Toward a COVID-19 testing policy: where and how to test when the purpose is to isolate silent spreaders

  1. AL Rivas
  2. AL Hoogesteijn
  3. JB Hittner
  4. MHV van Regenmortel
  5. P Kempaiah
  6. P Vogazianos
  7. A Antoniades
  8. A Ioannidis
  9. JL Febles
  10. FO Fasina

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Abstract

Background

To stop pandemics, such as COVID-19, infected individuals should be detected, treated if needed, and –to prevent contacts with susceptible individuals-isolated. Because most infected individuals may be asymptomatic, when testing misses such cases, epidemics may growth exponentially, inducing a high number of deaths. In contrast, a relatively low number of COVID-19 related deaths may occur when both symptomatic and asymptomatic cases are tested.

Methods

To evaluate these hypotheses, a method composed of three elements was evaluated, which included: (i) county- and country-level geo-referenced data, (ii) cost-benefit related considerations, and (iii) temporal data on mortality or test positivity (TP). TP is the percentage of infections found among tested individuals. Temporal TP data were compared to the tests/case ratio (T/C ratio) as well as the number of tests performed/million inhabitants (tests/mi) and COVID-19 related deaths/million inhabitants (deaths/mi).

Findings

Two temporal TP profiles were distinguished, which, early, displayed low (∼ 1 %) and/or decreasing TP percentages or the opposite pattern, respectively. Countries that exhibited >10 TP % expressed at least ten times more COVID-19 related deaths/mi than low TP countries. An intermediate pattern was identified when the T/C ratio was explored. Geo-referenced, TP-based analysis discovered municipalities where selective testing would be more cost-effective than alternatives.

Interpretations

When TP is low and/or the T/C ratio is high, testing detects asymptomatic cases and the number of COVID-19 related deaths/mi is low. Geo-referenced TP data can support cost-effective, site-specific policies. TP promotes the prompt cessation of epidemics and fosters science-based testing policies.

Funding

None

Research in context

Evidence before this study

To map this field, bibliographic searches were conducted in the Web of Science , which included the following results: (i) COVID-19 (95,133 hits), (ii) SARS COV-2 (33,680 hits), (iii) testing policy and COVID-19 (939 hits), (iv) testing policy and SARS COV-2 (340 hits), (v) testing policy and COVID-19 and asymptomatic (80 hits), (vi) testing policy and SARS COV-2 and asymptomatic (54 hits); (vii) test positivity and COVID-19 and validation (7 hits), and (viii) test positivity and SARS CoV-2 and validation (5 hits). Therefore, before this study, testing policy in relation to asymptomatic cases as well as test positivity represented a very low proportion (between ∼1 thousandth to ∼ 1 ten thousandth) of all publications. While many articles distinguished between diagnostic and screening tests, no paper was found in which testing policy is mentioned as part of a process ultimately designed to isolate all infected individuals. The few articles that mentioned test positivity only investigated symptomatic cases. These quanti/qualitative assessments led the authors to infer that neither testing policy nor test positivity had been adequately validated and/or investigated.

Added value of this study

We provide the first validation of test positivity as an estimate of disease prevalence under rapidly changing conditions: in pandemics, disease prevalence may vary markedly within short periods of time. We also address a double limitation of control campaigns against COVID-19, namely: it is unknown who and where to test. Asymptomatic cases are not likely to seek medical assistance: while they feel well, they silently spread this pandemic. Because they represent approximately half of all infected individuals, they are a large, moving, and invisible target. Where to find them is also unknown because (i) randomized testing is likely to fail and (ii) testing is very limited. Usually, the locations where infected people reside are not randomly distributed but geographically clustered, and, up to now less than four persons per thousand inhabitants are tested on a given day. However, by combining geo-referenced test positivity data with cost-benefit considerations, we generate approaches not only likely to induce high benefits without increasing costs but also free of assumptions: we measure bio-geography as it is.

Implications of all the available evidence

The fact that asymptomatic cases were not tested in many countries may explain the exponential growth and much higher number of deaths observed in those countries. Ineffective testing (and, therefore, ineffective isolation) can also result from the absence of geo-referenced data analysis. Because the geographical location where people reside, work, study, or shop is not a random event, the analysis of small greographical areas is essential. Only when actual geographical relationships are observed, optimal (cost-benefit oriented) testing policies can be devised.

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  1. ScreenIT

    SciScore for 10.1101/2020.12.22.20223651: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Differences in median deaths/million inhabitants between low TP and high TP countries were investigated with the Mann-Whitney test using a commercial statistical package (Minitab LLC, State College, PA, USA).
    Minitab
    suggested: (Minitab, RRID:SCR_014483)
    Geographical data were processed with ARC GIS software (ESRI, Redlands, Ca, USA).
    ARC GIS
    suggested: None

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.
    • Thank you for including a protocol registration statement.

    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 10.1101/2020.12.22.20223651v1 on medRxiv