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Background Lower respiratory tract infections are among the main causes of death. Although there are many respiratory viruses, diagnostic efforts are focused mainly on influenza. The Respiratory Viruses Network (RespVir) collects infection data, primarily from German university hospitals, for a high diversity of infections by respiratory pathogens. In this study, we computationally analysed a subset of the RespVir database, covering 217,150 samples tested for 17 different viral pathogens in the time span from 2010 to 2019. Methods We calculated the prevalence of 17 respiratory viruses, analysed their seasonality patterns using information-theoretic measures and agglomerative clustering, and analysed their propensity for dual infection using a new metric dubbed average coinfection exclusion score (ACES). Results After initial data pre-processing, we retained 206,814 samples, corresponding to 1,408,657 performed tests. We found that Influenza viruses were reported for almost the half of all infections and that they exhibited the highest degree of seasonality. Coinfections of viruses are frequent; the most prevalent coinfection was rhinovirus/bocavirus and most of the virus pairs had a positive ACES indicating a tendency to exclude each other regarding infection. Conclusions The analysis of respiratory viruses dynamics in monoinfection and coinfection contributes to the prevention, diagnostic, treatment, and development of new therapeutics. Data obtained from multiplex testing is fundamental for this analysis and should be prioritized over single pathogen testing.

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Background: The B-FAST project of the National University Network (NUM) examines and records applied surveillance strategies implemented in hospitals i.a., to protect patients and employees from SARS-CoV-2 infection.Methods: Infection control physicians in German university hospitals (UK), as well as non-university hospitals (NUK; Bavaria, Lower Saxony) were surveyed in March 2021 regarding SARS-CoV-2 testing/surveillance strategies in a cross-sectional study using a standardized online questionnaire. The focus was on screening strategies taking into account the "test" methods used (case history, PCR, antigen, antibody test).Results: The response rate was 91.7% (33/36) in UK and 11.3%-32.2% in NUK. Almost all hospitals (95.0%) performed a symptom and exposure check and/or testing upon inpatient admission. Non-cause-related testing (screening) of health care workers in COVID wards was preferably done by PCR in UK (69.7% PCR; 12.1% antigen), while NUK (29.9% PCR; 49.3% antigen) used antigen testing more frequently. Regardless of the type of facility, about half of the respondents rated the benefit of screening higher than the effort (patients: 49%; employees: 45%).Conclusion: Testing/surveillance strategies find a high level of acceptance at German hospitals and are generally carried out in accordance with the national testing strategy with differences depending on the level of care. Hintergrund: Im "Bundesweiten Forschungsnetzwerk zur Angewandten Surveillance und Testung" (B-FAST) werden anwendungsbezogene Strategien geprüft und erfasst, die u.a. in Krankenhäusern zum Schutz von Patient*innen und Beschäftigten vor SARS-CoV-2 implementiert wurden.Methoden: Im März 2021 wurden in einer Querschnittstudie mit einem standardisierten Online-Fragebogen die leitenden (Krankenhaus-)Hygieniker*innen in deutschen Universitätskliniken (UK), sowie Nicht-Universitätskliniken (NUK) in Bayern und Niedersachsen zu SARS-CoV-2 Test- und Surveillance-Strategien befragt. Im Fokus standen Screening-Strategien unter Berücksichtigung der eingesetzten "Test"-Methoden (Anamnese, PCR-, Antigen-, Antikörpertest).Ergebnisse: Der Rücklauf liegt bei 91,7% (33 von 36) aus den UK, bei 32,2% (37 von 115) aus den niedersächsischen NUK und bei 11,3% (30 von 265) aus den bayrischen NUK. Nahezu alle Kliniken (95,0%) führen bei der Aufnahme stationärer Patient*innen einen Symptom/Expositionscheck und/oder eine Testung durch. Das nicht-anlassbezogene Testen (Screening) von Beschäftigten im COVID-Bereich erfolgte in UK (69,7% PCR; 12,1% Antigen) bevorzugt durch PCR, während NUK (29,9% PCR; 49,3% Antigen) häufiger Antigentests einsetzten. Unabhängig von der Einrichtungsart bewerten etwa die Hälfte der Befragten den Nutzen von Screening höher als den Aufwand (bei Patient*innen 49,0%; bei Beschäftigten 45,0%).Fazit: Test- und Surveillance-Strategien finden an deutschen Kliniken hohe Akzeptanz und werden grundsätzlich in Anlehnung an die nationale Teststrategie - mit Unterschieden je nach Versorgungsstufe - durchgeführt.

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine. © 2021, The Author(s).

Background Bloodstream infections (BSI) caused by Enterobacteriaceae show increasing frequency of resistance to third-generation cephalosporin (3GC) antibiotics on the African continent but the mortality impact has not been quantified. Methods We used historic data from six African hospitals to assess the impact of 3GC resistance on clinical outcomes in Escherichia coli and Klebsiella pneumoniae BSI. We matched each bacteraemic patient to two uninfected patients. We compared outcomes between 3GC-susceptible and 3GC-resistant BSI and their respective uninfected controls using Cox regression models. Results For 1431 E. coli BSI patients, we matched 1152 (81%) 3GC-susceptible and 279 (19%) 3GC-resistant cases to 2263 and 546 uninfected inpatient controls. For 1368 K. pneumoniae BSI patients, we matched 502 (37%) 3GC-susceptible and 866 (63%) 3GC-resistant cases to 982 and 1656 uninfected inpatient controls. We found that 3GC-resistant E. coli had similar hazard ratios (HRs) for in-hospital mortality over their matched controls as compared to susceptible infections over their controls (ratio of HRs 1.03, 95% CI 0.73–1.46). Similarly, 3GC-resistance in K. pneumoniae BSI was not associated with mortality (ratio of HR 1.10, 95% CI 0.80–1.52). Estimates of mortality impact varied by site without a consistent pattern. Conclusions In a retrospective analysis, including the use of matched uninfected patients, there did not appear to be an impact of 3GC-resistance on mortality in E. coli or K. pneumoniae BSI in African hospitals, as compared with susceptible BSI with equivalent species. Better information on the actual use of antibiotics in treating infections in African hospitals would improve these impact estimates.

Over the last decade, carbapenemase-resistant Enterobacterales(Enterobacteriaceae) most notable Klebsiella pneumoniae contributed the fastest increasing burden of disease in terms of numbers of infections and numbers of deaths across Europe. Public health interventions to control this current epidemic[for full text, please go to the a.m. URL], Bad Honnef-Symposium 2019