Diagnostic performance of rapid antigen tests (RAT) for COVID-19 and factors associated with RAT-negative results among RT-PCR-positive individuals during Omicron BA.2, BA.5 and XBB.1 predominance.

BACKGROUND: While numerous studies have evaluated the real-world performance of rapid antigen tests (RATs), data on the effect of Omicron sublineages such as XBB and reinfections on RAT performance is limited. We assessed the performance of RATs and factors associated with RAT-negative results among individuals who tested SARS-CoV-2-positive by reverse transcription-polymerase chain reaction (RT-PCR). METHODS: We conducted a retrospective study among Singapore residents who underwent testing for SARS-CoV-2 with RAT (Acon Flowflex or SD Biosensor) and RT-PCR in the same clinical encounter between 9 May 2022 and 21 November 2022. RT-PCR served as a reference standard for RAT performance. Logistic regression was used to estimate the odds ratios (OR) of factors associated with negative RAT results among RT-PCR-positive cases. RESULTS: Of 8,620 clinical encounters analysed, 3,519 (40.8%) were SARS-CoV-2-positive on RT-PCR. Overall sensitivity and specificity of RAT was 84.6% (95% CI 83.3-85.7%) and 99.4% (95% CI 99.1-99.6%) respectively. Acon Flowflex consistently achieved higher sensitivity and specificity than SD Biosensor test kit. Among RT-PCR-positive cases, individuals who had a previous documented SARS-CoV-2 infection, coinfection with another respiratory pathogen or tested ≥ 6 days from symptom onset had higher odds of testing RAT-negative, but the associations were attenuated after adjustment for cycle threshold values (proxy for viral load). There was no significant difference in RAT performance between Omicron sublineages BA.2, BA.5 and XBB.1. CONCLUSION: Diagnostic performance of RAT was not affected by changes in predominant circulating Omicron sublineages. However, reinfection cases may be under ascertained by RAT. In individuals with a previous SARS-CoV-2 infection episode or symptom onset ≥ 6 days prior to testing, a confirmatory RT-PCR may be considered if there is high clinical suspicion.

Differences in virus and immune dynamics for SARS-CoV-2 Delta and Omicron infections by age and vaccination histories.

Vaccination against COVID-19 was integral to controlling the pandemic that persisted with the continuous emergence of SARS-CoV-2 variants. Using a mathematical model describing SARS-CoV-2 within-host infection dynamics, we estimate differences in virus and immunity due to factors of infecting variant, age, and vaccination history (vaccination brand, number of doses and time since vaccination). We fit our model in a Bayesian framework to upper respiratory tract viral load measurements obtained from cases of Delta and Omicron infections in Singapore, of whom the majority only had one nasopharyngeal swab measurement. With this dataset, we are able to recreate similar trends in URT virus dynamics observed in past within-host modelling studies fitted to longitudinal patient data.We found that Omicron had higher R0,within values than Delta, indicating greater initial cell-to-cell spread of infection within the host. Moreover, heterogeneities in infection dynamics across patient subgroups could be recreated by fitting immunity-related parameters as vaccination history-specific, with or without age modification. Our model results are consistent with the notion of immunosenescence in SARS-CoV-2 infection in elderly individuals, and the issue of waning immunity with increased time since last vaccination. Lastly, vaccination was not found to subdue virus dynamics in Omicron infections as well as it had for Delta infections.This study provides insight into the influence of vaccine-elicited immunity on SARS-CoV-2 within-host dynamics, and the interplay between age and vaccination history. Furthermore, it demonstrates the need to disentangle host factors and changes in pathogen to discern factors influencing virus dynamics. Finally, this work demonstrates a way forward in the study of within-host virus dynamics, by use of viral load datasets including a large number of patients without repeated measurements.

Serial Intervals and Incubation Periods of SARS-CoV-2 Omicron and Delta Variants, Singapore.

We compared serial intervals and incubation periods for SARS-CoV-2 Omicron BA.1 and BA.2 subvariants and Delta variants in Singapore. Median incubation period was 3 days for BA.1 versus 4 days for Delta. Serial interval was 2 days for BA.1 and 3 days for BA.2 but 4 days for Delta.

Identifying COVID-19 cases in outpatient settings.

Case identification is an ongoing issue for the COVID-19 epidemic, in particular for outpatient care where physicians must decide which patients to prioritise for further testing. This paper reports tools to classify patients based on symptom profiles based on 236 severe acute respiratory syndrome coronavirus 2 positive cases and 564 controls, accounting for the time course of illness using generalised multivariate logistic regression. Significant symptoms included abdominal pain, cough, diarrhoea, fever, headache, muscle ache, runny nose, sore throat, temperature between 37.5 and 37.9 °C and temperature above 38 °C, but their importance varied by day of illness at assessment. With a high percentile threshold for specificity at 0.95, the baseline model had reasonable sensitivity at 0.67. To further evaluate accuracy of model predictions, leave-one-out cross-validation confirmed high classification accuracy with an area under the receiver operating characteristic curve of 0.92. For the baseline model, sensitivity decreased to 0.56. External validation datasets reported similar result. Our study provides a tool to discern COVID-19 patients from controls using symptoms and day from illness onset with good predictive performance. It could be considered as a framework to complement laboratory testing in order to differentiate COVID-19 from other patients presenting with acute symptoms in outpatient care.