Person:

Mina, Michael

Rapid point-of-care (POC) testing for the SARS-CoV-2 virus provides an ability to quickly test individuals, facilitating near real-time action if infection is determined, such as isolation and initiation of contact tracing. Rapid POC testing also facilitates more frequent testing in individuals, which is crucial for screening programs and for environments where as up-to-date results are essential, for example to facilitate safe entrance into nursing homes. The Abbott ID NOW is a rapid POC test that delivers an accurate result in 5-13 minutes and that has demonstrated a high level of performance in variety of patient populations. However, recent anomalous results by Basu et al. have raised important questions about the sensitivity of the Abbott test. Understanding the validity of these anomalous results cannot be overstated. The ID NOW is currently used widely as a mainstay of many COVID-19 testing programs: urgent-care clinics, hospitals, businesses and other institutions rely on the test to provide fast and accurate results every day. Here, we assess the anomalous findings and find significant limitations in the study design, comparisons used, and patients evaluated. We describe how these limitations can account for essentially all of the losses in sensitivity described by Basu et al. We also discuss interim results from an ongoing multi-site clinical study in urgent-care clinics that indicate that the ID NOW COVID-19 rapid test is highly sensitive (≥94.7%) and specific (≥98.6%). Rapid POC testing in outpatient clinical settings and acute care facilities offers the ability to quickly diagnose and isolate infected patients and enhance safeguards against virus transmission. It is important that clinicians received accurate information about rates of sensitivity and specificity in order to feel confident using current POC rapid-testing systems.

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 epidemic’s 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.

The role of children in the transmission of SARS-CoV-2 remains unclear, and existing data are yet to provide a consistent explanation for the markedly skewed age distribution of COVID-19 cases. Whereas early data from symptomatic case confirmations suggested a lack of disease in children, subsequent contact tracing studies have found that children are likely to be infected. Governments are now facing immense pressure to weigh the public health benefit of interventions such as school closure against the significant economic disruption they impose. To motivate the discussion of age-stratified social distancing measures, we discuss potential biological mechanisms by which a skewed age distribution of cases may be generated and show through mathematical modelling how different age-targeted interventions are likely to affect the epidemic final size. We propose that identifying age-dependent transmissibility, in addition to susceptibility, will be essential to understand which social distancing measures are likely to be the most impactful going forward.