Estimating the effectiveness of routine asymptomatic PCR testing at different frequencies for the detection of SARS-CoV-2 infections

Abstract

Background: Routine asymptomatic testing using RT-PCR of people who interact with vulnerable populations, such as medical staff in hospitals or care workers in care homes, has been employed to help prevent outbreaks among vulnerable populations. Although the peak sensitivity of RT-PCR can be high, the probability of detecting an infection will vary throughout the course of an infection. The effectiveness of routine asymptomatic testing will therefore depend on testing frequency and how PCR detection varies over time.

Methods: We fitted a Bayesian statistical model to a dataset of twice weekly PCR tests of UK healthcare workers performed by self-administered nasopharyngeal swab, regardless of symptoms. We jointly estimated times of infection and the probability of a positive PCR test over time following infection; we then compared asymptomatic testing strategies by calculating the probability that a symptomatic infection is detected before symptom onset and the probability that an asymptomatic infection is detected within 7 days of infection.

Results: We estimated that the probability that the PCR test detected infection peaked at 77% (54-88%) 4 days after infection, decreasing to 50% (38-65%) by 10 days after infection. Our results suggest a substantially higher probability of detecting infections 1-3 days after infection than previously published estimates. We estimated that testing every other day would detect 57% (33-76%) of symptomatic cases prior to onset and 94% (75-99%) of asymptomatic cases within 7 days if test results were returned within a day.

Conclusions: Our results suggest that routine asymptomatic testing can enable detection of a high proportion of infected individuals early in their infection, provided that the testing is frequent and the time from testing to notification of results is sufficiently fast.

Keywords: COVID-19; Healthcare workers; PCR testing; Presymptomatic infections; SARS-CoV-2; Test sensitivity.

Fig. 1

Testing and symptom data for the 27 individuals used in the analysis. Each point represents a symptom report and PCR test result. The border of the point is green if the PCR test result was positive and purple if it was negative. The inside of the point is red if the individual reported symptoms and white if they did not. Black crosses show the date of the initial negative serological test. Points are aligned along the x-axis by the timing of each participant’s last asymptomatic report

Fig. 2

The posterior of the infection time (Ti) of each participant. The posterior distribution of the infection time for each participant (purple) alongside the censored interval within which their symptom onset occurred (green dashed lines). The square points show the results of PCR tests on each individual; black points denote negative tests and red points denote positive tests

Fig. 3

Estimation of positivity over time, and probability that different testing frequencies with PCR would detect infection. a Ct value data for the PCR tests in the SAFER trial. This plot does not show data for every individual included in the analysis. The x-axis shows a time since infection using the median infection date inferred by the model. Points below the threshold of 37, indicating a positive result, are shown in red. Negative results above 37 are shown in black. All negative results for which there is no ct value specified are given the value of 40. b Temporal variation in PCR-positivity based on time since infection. The grey interval and solid black line show the 95% uncertainty interval and the mean, respectively, for the empirical distribution calculated from the posterior samples of the times of infection (see Additional file 1: Section D for methodology). The blue interval and dashed black line show the 95% credible interval and median, respectively, of the logistic piecewise regression described above. c Probability of detecting virus before expected onset of symptoms, based on curve in b, assuming delay from test to results is either 1 or 2 days. Dashed black box shows a site of possible trade-off between testing frequency and results delay discussed in the text. d Probability of detecting an asymptomatic case within 7 days, based on curve in b, assuming delay from test to results is either 24 or 48 h

Fig. 4

A copy of Fig. 3 using a Ct value of 28 (instead of 37) to classify a test as positive or not. This is instructive of how a lateral flow test (LFT) might perform as they seem to be less sensitive to infections with lower viral loads than PCR tests. In c and d, the probabilities of detection are now considered with a 0-day delay since LFTs give results within minutes that can be passed on to the person being tested quickly

Fig. 5

A copy of Fig. 3 using a Ct value of 25 (instead of 37) to classify a test as positive or not. This is instructive of how a lateral flow test (LFT) might perform as they seem to be less sensitive to infections with lower viral loads than PCR tests. In c and d, the probabilities of detection are now considered with a 0-day delay since LFTs give results within minutes that can be passed on to the person being tested quickly