key: cord-0756871-53zopedx
authors: Hellewell, J.; Russell, T. W.; The SAFER Investigators and Field study team,; The Crick COVID-19 Consortium,; CMMID COVID-19 Working Group,; Beale, R.; Kelly, G.; Houlihan, C.; Nastouli, E.; Kucharski, A. J.
title: Estimating the effectiveness of routine asymptomatic PCR testing at different frequencies for the detection of SARS-CoV-2 infections
date: 2020-11-24
journal: nan
DOI: 10.1101/2020.11.24.20229948
sha: 519fabbaccfed13444b7f912012808eb6cc0ae9f
doc_id: 756871
cord_uid: 53zopedx

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 how testing and 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, 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. Findings: 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. Interpretation: 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.

Evidence before this study A number of studies have investigated the relationship between viral load of SARS-CoV-2 and the presence and severity of symptoms of COVID-19. Furthermore, a handful of studies have looked in detail at how the probability of detection by PCR varies over the course of an entire infection. We searched PubMed, BioRxiv, and MedRxiv for English-language articles with the search terms ("covid-19" OR "coronavirus" OR "SARS-CoV-2") AND ("test" OR "detection" OR "PCR" OR "polymerase chain reaction" OR "RT-PCR" OR "reverse transcriptase" OR "swab" OR "antigen" OR "symptoms" OR "RNA") AND ("regular" OR "widespread" OR "shedding" OR "health-care workers" OR "HCW" OR "regular") AND ("asymptomatic" OR "pre-symptomatic" OR "exposure"). This search returned a total of 396 results, of which 21 were studies that either gathered similar data, i.e. symptom, antibody and PCR test data longitudinally, or they specifically investigated PCR sensitivity over the course of infection. Only one study directly attempted to fit a similar PCR positivity curve, but no study inferred unobserved infection times for individuals and used those estimates to jointly fit (across all individuals) the probability of a positive PCR test as a function of time since infection.

Our study extends the existing literature in two specific ways. First we infer infection times for all individuals using a rigorous Bayesian framework and within our model we directly use the inferred posterior distributions of infection time to fit a curve across all individuals to characterise the probability of detection via PCR test over the course of infection. Because

HCWs in our dataset were tested regardless of symptoms, many of these tests were performed close to the time of infection and therefore help to characterise our probability of detection estimates, in contrast to earlier datasets that only include tests performed as a result of symptom onset or hospitalisation. Second, we incorporated these estimates into a model of testing frequency to provide insights into the likely effectiveness of routine testing in settings like hospitals. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

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Routine asymptomatic testing by PCR is being used in many settings with the aim of preventing outbreaks within vulnerable populations. We present evidence that the majority of SARS-CoV-2 infections can be detected prior to symptom onset (or within 7 days if there is no symptom onset) by a routine asymptomatic testing regime with a high enough testing frequency and short delay between testing and notification of cases. Therefore, routine asymptomatic testing regimes can be calibrated using our findings to help optimise infection detection.

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Detection of current infection with Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) is a crucial component of targeted policy responses to the COVID-19 pandemic that involve minimising infection within vulnerable groups. For instance, residents and staff in care homes may be tested regularly to minimise outbreaks among elderly populations 1 .

Alternatively, healthcare workers (HCWs) may be routinely tested to prevent nosocomial transmission to patients who may have other comorbidities 2,3 . Both of these populations have a substantially higher risk of fatality from COVID-19 infection than the general population 4,5 .

In the UK, testing commonly uses polymerase chain reaction (PCR) to detect the presence of viral RNA in the nasopharynx of those sampled 6 . The sensitivity of PCR tests at any given point during infection depends upon the amount of viral RNA present, this increases at the start of the infection up to the peak viral load, which appears to occur just before, or at, the time of symptom onset [7] [8] [9] . Viral load then decreases, but infected individuals continue to shed the virus for an average of 17 days after initial infection (but this can be far longer than the average, the longest observed duration has been 83 days) 10 . A greater severity of illness is frequently associated with a significantly longer duration of viral shedding [11] [12] [13] .

Asymptomatic infections have been found to have similar viral loads to symptomatic cases around the time of infection, but also exhibit shorter durations of viral shedding 14 .

