Safely return to schools and offices: early and frequent screening with high sensitivity antigen tests effectively identifies COVID-19 patients

  1. Yong Dam Jeong
  2. Keisuke Ejima
  3. Kwang Su Kim
  4. Shoya Iwanami
  5. Shingo Iwami
  6. Kazuyuki Aihara

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Abstract

Background

In-person interaction at school and offices offers invaluable experience to students and benefits to companies. In the midst of the pandemic, ways to safely go back to schools and offices have been argued. Centers for Disease Control and Prevention (CDC) recommends taking all precautions such as vaccination and universal indoor masking. However, even if all the precautions are implemented and transmission is perfectly prevented in the facilities, they may be infected outside of the facilities, which would be a source of transmission in the facilities. Therefore, identifying those infected outside of the facility through screening is essential to safely go back to schools or offices. However, studies investigating the effectiveness of screening are limited. Further, it is not well clarified now which screening strategy (e.g., low or high sensitivity antigen tests, intervals and the number of tests) effectively identify infected and infectious individuals to avoid transmission in facilities

Methods

We assessed the effectiveness of various screening strategies in schools and offices through quantitative simulation. The effectiveness was assessed by the proportion of identified infected and infectious participants. Infection dynamics in the facility is governed by transmission dynamics of the population they belong to, and the screening is initiated at different epidemic phases: growth, peak, and declining phases. The viral load trajectory over time for each infected individual was modelled by the viral dynamics model, and the transmission process at the population level was modelled by a deterministic compartment model. The model parameters were estimated from clinical and epidemiological data. Screening strategies were varied by antigen tests with different sensitivity and schedules of screening over 10 days.

Results

Under the daily screening, we found high sensitivity antigen tests (the detection limit: 6.3 × 10 4 copies/mL) yielded 88% (95%CI 86-89) of effectiveness by the end of 10 days screening period, which is about 20% higher than that with low sensitivity antigen tests (2.0 × 10 6 copies/mL). Comparing screening scenarios with different schedules, we found early and frequent screening is the key to maximize the effectiveness. Sensitivity analysis revealed that less frequent tests might be an option when the number of antigen tests is limited especially when the screening is performed at the growth phase.

Discussion

High sensitivity antigen tests, high frequency screening, and immediate initiation of screening are the key to safely restart educational and economic activities allowing in-person interactions. Our computational framework is useful in assessment of screening strategies by incorporating additional factors for specific situations.

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  1. ScreenIT

    SciScore for 10.1101/2021.10.08.21264782: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Viral load data: Longitudinal viral load data of symptomatic and asymptomatic COVID-19 patients were searched through PubMed and Google scholar.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Google scholar
    suggested: (Google Scholar, RRID:SCR_008878)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Regardless of the flexibility of the simulation framework, there are a few limitations which need to be addressed in future studies. First, we did not consider false-positive rate of the antigen tests (specificity of antigen tests for SARS-CoV-2: 99.6% (Dinnes et al., 2021)). Especially in screening settings under low prevalence, even a small false positive rate would be an issue. For example, if the prevalence is 1% (and the sensitivity is 100%), the screening test with 99% sensitivity will produce almost the same number of false negative cases. As people with false positive results may need to be confirmed by PCR tests, targeted screening (targeting high prevalence population) should be considered. Second, both the transmission model and the viral dynamics model did not consider emerging variants, reinfection, breakthrough infection, and vaccine effect. If the viral dynamics of delta variant is different from the original variant (Li et al. suggested that the delta variant presented faster viral replication (B. Li et al., 2021)), our model needs to be updated. However, we anticipate that faster viral replication would not influence our findings because the replication speed is associated with the length that the virus cannot be detected, which is already less than 3 days with the original variant. The models need to update the transmission risk of under vaccination. Third, the transmission model did not incorporate behavioral change during the pandemic or screening. The con...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2021.10.08.21264782v1 on medRxiv