Morning SARS-CoV-2 testing yields better detection of infection due to higher viral loads in saliva and nasal swabs upon waking

  1. Alexander Viloria Winnett
  2. Michael K. Porter
  3. Anna E. Romano
  4. Emily S. Savela
  5. Reid Akana
  6. Natasha Shelby
  7. Jessica A. Reyes
  8. Noah W. Schlenker
  9. Matthew M. Cooper
  10. Alyssa M. Carter
  11. Jenny Ji
  12. Jacob T. Barlow
  13. Colten Tognazzini
  14. Matthew Feaster
  15. Ying-Ying Goh
  16. Rustem F. Ismagilov

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Abstract

Background

The analytical sensitivities of SARS-CoV-2 diagnostic tests span 6 orders of magnitude. Optimizing sample-collection methods to achieve the most reliable detection for a given sensitivity would increase the effectiveness of testing and minimize COVID-19 outbreaks.

Methods

From September 2020 to April 2021 we performed a household-transmission study in which participants self-collected samples every morning and evening throughout acute SARS-CoV-2 infection. Seventy mildly symptomatic participants collected saliva and, of those, 29 also collected nasal-swab samples. Viral load was quantified in 1194 saliva and 661 nasal-swab samples using a high-analytical-sensitivity RT-qPCR assay (LOD, 1,000 SARS-CoV-2 RNA copies/mL).

Findings

Viral loads in both saliva and nasal-swab samples were significantly higher in morning-collected samples than evening-collected samples after symptom onset. We used these quantitative measurements to infer which diagnostic tests would have detected infection (based on sample type and test analytical sensitivity). We find that morning collection would have resulted in significantly improved detection and that this advantage would be most pronounced for tests with low to moderate analytical sensitivity, which would likely have missed infections if sampling in the evening.

Interpretation

Collecting samples for COVID-19 testing in the morning offers a simple and low-cost improvement to clinical diagnostic sensitivity of low- to moderate-analytical-sensitivity tests. The phenomenon of higher viral loads in the morning may also have implications related to when transmission is more likely to occur.

Funding

Bill & Melinda Gates Foundation, Ronald and Maxine Linde Center for New Initiatives (Caltech), Jacobs Institute for Molecular Engineering for Medicine (Caltech)

RESEARCH IN CONTEXT

Evidence before this study

Reliable COVID-19 diagnostic testing is critical to reducing transmission of SARS-CoV-2 and reducing cases of severe or fatal disease, particularly in areas with limited vaccine access or uptake. Saliva and anterior-nares nasal swabs are common sample types; however, different diagnostic tests using these sample types have a range of analytical sensitivities spanning 6 orders of magnitude, with limits of detection (LODs) between 10 2 and 10 8 genomic copy equivalents of SARS-CoV-2 RNA (copies) per mL of sample. Due to limitations in clinical laboratory capacity, many low-resource settings rely on COVID-19 tests that fall on the moderate (LODs of 10 4 to 10 5 copies/mL) to lower (LODs of 10 5 to 10 8 copies/mL) end of this spectrum of analytical sensitivity. Alterations in sample collection methods, including time of sample collection, may improve the performance of these diagnostics.

Added value of this study

This study quantifies viral loads from saliva and nasal-swab samples that were longitudinally self-collected by symptomatic patients in the morning immediately after waking and in the evening just prior to sleeping throughout the course of acute SARS-CoV-2 infection. The study cohort was composed of mildly or moderately symptomatic individuals (outpatients). This analysis demonstrates significantly higher viral loads in samples collected in the morning, relative to those collected in the evening. When using moderate to lower analytical sensitivity test methods, these loads are inferred to result in significantly better detection of infected individuals in the morning.

Implications of available evidence

These findings suggest that samples collected in the morning immediately after waking will better detect SARS-CoV-2 infection in symptomatic individuals tested by moderate to lower analytical sensitivity COVID-19 diagnostic tests (LODs at or above 10 4 viral copies per mL of sample), such as many rapid antigen tests currently available.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2022.03.02.22271724: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: 9,19 This study was reviewed and approved by the Institutional Review Board of the California Institute of Technology (protocol #20-1026).
    Consent: All adult participants provided written informed consent, and minors (ages 6-17) provided assent accompanied by parental permission.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical analyses: Initial processing was performed in Python v3.8.2, where log transformed averages were calculated (Fig1A, Fig1C).
    Python
    suggested: (IPython, RRID:SCR_001658)
    Data was exported and differences in Ct from sequential samples and the average difference in Ct values was performed in Microsoft Excel (Fig 2B, Fig 2C).
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)
    Plots were prepared in GraphPad Prism 9.2.0, including calculation of medians.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    For comparison of the differences between morning and evening viral loads and differences in Ct values, Mann-Whitney test was performed using GraphPad (Fig 2).
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)
    One-sided binomial tests to compare inferred percentage of infections detectable by assays with varying LODs for samples collected in the morning or evening (Fig 3) was performed in Python v3.8.2 using the scipy.stats package.
    scipy
    suggested: (SciPy, RRID:SCR_008058)

    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:
    This study is subject to four main limitations. First, we had a limited number of samples collected prior to the onset of symptoms, limiting our ability to discern a difference in detectability with morning or evening samples during the pre-symptomatic phase of infection. Second, this study was performed prior to the dominance of the Delta and Omicron variants of SARS-CoV-2, which may exhibit different viral-load kinetics. Third, host factors, including vaccination status, may also influence viral-load kinetics, but this study is not powered to demonstrate differences in viral-load profiles by demographic factors - data from all SARS-CoV-2-positive participants was aggregated for analysis. Fourth, this analysis involves inferring positivity by assays with varying analytical sensitivities (LODs), based on the quantitatively measured viral loads. A direct comparison with a specific test is needed to test real-world efficacy.

    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 10.1101/2022.03.02.22271724 on medRxiv