RESUMO
SARS-CoV-2 infection fatality ratios (IFR) remain controversially discussed with implications for political measures, but the number of registered infections depends on testing strategies and deduced case fatality ratios (CFR) are poor proxies for IFR. The German county of Tirschenreuth suffered a severe SARS-CoV-2 outbreak in spring 2020 with particularly high CFR. To estimate seroprevalence, dark figure, and IFR for the Tirschenreuth population aged [≥]14 years in June/July 2020 with misclassification error control, we conducted a population-based study, including home visits for elderly, and analyzed 4203 participants for SARS-CoV-2 antibodies via three antibody tests (64% of our random sample). Latent class analysis yielded 8.6% standardized county-wide seroprevalence, dark figure factor 5.0, and 2.5% overall IFR. Seroprevalence was two-fold higher among medical workers and one third among current smokers with similar proportions of registered infections. While seroprevalence did not show an age-trend, the dark figure was 12.2 in the young versus 1.7 for [≥]85-year-old. Age-specific IFRs were <0.5% below 60 years of age, 1.0% for age 60-69, 13.2% for age 70+, confirming a previously reported age-model for IFR. Senior care homes accounted for 45% of COVID-19-related deaths, reflected by an IFR of 7.5% among individuals aged 70+ and an overall IFR of 1.4% when excluding senior care home residents from our computation. Our data underscore senior care home infections as key determinant of IFR additionally to age, insufficient targeted testing in the young, and the need for further investigations on behavioral or molecular causes of the fewer infections among current smokers.
RESUMO
During the SARS-CoV-2 outbreak, several epidemiological measures, such as cumulative case-counts, incidence rates, effective reproduction numbers and doubling times, have been used to inform the general public and to justify interventions such as lockdown. During the course of the epidemic, it has been very likely that not all infectious people have been identified, which lead to incomplete case-detection. Apart from asymptomatic infections, possible reasons for incomplete case-detection are availability of test kits and changes in test policies during the course of the epidemic. So far, it has not been examined how biased the reported epidemiological measures are in the presence of incomplete case detection. In this work, we assess the four frequently used measures with respect to incomplete case-detection: 1) cumulative case-count, 2) incidence rate, 3) effective reproduction number and 4) doubling time. We apply an age-structured SIR model to simulate a SARS-CoV-2 outbreak followed by a lockdown in a hypothetical population. Different scenarios about temporal variations in case-detection are applied to the four measures during outbreak and lockdown. The biases resulting from incomplete case-detection on the four measures are compared. It turns out that the most frequently used epidemiological measure, the cumulative case count is most prone to bias in all of our settings. The effective reproduction number is the least biased measure. With a view to future reporting about this or other epidemics, we recommend to use of the effective reproduction number for informing the general public and policy makers.
RESUMO
To assess the current dynamic of an epidemic it is central to collect information on the daily number of newly diseased cases. This is especially important in real-time surveillance, where the aim is to gain situational awareness, e.g., if cases are currently increasing or decreasing. Reporting delays between disease onset and case reporting hamper our ability to understand the dynamic of an epidemic close to now when looking at the number of daily reported cases only. Nowcasting can be used to adjust daily case counts for occurred-but-not-yet-reported events. Here, we present a novel application of nowcasting to data on the current COVID-19 pandemic in Bavaria. It is based on a hierarchical Bayesian model that considers changes in the reporting delay distribution over time and associated with the weekday of reporting. Furthermore, we present a way to estimate the effective time-dependent case reproduction number Re(t) based on predictions of the nowcast. The approaches are based on previously published work, that we considerably extended and adapted to the current task of nowcasting COVID-19 cases. We provide methodological details of the developed approach, illustrate results based on data of the current epidemic, and evaluate the model based on synthetic and retrospective data on COVID-19 in Bavaria. Results of our nowcasting are reported to the Bavarian health authority and published on a webpage on a daily basis (https://corona.stat.uni-muenchen.de/). Code and synthetic data for the analysis is available from https://github.com/FelixGuenther/nc_covid19_bavaria and can be used for adaptions of our approach to different data.
