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The emergence and rapid spread of SARS-CoV-2 variants may affect vaccine efficacy substantially. The Omicron variant termed BA.2, which differs substantially from BA.1 based on genetic sequence, is currently replacing BA.1 in several countries, but its antigenic characteristics have not yet been assessed. Here, we used antigenic cartography to quantify and visualize antigenic differences between early SARS-CoV-2 variants (614G, Alpha, Beta, Gamma, Zeta, Delta, and Mu) using hamster antisera obtained after primary infection. We first verified that the choice of the cell line for the neutralization assay did not affect the topology of the map substantially. Antigenic maps generated using pseudo-typed SARS-CoV-2 on the widely used VeroE6 cell line and the human airway cell line Calu-3 generated similar maps. Maps made using authentic SARS-CoV-2 on Calu-3 cells also closely resembled those generated with pseudo-typed viruses. The antigenic maps revealed a central cluster of SARS-CoV-2 variants, which grouped on the basis of mutual spike mutations. Whereas these early variants are antigenically similar, clustering relatively close to each other in antigenic space, Omicron BA.1 and BA.2 have evolved as two distinct antigenic outliers. Our data show that BA.1 and BA.2 both escape vaccine-induced antibody responses as a result of different antigenic characteristics. Thus, antigenic cartography could be used to assess antigenic properties of future SARS-CoV-2 variants of concern that emerge and to decide on the composition of novel spike-based (booster) vaccines.

Background Performance of the SD Biosensor saliva antigen rapid test was evaluated at a large designated testing site in non-hospitalized patients, with or without symptoms. Method All eligible people over 18 years of age presenting for a booked appointment at the designated SARS-CoV-2 testing site were approached for inclusion and enrolled following verbal informed consent. One nasopharyngeal swab was taken to carry out the default antigen rapid test from which the results were reported back to the patient and one saliva sample was self-taken according to verbal instruction on site. This was used for the saliva antigen rapid test, the RT-PCR and for virus culture. Sensitivity of the saliva antigen rapid test was analyzed in two ways: i, compared to saliva RT-PCR; and ii, compared to virus culture of the saliva samples. Study participants were also asked to fill in a short questionnaire stating age, sex, date of symptom onset. Recommended time of ≥30mins since last meal, drink or cigarette if applicable was also recorded. The study was carried out in February-March 2021 for 4 weeks. Results We could include 789 people with complete records and results. Compared to saliva RT-PCR, overall sensitivity and specificity of the saliva antigen rapid test was 66.1% and 99.6% which increased to 88.6% with Ct ≤30 cutoff. Analysis by days post onset did not result in higher sensitivities because the large majority of people were in the very early phase of disease ie Conclusion Overall, the potential benefits of saliva antigen rapid test, could outweigh the lower sensitivity compared to nasopharyngeal antigen rapid test in a comprehensive testing strategy, especially for home/self-testing and in vulnerable populations like elderly, disabled or children where in intrusive testing is either not possible or causes unnecessary stress.

Background SARS-CoV-2 RT-PCR assays are more sensitive than rapid antigen detection assays (RDT) and can detect viral RNA even after an individual is no longer infectious. RDT can reduce the time to test and the results might better correlate with infectiousness. Aim We assessed the ability of five RDT to identify infectious COVID-19 cases and systematically recorded the turnaround time of RT-PCR testing. Methods Sensitivity of RDT was determined using a serially diluted SARS-CoV-2 stock with known viral RNA concentration. The probability of detecting infectious virus at a given viral load was calculated using logistic regression of viral RNA concentration and matched culture results of 78 specimens from randomly selected non-hospitalised cases. The probability of each RDT to detect infectious cases was calculated as the sum of the projected probabilities for viral isolation success for every viral RNA load found at the time of diagnosis in 1,739 confirmed non-hospitalised COVID-19 cases. Results The distribution of quantification cycle values and estimated RNA loads for patients reporting to drive-through testing was skewed to high RNA loads. With the most sensitive RDT (Abbott and SD Biosensor), 97.30% (range: 88.65–99.77) of infectious individuals would be detected. This decreased to 92.73% (range: 60.30–99.77) for Coris BioConcept and GenBody, and 75.53% (range: 17.55–99.77) for RapiGEN. Only 32.9% of RT-PCR results were available on the same day as specimen collection. Conclusion The most sensitive RDT detected infectious COVID-19 cases with high sensitivity and may considerably improve containment through more rapid isolation and contact tracing.