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Automated assays for detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies in coronavirus disease 2019 (COVID-19) diagnostics have recently come available. We compared the performance of the Elecsys® Anti–SARS-CoV-2 and LIAISON® SARS-CoV-2 S1/S2 IgG tests. The seroconversion panel comprised of 120 samples from 13 hospitalized COVID-19 patients. For the sensitivity and specificity testing, samples from COVID-19 outpatients >15 days after positive nucleic acid amplification test (NAAT) result ( n = 35) and serum control samples collected before the COVID-19 era ( n = 161) were included in the material. Samples for the detection of possible cross-reactions were also tested. Based on our results, the SARS-CoV-2 antibodies can be quite reliably detected 2 weeks after NAAT positivity and 3 weeks after the symptom onset with both tests. However, since some COVID-19 patients were positive only with Elecsys®, the antibodies should be screened against N-antigen (Elecsys®) and reactive samples confirmed with S antigen (LIAISON®), but both results should be reported. In some COVID-19 patients, the serology can remain negative.

We evaluated a rapid antigen test against SARS-CoV-2 virus (Roche-SD Biosensor; RSDB-RAT) in children and adults with respiratory symptoms compared to those with non-respiratory symptoms or asymptomatic. Also the performance of RSDB-RAT with respect to the duration of respiratory symptoms was assessed. A viral cross-reactivity panel was included. RSDB-RAT was reliable in detecting SARS-CoV-2 in children and adults if the respiratory symptoms had endured 1-7 days. If the respiratory symptoms had lasted less than 1 day, the sensitivity was significantly lower. No cross-reactivity with other respiratory viruses was observed.

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Two SARS-CoV-2 Variants of Concern, Alpha (~ 80%) and Beta (~ 23%) rapidly became dominant in Finland in the spring of 2021 but diminished near summer. To assess their temporal epidemiological dynamics among Finnish cases, we began large-scale sequencing efforts to identify spreading events and sources via phylogenetic clustering analyses. The results show the majority belonged to clusters spreading in the community while few sequenced samples were singletons. The results highlight the importance of surveillance and preventative policies in controlling the epidemic.