Estimates of temporal variation in the probability of detecting infections by PCR are crucial for planning effective routine asymptomatic testing strategies in settings with vulnerable populations. The testing frequency required to detect the majority of infections before they can transmit onwards will depend on both how soon -and how long -an individual remains positive by PCR test. Measuring the probability that testing will detect SARS-CoV-2 at a given is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

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To address these challenges, we analysed data that covered the regular testing of healthcare workers (HCWs) in London, UK. We inferred their likely time of infection and used the results of the repeated tests performed over the course of their infection to infer the probability of testing positive depending on the amount of time elapsed since infection.

This overcame the bias towards testing around the time of symptom onset, although we focused on data from symptomatic infections so the timing of symptom onset could be used to infer the likely time of infection.

We used data from the SAFER study 16 is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted November 24, 2020. ;  based on the interval between their last asymptomatic report, We also performed a sensitivity analysis whereby the testing data for one HCW at a time was left out from the model fitting procedure to see if the PCR testing data for any . CC-BY 4.0 International license It is made available under a perpetuity.

is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted November 24, 2020. ; https://doi.org/10.1101/2020.11.24.20229948 doi: medRxiv preprint individual HCW had an undue influence on the overall regression fit (results are shown in Supplement B).

We looked at two different ways of assessing the performance of different routine asymptomatic testing frequencies. Firstly, we calculated the probability that a symptomatic case would be detected before symptom onset; this demonstrates the ability of testing to catch infections before people eventually self-isolate due to symptoms (by which point they may already have infected someone). Secondly, we calculated the probability that an asymptomatic case is caught within 7 days of infection, estimating how frequently testing would need to be to detect asymptomatic infections in a timely manner. The mathematical equations used to calculate each of these probabilities are shown in Supplement C.

The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication.

The model found that the majority of individuals included in this analysis were infected around the beginning of the study period in late March ( Figure 2 ). This corresponds with a period of greatly increased hospitalisation in London, which could potentially mean much higher exposure to infectious COVID-19 patients. However, this analysis cannot say for certain where these HCWs were infected.

We estimated that the peak probability of a positive PCR test is 77% (54 -88%) at 4 days after infection. The probability of a positive PCR test then decreases to 50% (38 -65%) by 10 days after infection and reaches virtually 0% probability by 30 days after infection ( Figure   3A ). Summary statistics for the posterior distributions of the piecewise logistic regression parameters are shown in Table 1 . We compared our results for the probability of infection is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted November 24, 2020. ; https://doi.org/10.1101/2020.11.24.20229948 doi: medRxiv preprint throughout infection to previous results in Supplement A, we found greater probability of detecting infections 1 to 3 days infection and a consistently lower probability of detection infections around 10 to 30 days after infection than previous results.

Our routine asymptomatic testing scenarios established that the higher the frequency of testing, the higher the probability that a symptomatic case will be detected before symptom onset ( Figure 3B ) and the higher the probability that an asymptomatic case is detected within 7 days ( Figure 3C) .A 2 day delay between testing and notification compared to a 1 day delay led to reduced probability of detection in both testing scenarios (Figures 3B, 3C) . This is because a longer delay means that an infection must be caught earlier to allow for a longer period of time between a test being administered and the infected person being notified of the results. An increased delay from testing to notification caused a greater relative reduction in the probability of detecting an asymptomatic case within 7 days of infection when the testing frequency was lower ( Figure 3C) .

When considering what is an acceptable testing frequency for detecting a desired proportion of symptomatic cases prior to their symptom onset, there may be a trade-off between testing frequency and the delay from testing to notification. For example, the probability of detecting a symptomatic case prior to onset is very similar for a 2 day testing frequency with a 2 day notification delay (41%, 23 -58%) compared to a 4 day testing frequency with a 1 day notification delay (39%, 22 -56%). This trade-off is depicted graphically in the dashed black box in Figure 3B .

