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Background Serological immunoassays that can identify protective immunity against SARS‐CoV‐2 are needed to adapt quarantine measures, assess vaccination responses, and evaluate donor plasma. To date, however, the utility of such immunoassays remains unclear. In a mixed‐design evaluation study, we compared the diagnostic accuracy of serological immunoassays that are based on various SARS‐CoV‐2 proteins and assessed the neutralizing activity of antibodies in patient sera. Methods Consecutive patients admitted with confirmed SARS‐CoV‐2 infection were prospectively followed alongside medical staff and biobank samples from winter 2018/2019. An in‐house enzyme‐linked immunosorbent assay utilizing recombinant receptor‐binding domain (RBD) of the SARS‐CoV‐2 spike protein was developed and compared to three commercially available enzyme‐linked immunosorbent assays (ELISAs) targeting the nucleoprotein (N), the S1 domain of the spike protein (S1) and a lateral flow immunoassay (LFI) based on full‐length spike protein. Neutralization assays with live SARS‐CoV‐2 were performed. Results One‐thousand four‐hundred and seventy‐seven individuals were included comprising 112 SARS‐CoV‐2 positives (defined as a positive real‐time PCR result; prevalence 7.6%). IgG seroconversion occurred between day 0 and day 21. While the ELISAs showed sensitivities of 88.4% for RBD, 89.3% for S1, and 72.9% for N protein, the specificity was above 94% for all tests. Out of 54 SARS‐CoV‐2 positive individuals, 96.3% showed full neutralization of live SARS‐CoV‐2 at serum dilutions ≥1:16, while none of the 6 SARS‐CoV‐2 negative sera revealed neutralizing activity. Conclusions ELISAs targeting RBD and S1 protein of SARS‐CoV‐2 are promising immunoassays which shall be further evaluated in studies verifying diagnostic accuracy and protective immunity against SARS‐CoV‐2.

Objective: From January 1, 2018, until July 31, 2020, our hospital network experienced an outbreak of vancomycin-resistant enterococci (VRE). The goal of our study was to improve existing processes by applying machine-learning and graph-theoretical methods to a nosocomial outbreak investigation.Methods: We assembled medical records generated during the first 2 years of the outbreak period (January 2018 through December 2019). We identified risk factors for VRE colonization using standard statistical methods, and we extended these with a decision-tree machine-learning approach. We then elicited possible transmission pathways by detecting commonalities between VRE cases using a graph theoretical network analysis approach.Results: We compared 560 VRE patients to 86,684 controls. Logistic models revealed predictors of VRE colonization as age (aOR, 1.4 (per 10 years), with 95% confidence interval [CI], 1.3-1.5; P < .001), ICU admission during stay (aOR, 1.5; 95% CI, 1.2-1.9; P < .001), Charlson comorbidity score (aOR, 1.1; 95% CI, 1.1-1.2; P < .001), the number of different prescribed antibiotics (aOR, 1.6; 95% CI, 1.5-1.7; P < .001), and the number of rooms the patient stayed in during their hospitalization(s) (aOR, 1.1; 95% CI, 1.1-1.2; P < .001). The decision-tree machinelearning method confirmed these findings. Graph network analysis established 3 main pathways by which the VRE cases were connected: healthcare personnel, medical devices, and patient rooms.Conclusions: We identified risk factors for being a VRE carrier, along with 3 important links with VRE (healthcare personnel, medical devices, patient rooms). Data science is likely to provide a better understanding of outbreaks, but interpretations require data maturity, and potential confounding factors must be considered.