Heterogeneity is essential for contact tracing

  1. Bjarke Frost Nielsen
  2. Kim Sneppen
  3. Lone Simonsen
  4. Joachim Mathiesen

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

(Dated: June 5, 2020)

The COVID-19 pandemic is most often modelled by well-mixed models, sometimes stratified by age and work. People are, however, different from one another in terms of interaction frequency as well as in formation of social groups. This contact heterogeneity especially challenges models of contact tracing (CT), but also predictions of epidemic severity generally. We explore how heterogeneity affects CT effectiveness and overall epidemic severity, using a real-world contact network.

Methods

Utilizing smartphone proximity data from Danish university students, we simulate the spread of COVID-19 on a network with realistic contact structure. Two modes of network homogenization are implemented to probe effects of heterogeneity. We then simulate a CT scheme on the network and explore the impact of heterogeneity, testing probability and contact threshold for quarantining.

Results

Measuring contact heterogeneity, we find an exponential distribution which persists on a timescale of several weeks. Comparing the true network to edge-swapped and randomized versions, we find that heterogeneity decreases the severity of COVID-19 in general, and that it drastically improves CT.

Conclusions

To capture heterogeneity, it is necessary to reconsider disease transmission models. Our findings show that heterogeneity is essential for CT, and that CT is effective even if only the most frequent contacts can be tracked down. We find that contact heterogeneity impedes the spread of COVID-19 in comparison with well-mixed networks. In perspective, this means that fitting traditional SEIR models to epidemic data is likely to overestimate the severity.

Article activity feed

  1. Version published to 10.1101/2020.06.05.20123141v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.06.05.20123141: (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: 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.
    • No protocol registration statement was detected.

    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.

  3. Version published to 10.1101/2020.06.05.20123141v1 on medRxiv