To assess the current dynamics 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, for example, if cases are currently increasing or decreasing. Reporting delays between disease onset and case reporting hamper our ability to understand the dynamics 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‐varying case reproduction number 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 pandemic, 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 are available from https://github.com/FelixGuenther/nc_covid19_bavaria and can be used for adaption of our approach to different data.

SARS-CoV-2 infection fatality ratios (IFR) remain controversially discussed with implications for political measures. The German county of Tirschenreuth suffered a severe SARS-CoV-2 outbreak in spring 2020, with particularly high case fatality ratio (CFR). To estimate seroprevalence, underreported infections, and IFR for the Tirschenreuth population aged ≥14 years in June/July 2020, we conducted a population-based study including home visits for the elderly, and analyzed 4203 participants for SARS-CoV-2 antibodies via three antibody tests. Latent class analysis yielded 8.6% standardized county-wide seroprevalence, a factor of underreported infections of 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 factor of underreported infections 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, and 13.2% for age 70+. 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.

We analysed the coronavirus disease 2019 epidemic curve from March to the end of April 2020 in Germany. We use statistical models to estimate the number of cases with disease onset on a given day and use back-projection techniques to obtain the number of new infections per day. The respective time series are analysed by a trend regression model with change points. The change points are estimated directly from the data. We carry out the analysis for the whole of Germany and the federal state of Bavaria, where we have more detailed data. Both analyses show a major change between 9 and 13 March for the time series of infections: from a strong increase to a decrease. Another change was found between 25 March and 29 March, where the decline intensified. Furthermore, we perform an analysis stratified by age. A main result is a delayed course of the pandemic for the age group 80 + resulting in a turning point at the end of March. Our results differ from those by other authors as we take into account the reporting delay, which turned out to be time dependent and therefore changes the structure of the epidemic curve compared to the curve of newly reported cases.

Imaging technology and machine learning algorithms for disease classification set the stage for high‐throughput phenotyping and promising new avenues for genome‐wide association studies (GWAS). Despite emerging algorithms, there has been no successful application in GWAS so far. We establish machine learning‐based phenotyping in genetic association analysis as misclassification problem. To evaluate chances and challenges, we performed a GWAS based on automatically classified age‐related macular degeneration (AMD) in UK Biobank (images from 135,500 eyes; 68,400 persons). We quantified misclassification of automatically derived AMD in internal validation data (4,001 eyes; 2,013 persons) and developed a maximum likelihood approach (MLA) to account for it when estimating genetic association. We demonstrate that our MLA guards against bias and artifacts in simulation studies. By combining a GWAS on automatically derived AMD and our MLA in UK Biobank data, we were able to dissect true association (ARMS2/HTRA1, CFH) from artifacts (near HERC2) and identified eye color as associated with the misclassification. On this example, we provide a proof‐of‐concept that a GWAS using machine learning‐derived disease classification yields relevant results and that misclassification needs to be considered in analysis. These findings generalize to other phenotypes and emphasize the utility of genetic data for understanding misclassification structure of machine learning algorithms.

Antibody testing for determining the SARS-CoV-2 serostatus was rapidly introduced in early 2020 and since then has been gaining special emphasis regarding correlates of protection. With limited access to representative samples with known SARS-CoV-2 infection status during the initial period of test development and validation, spectrum bias has to be considered when moving from a “test establishment setting” to population-based settings, in which antibody testing is currently implemented. To provide insights into the presence and magnitude of spectrum bias and to estimate performance measures of antibody testing in a population-based environment, we compared SARS-CoV-2 neutralization to a battery of serological tests and latent class analyses (LCA) in a subgroup (n = 856) of the larger population based TiKoCo-19 cohort (n = 4185). Regarding spectrum bias, we could proof notable differences in test sensitivities and specificities when moving to a population-based setting, with larger effects visible in earlier registered tests. While in the population-based setting the two Roche ELECSYS anti-SARS-CoV-2 tests outperformed every other test and even LCA regarding sensitivity and specificity in dichotomous testing, they didn’t provide satisfying quantitative correlation with neutralization capacity. In contrast, our in-house anti SARS-CoV-2-Spike receptor binding domain (RBD) IgG-ELISA (enzyme-linked-immunosorbant assay) though inferior in dichotomous testing, provided satisfactory quantitative correlation and may thus represent a better correlate of protection. In summary, all tests, led by the two Roche tests, provided sufficient accuracy for dichotomous identification of neutralizing sera, with increasing spectrum bias visible in earlier registered tests, while the majority of tests, except the RBD-ELISA, didn’t provide satisfactory quantitative correlations.