The world faces an unprecedented SARS-CoV2 pandemic where many critical factors still remain unknown. The case fatality rates (CFR) reported in the context of the SARS-CoV-2 pandemic substantially differ between countries. For SARS-CoV-2 infection with its broad clinical spectrum from asymptomatic to severe disease courses, the infection fatality rate (IFR) is the more reliable parameter to predict the consequences of the pandemic. Here we combined virus RT-PCR testing and assessment for SARS-CoV2 antibodies to determine the total number of individuals with SARS-CoV-2 infections in a given population. Methods: A sero-epidemiological GCP- and GEP-compliant study was performed in a small German town which was exposed to a super-spreading event (carnival festivities) followed by strict social distancing measures causing a transient wave of infections. Questionnaire-based information and biomaterials were collected from a random, household-based study population within a seven-day period, six weeks after the outbreak. The number of present and past infections was determined by integrating results from anti-SARS-CoV-2 IgG analyses in blood, PCR testing for viral RNA in pharyngeal swabs and reported previous positive PCR tests. Results: Of the 919 individuals with evaluable infection status (out of 1,007; 405 households) 15.5% (95% CI: [12.3%; 19.0%]) were infected. This is 5-fold higher than the number of officially reported cases for this community (3.1%). Infection was associated with characteristic symptoms such as loss of smell and taste. 22.2% of all infected individuals were asymptomatic. With the seven SARS-CoV-2-associated reported deaths the estimated IFR was 0.36% [0.29%; 0.45%]. Age and sex were not found to be associated with the infection rate. Participation in carnival festivities increased both the infection rate (21.3% vs. 9.5%, p<0.001) and the number of symptoms in the infected (estimated relative mean increase 1.6, p=0.007). The risk of a person being infected was not found to be associated with the number of study participants in the household this person lived in. The secondary infection risk for study participants living in the same household increased from 15.5% to 43.6%, to 35.5% and to 18.3% for households with two, three or four people respectively (p<0.001). Conclusions: While the number of infections in this high prevalence community is not representative for other parts of the world, the IFR calculated on the basis of the infection rate in this community can be utilized to estimate the percentage of infected based on the number of reported fatalities in other places with similar population characteristics. Whether the specific circumstances of a super-spreading event not only have an impact on the infection rate and number of symptoms but also on the IFR requires further investigation. The unexpectedly low secondary infection risk among persons living in the same household has important implications for measures installed to contain the SARS-CoV-2 virus pandemic.

A SARS-CoV2 super-spreading event occurred during carnival in a small town in Germany. Due to the rapidly imposed lockdown and its relatively closed community, this town was seen as an ideal model to investigate the infection fatality rate (IFR). Here, a 7-day seroepidemiological observational study was performed to collect information and biomaterials from a random, household-based study population. The number of infections was determined by IgG analyses and PCR testing. We found that of the 919 individuals with evaluable infection status, 15.5% (95% CI:[12.3%; 19.0%]) were infected. This is a fivefold higher rate than the reported cases for this community (3.1%). 22.2% of all infected individuals were asymptomatic. The estimated IFR was 0.36% (95% CI:[0.29%; 0.45%]) for the community and 0.35% [0.28%; 0.45%] when age-standardized to the population of the community. Participation in carnival increased both infection rate (21.3% versus 9.5%, p < 0.001) and number of symptoms (estimated relative mean increase 1.6, p = 0.007). While the infection rate here is not representative for Germany, the IFR is useful to estimate the consequences of the pandemic in places with similar healthcare systems and population characteristics. Whether the super-spreading event not only increases the infection rate but also affects the IFR requires further investigation.

Summary A popular modeling approach for competing risks analysis in longitudinal studies is the proportional subdistribution hazards model by Fine and Gray (1999. A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association94, 496–509). This model is widely used for the analysis of continuous event times in clinical and epidemiological studies. However, it does not apply when event times are measured on a discrete time scale, which is a likely scenario when events occur between pairs of consecutive points in time (e.g., between two follow-up visits of an epidemiological study) and when the exact lengths of the continuous time spans are not known. To adapt the Fine and Gray approach to this situation, we propose a technique for modeling subdistribution hazards in discrete time. Our method, which results in consistent and asymptotically normal estimators of the model parameters, is based on a weighted ML estimation scheme for binary regression. We illustrate the modeling approach by an analysis of nosocomial pneumonia in patients treated in hospitals.