes to alleviate the severity and improve the prognosis of patients with novel coronavirus disease (COVID19). J Internal Med. 2020; 288(1):128–38.
Ebrahim SH, Ahmed QA, Gozzer E, Schlagenhauf P, Memish ZA. Covid19 and community mitigation strategies in a pandemic. BMJ. 2020; 368. https://doi.org/10.1136/bmj.m1066.
Ebrahim SH, Zhuo J, Gozzer E, Ahmed QA, Imtiaz R, Ahmed Y, Doumbia S, Rahman NM, Elachola H, WilderSmith A, Memish ZA. All hands on deck: A synchronized wholeofworld approach for COVID19 mitigation. Int J Infect Dis. 2020; 98:208–15.
Dehning J, Zierenberg J, Spitzner FP, Wibral M, Neto JP, Wilczek M, Priesemann V. Inferring change points in the spread of COVID19 reveals the effectiveness of interventions. Science. 2020; 369(6500). https://doi.org/10.1126/science.abb9789.
Hippich M, Holthaus L, Assfalg R, ZapardielGonzalo J, Kapfelsperger H, Heigermoser M, Haupt F, Ewald DA, Welzhofer TC, Marcus BA, Heck S, Koelln A, Stock J, Voss F, Secchi M, Piemonti L, de la Rosa K, Protzer U, Boehmer M, Achenbach P, Lampasona V, Bonifacio E, Ziegler AG. A public health antibody screening indicates a 6fold higher SARSCoV2 exposure rate than reported cases in children. Med. 2021; 2(2):149–163.e4. https://doi.org/10.1016/j.medj.2020.10.003.
Michelozzi P, de’Donato F, Scortichini M, Pezzotti P, Stafoggia M, De Sario M, Costa G, Noccioli F, Riccardo F, Bella A, et al. Temporal dynamics in total excess mortality and COVID19 deaths in Italian cities. BMC Public Health. 2020; 20(1):1–8. https://doi.org/10.1186/s12889020093358.
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All three authors contributed to the conception and design of the project, analyzed and interpreted the data. CS wrote the initial version of the manuscript, supported by AM. TW provided critical comments. All authors have read and approved the final version.
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Staerk, C., Wistuba, T. & Mayr, A. Estimating effective infection fatality rates during the course of the COVID19 pandemic in Germany. BMC Public Health 21, 1073 (2021). https://doi.org/10.1186/s12889021111277
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DOI: https://doi.org/10.1186/s12889021111277