Containment of COVID-19: Simulating the impact of different policies and testing capacities for contact tracing, testing, and isolation

  1. Vincenzo G. Fiore
  2. Nicholas DeFelice
  3. Benjamin S. Glicksberg
  4. Ofer Perl
  5. Anastasia Shuster
  6. Kaustubh Kulkarni
  7. Madeline O’Brien
  8. M. Andrea Pisauro
  9. Dongil Chung
  10. Xiaosi Gu

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Abstract

Efficient contact tracing and testing are fundamental tools to contain the transmission of SARS-CoV-2. We used multi-agent simulations to estimate the daily testing capacity required to find and isolate a number of infected agents sufficient to break the chain of transmission of SARS-CoV-2, so decreasing the risk of new waves of infections. Depending on the non-pharmaceutical mitigation policies in place, the size of secondary infection clusters allowed or the percentage of asymptomatic and paucisymptomatic (i.e., subclinical) infections, we estimated that the daily testing capacity required to contain the disease varies between 0.7 and 9.1 tests per thousand agents in the population. However, we also found that if contact tracing and testing efficacy dropped below 60% (e.g. due to false negatives or reduced tracing capability), the number of new daily infections did not always decrease and could even increase exponentially, irrespective of the testing capacity. Under these conditions, we show that population-level information about geographical distribution and travel behaviour could inform sampling policies to aid a successful containment, while avoiding concerns about government-controlled mass surveillance.

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  1. Version published to 10.1371/journal.pone.0247614
  2. ScreenIT

    SciScore for 10.1101/2020.06.05.20123372: (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
    4.5 Code specifics and availability: The code was optimised for MATLAB r2019b (MathWorks, Natick, MA), and it allows loading any black and white dot-map of population density to test the effects of the different policies under realistic conditions of population density and geographic distribution.
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)

    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:
    The current study has a few limitations due to the simplifications that have been incorporated in the simulations. Some of these simplifications have been motivated by the fact that the virus itself is still very well under investigation and is therefore associated with multiple open questions. Further refining of our knowledge of the transmission mechanisms, the viral shedding or the symptomatology can affect the estimations of the testing capacities. Conversely, the described systemic failure under conditions of low contact tracing and testing efficacy is driven by the well-established presence of asymptomatic carriers, jointly with pre-symptomatic viral shedding. Other simplifications, concerning for instance the behaviour of the agents, are motivated by the need to execute a broad investigation across multiple conditions in a reasonably short time. Policy makers could use our findings as a proof of concept while focussing on a single map to include more realistic population-level behaviour (e.g. commuters might move long distances on a map, but follow predictable paths every day). This would allow to simulate context specific effects of tailored policies to aid contact tracing and testing, increasing the predictive power of the findings.

    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.

    About SciScore

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  3. Version published to 10.1101/2020.06.05.20123372v1 on medRxiv