A Modelling Study for Designing a Multi-layered Surveillance Approach to Detect the Potential Resurgence of SARS-CoV-2

  1. Yang Liu
  2. Wenfeng Gong
  3. Samuel Clifford
  4. Maria E. Sundaram
  5. CMMID-COVID19 Working Group
  6. Mark Jit
  7. Stefan Flasche
  8. Petra Klepac

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Abstract

Background

Countries achieving control of COVID-19 after an initial outbreak will continue to face the risk of SARS-CoV-2 resurgence. This study explores surveillance strategies for COVID-19 containment based on polymerase chain reaction tests.

Methods

Using a dynamic SEIR-type model to simulate the initial dynamics of a COVID-19 introduction, we investigate COVID-19 surveillance strategies among healthcare workers, hospital patients, and community members. We estimate surveillance sensitivity as the probability of COVID-19 detection using a hypergeometric sampling process. We identify test allocation strategies that maximise the probability of COVID-19 detection across different testing capacities. We use Beijing, China as a case study.

Findings

Surveillance subgroups are more sensitive in detecting COVID-19 transmission when they are defined by more COVID-19 specific symptoms. In this study, fever clinics have the highest surveillance sensitivity, followed by respiratory departments. With a daily testing rate of 0.07/1000 residents, via exclusively testing at fever clinic and respiratory departments, there would have been 598 [95% eCI: 35, 2154] and 1373 [95% eCI: 47, 5230] cases in the population by the time of first case detection, respectively. Outbreak detection can occur earlier by including non-syndromic subgroups, such as younger adults in the community, as more testing capacity becomes available.

Interpretation

A multi-layer approach that considers both the surveillance sensitivity and administrative constraints can help identify the optimal allocation of testing resources and thus inform COVID-19 surveillance strategies.

Funding

Bill & Melinda Gates Foundation, National Institute of Health Research (UK), National Institute of Health (US), the Royal Society, and Wellcome Trust.

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  1. ScreenIT

    SciScore for 10.1101/2020.06.27.20141440: (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

    No key resources detected.

    Results from OddPub: Thank you for sharing your code.

    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.
    • No protocol registration statement was detected.

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  2. Version published to 10.1101/2020.06.27.20141440 on medRxiv