The Robert Koch Institute (RKI) is a German federal government agency and research institute responsible for disease control and prevention. It is located in Berlin and Wernigerode. As an upper federal agency, it is subordinate to the Federal Ministry of Health. It was founded in 1891 and is named for its founding director, the founder of modern bacteriology and Nobel laureate Robert Koch.
National-level monitoring of mental well-being is increasingly important, but existing measures vary widely, hindering comparability. This study aimed to identify a suitable multicomponent measure for regular nationwide surveillance and cross-national comparability by incorporating recommendations on measuring well-being. We surveyed 2614 participants, balanced across age (18–74 years), sex, and education, to explore the added value of combining the seven-item Short Warwick–Edinburgh Well-being Scale (SWEMWBS) with three widely used single-item measures of mental well-being (happiness, life satisfaction, and meaning of life). These reflect hedonia, eudaimonia, and interpersonal well-being. We also examined the factor structure of this composite measure. The analysis confirmed a three-factor structure for SWEMWBS and suggested a fourth factor combining the three single items, with a higher-order general well-being factor. The results supported the use of a composite score for mental well-being, as well as separate analyses of hedonic and eudaimonic components. Meaning of life had the highest added value, indicating potential distinctiveness. This study demonstrates that a comprehensive, yet economical, measure of mental well-being can be applied to large-scale assessments. Combining SWEMWBS with the three common single-item measures enhances comparability while providing a more nuanced understanding of well-being. The results supported the use of a composite score for mental well-being, as well as separate analyses of hedonic and eudaimonic components, which is particularly relevant for routine public health surveillance. Regular public surveillance of mental well-being can inform public health strategies and track changes over time.
Because anaplastic thyroid cancer (ATC) is a rare diagnosis, detailed epidemiological data are difficult to find in publicly accessible platforms. The aim of this study was to report current incidence and survival estimates for ATC in Germany and to analyse their trends over time. Data from the population-based cancer registries in Germany were pooled. ATC was classified according to the International Classification of Diseases, 10th edition (ICD-10). Age-standardised incidence rates (per 100,000) for the years 2011 to 2022 were calculated. The relative survival was estimated using period analysis. Annual percentage changes (APC) in the incidence and survival trends over time were computed. The number of new ATC cases diagnosed between 2011 and 2022 was 2127 (924 in men and 1203 in women). The current raw incidence is 0.19 in men and 0.22 in women. The age-standardised incidence in men changed slightly, but not significantly, during this period (APC − 2
Animal listeriosis is a sporadic bacterial infection caused by Listeria (L.) monocytogenes and L. ivanovii. In Germany, only listeriosis caused by L. monocytogenes is considered a notifiable disease. The objective of this report is to analyze official surveillance data on animal listeriosis in Germany from 2024 to 2025 to assess the epidemiological situation and the spatiotemporal distribution of this One Health relevant disease. A total of 341 notifications involving 652 affected animals were reported. The highest number of cases occurred in food-producing animals, particularly cattle, followed by sheep and goats. Listeriosis has also been reported in pet animals, wildlife, and poultry. The epidemiological situation and geographical distribution of the disease have remained consistent over the past decade, with the highest incidences in Berlin, Bavaria, and Baden-Wuerttemberg. Listeriae cause disease in a broad range of hosts nationwide. Monitoring listeriosis in animals is crucial for public health and the safety of the food supply. Systematic collection of animal isolates is essential to understand transmission from environmental reservoirs to humans—a currently puzzling link. This knowledge is vital for protecting human health.
Explainable artificial intelligence (XAI) techniques, particularly Shapley additive explanations (SHAP), are essential for interpreting ensemble tree-based models in critical areas such as healthcare. However, SHAP values are often treated as point estimates that neglect uncertainty originating from aleatoric (irreducible noise) and epistemic (lack of data) sources. This work introduces an approach that decomposes SHAP value uncertainty into aleatoric, epistemic, and entanglement components. This approach employs Dempster-Shafer evidence theory and Dirichlet process (DP) hypothesis sampling over tree ensembles. The use-case validation reveals insights into epistemic uncertainty within SHAP explanations, enhancing the reliability and interpretability of SHAP attributions. This informs robust decision-making and model refinement. Our findings suggest that reducing epistemic uncertainty requires improved data quality and model development techniques. Tree-based models, particularly bagging, are effective in quantifying such uncertainties.
During the SARS-CoV-2 pandemic, wastewater-based genomic surveillance (WWGS) emerged as an efficient viral surveillance tool that takes into account asymptomatic cases and can identify known and novel mutations and offers the opportunity to assign known virus lineages based on the detected mutations profiles. WWGS can also hint towards novel or cryptic lineages, but it is difficult to clearly identify and define novel lineages from wastewater (WW) alone. While WWGS has significant advantages in monitoring SARS-CoV-2 viral spread, technical challenges remain, including poor sequencing coverage and quality due to viral RNA degradation. As a result, the viral RNAs in wastewater have low concentrations and are often fragmented, making sequencing difficult. WWGS analysis requires advanced computational tools that are yet to be developed and benchmarked. The existing bioinformatics tools used to analyze wastewater sequencing data are often based on previously developed methods for quantifying the expression of transcripts or viral diversity. Those methods were not developed for wastewater sequencing data specifically, and are not optimized to address unique challenges associated with wastewater. While specialized tools for analysis of wastewater sequencing data have also been developed recently, it remains to be seen how they will perform given the ongoing evolution of SARS-CoV-2 and the decline in testing and patient-based genomic surveillance. Here, we discuss opportunities and challenges associated with WWGS, including sample preparation, sequencing technology, and bioinformatics methods.