The ongoing COVID-19 pandemic has led to increasing focus on routine asymptomatic testing strategies that could prevent sustained transmission in hospitals and other defined settings with at-risk individuals such as care homes. Using data on repeated testing of healthcare workers, we estimated that peak positivity for PCR tests for SARS-CoV-2 infections occurs 4 days after infection, which is just before the average incubation duration, in agreement with other studies finding that viral load in the respiratory tract is highest at this point 21, 22 . is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

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We found a substantially higher probability of detection by PCR between 1 and 3 days after infection than a previous study 23 . The low detection probabilities estimated in the previous study for the period 1 to 3 days after infection were fitted to very small amounts of data: one observed negative test on each of 1, 2, and 3 days after infection. Due to the fact that HCWs in the SAFER study were repeatedly tested even when asymptomatic, many of the tests took place close to the inferred infection times. This provided more test data for our model to fit to for the period just after infection. We provide a more rigorous exploration of the differences between our results and existing work in Supplement A. A plausible explanation for this difference could be due to the sample collection method and disease severity of the people being tested, leading to different observed viral load dynamics. The SAFER study data used here was collected from self-administered tests by HCWs and the symptoms recorded were those that were compatible with SARS-CoV-2 according to Public Health England, including a "new continuous cough or alteration in sense of taste or smell" 16 . Conversely, the datasets used for fitting the Kucirka model consist mainly of HCW-administered tests on hospitalised patients who are likely to have more severe infections, a factor that has been associated with a longer duration of viral shedding 10 in some studies. As such, our curve for the probability of detection by PCR may constitute a closer approximation of PCR test sensitivity over time in individuals with mild symptomatic infections. This would make it particularly useful for estimating the effectiveness of routine asymptomatic testing strategies, which would seek to detect all infections, not just the most severe.

Incorporating our estimates of PCR detection probability into a model of routine asymptomatic testing strategies, we found that there is the potential for a trade-off between the turnaround time for test results and testing frequency (Example in dashed black box, Figure 3B ). This could be particularly relevant for settings that do not have the resources or capacity for very high frequency testing, but could ensure prompt results.

Although our analysis focuses on the probability of testing positive, any potential testing and isolation strategy would also need to consider the potential for false positives, particularly at low prevalence 24 .

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The maximum probability of detection of 77% shown by the curve in Figure 3A refers to the whole population and does not imply that an individual person's peak probability of being detected by a PCR test is 77%. The curve is fitted to combined test results for many individuals, each of whom will have had variation in the timing of their particular peak probability of detection. This variation is smoothed out over all individuals to lead to the curve shown in Figure 3A .

We assumed that symptoms reported during the study were due to clinical episodes of COVID-19 infection, and not due to other respiratory infections with similar symptoms. All individuals in the analysis seroconverted over the course of the study, suggesting that such symptoms were likely to be associated with SARS-CoV-2 infection.

Our analysis is also limited by excluding asymptomatic HCWs that seroconverted over the course of the study. Symptomatic infections may have higher viral loads and be more likely to be detected than asymptomatic infections, however this has not been found to be the case elsewhere 14 . Our repeated testing model presents results for detecting asymptomatic infections that relies on the assumption that the probability of detection over time is the same for symptomatic and asymptomatic infections. If asymptomatic infections are instead less likely to be detected then our estimate of the probability of detection within 7 days of infection will be an overestimate.

Routine asymptomatic testing is a crucial component of effective targeted control strategies for COVID-19, and our results suggest that frequent testing and fast turnaround times could yield high probabilities of detecting infections -and hence prevent outbreaks -early in at-risk settings.

The subset of the data, including individuals that seroconvert and show symptoms at some stage during the data collection period, required to reproduce the figures and results of this study can be found at the public github repository: https://github.com/cmmid/pcr-profile.

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is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted November 24, 2020. ; https://doi.org /10.1101 The code required to reproduce the figures and results of this study can be found at the public github repository: https://github.com/cmmid/pcr-profile.

We declare no competing interests.

The following funding sources are acknowledged as providing funding for the named is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted November 24, 2020. ; https://doi.org/10.1101/2020.11.24.20229948 doi: medRxiv preprint Dashed black box shows a site of possible trade-off between testing frequency and results delay discussed in the text C) Probability of detecting an asymptomatic case within 7 days, based on curve in (A), assuming delay from test to results is either 24 or 48 hours. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted November 24, 2020. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint

The copyright holder for this this version posted November 24, 2020. ; https://doi.org/10.1101/2020.11.24.20229948 doi: medRxiv preprint 

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