Flood damage estimation is a core task in flood risk assessments and requires reliable flood loss models. Identifying the driving factors of flood loss at residential buildings and gaining insight into their relations is important to improve our understanding of flood damage processes. For that purpose, we learn probabilistic graphical models, which capture and illustrate (in‐)dependencies between the considered variables. The models are learned based on postevent surveys with flood‐affected residents after six flood events, which occurred in Germany between 2002 and 2013. Besides the sustained building damage, the survey data contain information about flooding parameters, early warning and emergency measures, property‐level mitigation measures and preparedness, socioeconomic characteristics of the household, and building characteristics. The analysis considers the entire data set with a total of 4,468 cases as well as subsets of the data set partitioned into single flood events and flood types: river floods, levee breaches, surface water flooding, and groundwater floods, to reveal differences in the damaging processes. The learned networks suggest that the flood loss ratio of residential buildings is directly influenced by hydrological and hydraulic aspects as well as by building characteristics and property‐level mitigation measures. The study demonstrates also that for different flood events and process types the building damage is influenced by varying factors. This suggests that flood damage models need to be capable of reproducing these differences for spatial and temporal model transfers.

In view of globally increasing flood losses, a significantly improved and more efficient flood risk management and adaptation policy are needed. One prerequisite is reliable risk assessments on the continental scale. Flood loss modeling and risk assessments for Europe are until now based on regional approaches using deterministic depth-damage functions. Uncertainties associated with the risk estimation are hardly known. To reduce these shortcomings, we present a novel, consistent approach for probabilistic flood loss modeling for Europe, based on the upscaling of the Bayesian Network Flood Loss Estimation MOdel for the private sector, BN-FLEMOps. The model is applied on the mesoscale in the whole of Europe and can be adapted to regional situations. BN-FLEMOps is validated in three case studies in Italy, Austria, and Germany. The officially reported loss figures of the past flood events are within the 95% quantile range of the probabilistic loss estimation, for all three case studies. In the Italian, Austrian, and German case studies, the median loss estimate shows an overestimation by 28% (2.1 million euro) and 305% (5.8 million euro) and an underestimation by 43% (104 million euro), respectively. In two of the three case studies, the performance of the model improved, when updated with empirical damage data from the area of interest. This approach represents a step forward in European wide flood risk modeling, since it delivers consistent flood loss estimates and inherently provides uncertainty information. Further validation and tests with respect to adapting the model to different European regions are recommended. Plain Language SummaryWe present a novel, probabilistic approach to calculate flood losses for residential buildings in Europe. We show how the approach can be applied to the European scale and which data sets are required for the calculation. The validity of the approach is proven on the basis of three historic flood events in Italy, Austria, and Germany. This approach which delivers consistent flood loss estimates including uncertainty information supports more efficient flood risk management and adaptation policy in Europe.

Background Five SARS-CoV-2 variants are currently considered as variants of concern (VOC). Omicron was declared a VOC at the end of November 2021. Based on different diagnostic methods, the occurrence of Omicron was reported by 52 countries worldwide on December 7 2021. First notified by South Africa with alarming reports on increasing infection rates, this new variant was soon suspected to replace the currently pre-dominating Delta variant leading to further infection waves worldwide. Methods Using VOC PCR screening and Next Generation Sequencing (NGS) analysis of selected samples, we investigated the circulation of Omicron in the German federal state Bavaria. For this, we analyzed SARS-CoV-2 surveillance data from our laboratory generated from calendar week (CW) 01 to 49/2021. Results So far, we have detected 69 Omicron cases in our laboratory from CW 47–49/2021 using RT-qPCR followed by melting curve analysis. The first 16 cases were analyzed by NGS and all were confirmed as Omicron. Conclusion Our data strongly support no circulation of the new Omicron variant before CW 47/2021.

Purpose Omicron is rapidly spreading as a new SARS-CoV-2 variant of concern (VOC). The question whether this new variant has an impact on SARS-CoV-2 rapid antigen test (RAT) performance is of utmost importance. To obtain an initial estimate regarding differences of RATs in detecting omicron and delta, seven commonly used SARS-CoV-2 RATs from different manufacturers were analysed using cell culture supernatants and clinical specimens. Methods For this purpose, cell culture-expanded omicron and delta preparations were serially diluted in Dulbecco’s modified Eagle’s Medium (DMEM) and the Limit of Detection (LoD) for both VOCs was determined. Additionally, clinical specimens stored in viral transport media or saline (n = 51) were investigated to complement in vitro results with cell culture supernatants. Ct values and RNA concentrations were determined via quantitative reverse transcription polymerase chain reaction (RT-qPCR). Results The in vitro determination of the LoD showed no obvious differences in detection of omicron and delta for the RATs examined. The LoD in this study was at a dilution level of 1:1,000 (corresponding to 3.0—5.6 × 106 RNA copies/mL) for tests I–V and at a dilution level of 1:100 (corresponding to 3.7—4.9 × 107 RNA copies/mL) for tests VI and VII. Based on clinical specimens, no obvious differences were observed between RAT positivity rates when comparing omicron to delta in this study setting. Overall positivity rates varied between manufacturers with 30–81% for omicron and 42–71% for delta. Test VII was only conducted in vitro with cell culture supernatants for feasibility reasons. In the range of Ct < 23, positivity rates were 50–100% for omicron and 67–93% for delta. Conclusion In this study, RATs from various manufacturers were investigated, which displayed no obvious differences in terms of analytical LoD in vitro and RAT positivity rates based on clinical samples comparing the VOCs omicron and delta. However, differences between tests produced by various manufacturers were detected. In terms of clinical samples, a focus of this study was on specimens with high virus concentrations. Further systematic, clinical and laboratory studies utilizing large datasets are urgently needed to confirm reliable performance in terms of sensitivity and specificity for all individual RATs and SARS-CoV-2 variants.