(1) Background: Streptococcus dysgalactiae subspecies equisimilis (SDSE) is an important β-hemolytic pathogen historically described as mainly affecting animals. Studies epidemiologically assessing the pathogenicity in the human population in Germany are rare. (2) Methods: the present study combines national surveillance data from 2010 to 2022 with a single-center clinical study conducted from 2016 to 2022, focusing on emm type, Lancefield antigen, antimicrobial resistance, patient characteristics, disease severity, and clinical infection markers. (3) Results: The nationwide reported invasive SDSE infections suggest an increasing infection burden for the German population. One particular emm type, stG62647, increased over the study period, being the dominant type in both study cohorts, suggesting a mutation-driven outbreak of a virulent clone. The patient data show that men were more affected than women, although in the single-center cohort, this trend was reversed for patients with stG62647 SDSE. Men affected by stG62647 developed predominantly fascial infections, whereas women suffering from superficial and fascial non-stG62647 SDSE infections were significantly younger than other patients. Increasing age was a general risk factor for invasive SDSE infections. (4) Conclusions: further studies are needed to further elucidate the raised questions regarding outbreak origin, underlying molecular mechanisms as well as sex-dependent pathogen adaptation.

(1) Background: to describe the dynamic of the pandemic across 35 European countries over a period of 9 months. (2) Methods: a three-phase time series model was fitted for 35 European countries, predicting deaths based on SARS-CoV-2 incidences. Hierarchical clustering resulted in three clusters of countries. A multiple regression model was developed predicting thresholds for COVID-19 incidences, coupled to death numbers. (3) Results: The model showed strongly connected deaths and incidences during the waves in spring and fall. The corrected case-fatality rates ranged from 2% to 20.7% in the first wave, and from 0.5% to 4.2% in the second wave. If the incidences stay below a threshold, predicted by the regression model ($R^2=85.0\%$), COVID-19 related deaths and incidences were not necessarily coupled. The clusters represented different regions in Europe, and the corrected case-fatality rates in each cluster flipped from high to low or vice versa. Severely and less severely affected countries flipped between the first and second wave. (4) Conclusions: COVID-19 incidences and related deaths were uncoupled during the summer but coupled during two waves. Once a country-specific threshold of infections is reached, death numbers will start to rise, allowing health care systems and countries to prepare.

(1) Background: The aim of our study was to identify specific risk factors for fatal outcome in critically ill COVID-19 patients. (2) Methods: Our data set consisted of 840 patients enclosed in the LEOSS registry. Using lasso regression for variable selection, a multifactorial logistic regression model was fitted to the response variable survival. Specific risk factors and their odds ratios were derived. A nomogram was developed as a graphical representation of the model. (3) Results: 14 variables were identified as independent factors contributing to the risk of death for critically ill COVID-19 patients: age (OR 1.08, CI 1.06–1.10), cardiovascular disease (OR 1.64, CI 1.06–2.55), pulmonary disease (OR 1.87, CI 1.16–3.03), baseline Statin treatment (0.54, CI 0.33–0.87), oxygen saturation (unit = 1%, OR 0.94, CI 0.92–0.96), leukocytes (unit 1000/$\mathsf{\mu }$L, OR 1.04, CI 1.01–1.07), lymphocytes (unit 100/$\mathsf{\mu }$L, OR 0.96, CI 0.94–0.99), platelets (unit 100,000/$\mathsf{\mu }$L, OR 0.70, CI 0.62–0.80), procalcitonin (unit ng/mL, OR 1.11, CI 1.05–1.18), kidney failure (OR 1.68, CI 1.05–2.70), congestive heart failure (OR 2.62, CI 1.11–6.21), severe liver failure (OR 4.93, CI 1.94–12.52), and a quick SOFA score of 3 (OR 1.78, CI 1.14–2.78). The nomogram graphically displays the importance of these 14 factors for mortality. (4) Conclusions: There are risk factors that are specific to the subpopulation of critically ill COVID-19 patients.

(1) Background: Between March 2020 and January 2022 severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) caused five infection waves in Europe. The first and the second wave was caused by wildtype SARS-CoV-2, while the following waves were caused by the variants of concern Alpha, Delta, and Omicron respectively. (2) Methods: In the present analysis, the first four waves were compared in Germany and the UK, in order to examine the COVID-19 epidemiology and its modulation by non-pharmaceutical interventions (NPI). (3) Results: The number of COVID-19 patients on intensive care units and the case fatality rate were used to estimate disease burden, the excess mortality to assess the net effect of NPI and other measures on the population. The UK was more severely affected by the first and the third wave while Germany was more affected by the second wave. The UK had a higher excess mortality during the first wave, afterwards the excess mortality in both countries was nearly identical. While most NPI were lifted in the UK in July 2021, the measures were kept and even aggravated in Germany. Nevertheless, in autumn 2021 Germany was much more affected, nearly resulting in a balanced sum of infections and deaths compared to the UK. Within the whole observation period, in Germany the number of COVID-19 patients on ICUs was up to four times higher than in the UK. Our results show that NPI have a limited effect on COVID-19 burden, seasonality plays a crucial role, and a higher virus circulation in a pre-wave situation could be beneficial. (4) Conclusions: Although Germany put much more effort and resources to fight the pandemic, the net balance of both countries was nearly identical, questioning the benefit of excessive ICU treatments and of the implementation of NPI, especially during the warm season.

Previously it was shown that application of probiotics stopped the acquisition of vancomycin-resistant Enterococcus faecium (VRE) by patients in an early rehabilitation ward. Once the application of probiotics ended, we examined whether acquisition of VRE reoccurred. Furthermore, we examined whether probiotics altered prevalence of vancomycin-susceptible E. faecium (VSE) and Gram-negative bacteria, which produce extended spectrum beta-lactamase (ESBL). Although probiotic application ceased in April 2018, VRE-colonized patients rarely presented on that ward until 2019. Probiotic treatment also resulted in a decreased number of patients with VSE and ESBL. While decreased incidence of VRE occurred immediately, decreased VSE and ESBL numbers occurred months later. A probiotic-mediated decrease of VSE and ESBL incidence cannot be explained when assuming bacterial transmission exclusively as a linear cause and effect event. The decrease is better understood by considering bacterial transmissions to be stochastic events, which depend on various driving forces similar to an electric current. We hypothesize that VRE, VSE and ESBL uptake by patients and by staff members mutually reinforced each other, leading staff members to form a bacterial reservoir, similar to a condenser that stores electrical energy. Probiotic treatment then inhibited regeneration of that store, resulting in a breakdown of the driving force.