RESUMO
Rapid antigen detection tests (RADTs) for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are now in widespread use in the United States. RADTs play an important role in maintaining an open society but require periodic reassessment to ensure test performance remains intact as the virus evolves. The nucleocapsid (N) protein is the target for the majority of RADTs and the SARS-CoV-2 Omicron variant has several N protein mutations that are previously uncharacterized. We sought to assess the impact of these mutations by testing 30 Omicron variant samples across a wide range of viral loads on three widely used RADTs: the iHealth COVID-19 Antigen Rapid Test, the ACON Laboratories FlowFlex COVID-19 Antigen Home Test, and the Abbott BinaxNOW COVID-19 Antigen Card, using 30 Delta variant samples as a comparator. We found no change in the analytic sensitivity of all three RADTs for detection of Omicron versus Delta, but noted differences in performance between assays. No RADT was able to detect samples with a cycle threshold (Ct) value of [≥]27.5 for the envelope gene target on the Roche cobas RT-PCR assay. Epidemiologic studies are necessary to correlate these findings with their real-world performance.
RESUMO
The emergence of the SARS-CoV-2 Omicron variant has motivated a re-evaluation of the test characteristics for lateral flow immunochromatographic assays (LFIAs), commonly referred to as rapid antigen tests. To address this need, we evaluated the analytic sensitivity of one of the most widely used LFIAs in the US market, the Abbott BinaxNOW COVID-19 Ag At-Home Card using 32 samples of Omicron and 30 samples of the Delta variant. Samples were chosen to intentionally over-represent the range of viral loads where differences are most likely to appear. We found no changes in the analytic sensitivity of the BinaxNOW assay by variant even after controlling for variation in cycle threshold values in the two populations. Similar to prior studies, the sensitivity of the assay is highly dependent on the amount of virus present in the sample. While the analytic sensitivity of the BinaxNOW LFIA remains intact versus the Omicron variant, its clinical sensitivity is influenced by the interaction between viral replication, the dynamics of tissue tropism and the timing of sampling. Further research is necessary to optimally adapt current testing strategies to robustly detect early infection by the Omicron variant to prevent transmission.
RESUMO
To assess the impact of the SARS-CoV-2 pandemic on seasonal respiratory viruses, absolute case counts and viral reproductive rates from 2019-2020 were compared against previous seasons. Our findings suggest that the public health measures implemented to reduce SARS-CoV-2 transmission significantly reduced the transmission of other respiratory viruses.
RESUMO
Virologic testing for SARS-CoV-2 has been central to the COVID-19 pandemic response, but interpreting changes in incidence and fraction of positive tests towards understanding the epidemic trajectory is confounded by changes in testing practices. Here, we show that the distribution of viral loads, in the form of Cycle thresholds (Ct), from positive surveillance samples at a single point in time can provide accurate estimation of an epidemics trajectory, subverting the need for repeated case count measurements which are frequently obscured by changes in testing capacity. We identify a relationship between the population-level cross-sectional distribution of Ct values and the growth rate of the epidemic, demonstrating how the skewness and median of detectable Ct values change purely as a mathematical epidemiologic rule without any change in individual level viral load kinetics or testing. Although at the individual level measurement variation can complicate interpretation of Ct values for clinical use, we show that population-level properties reflect underlying epidemic dynamics. In support of these theoretical findings, we observe a strong relationship between the time-varying effective reproductive number, R(t), and the distribution of Cts among positive surveillance specimens, including median and skewness, measured in Massachusetts over time. We use the observed relationships to derive a novel method that allows accurate inference of epidemic growth rate using the distribution of Ct values observed at a single cross-section in time, which, unlike estimates based on case counts, is less susceptible to biases from delays in test results and from changing testing practices. Our findings suggest that instead of discarding individual Ct values from positive specimens, incorporation of viral loads into public health data streams offers a new approach for real-time resource allocation and assessment of outbreak mitigation strategies, even where repeat incidence data is not available. Ct values or similar viral load data should be regularly reported to public health officials by testing centers and incorporated into monitoring programs.
