PlumX Metrics
Embed PlumX Metrics

Effect of manual and digital contact tracing on COVID-19 outbreaks: A study on empirical contact data

medRxiv
2020
  • 6
    Citations
  • 0
    Usage
  • 0
    Captures
  • 1
    Mentions
  • 24
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    6
    • Citation Indexes
      6
      • CrossRef
        6
  • Mentions
    1
    • News Mentions
      1
      • 1
  • Social Media
    24
    • Shares, Likes & Comments
      24
      • Facebook
        24

Most Recent News

EU Track and Trace: the €100m failure

Despite European hopes being invested in the technology, contact tracing apps have only succeeded in tracking 5% of registered cases since they were introduced in

Article Description

In the fight against the COVID-19 pandemic, lockdowns have succeeded in limiting contagions in many countries, at however heavy societal costs: more targeted non-pharmaceutical interventions are desirable to contain or mitigate potential resurgences. Contact tracing, by identifying and quarantining people who have been in prolonged contact with an infectious individual, has the potential to stop the spread where and when it occurs, with thus limited impact. The limitations of manual contact tracing (MCT), due to delays and imperfect recall of contacts, might be compensated by digital contact tracing (DCT) based on smartphone apps, whose impact however depends on the app adoption. To assess the efficiency of such interventions in realistic settings, we use here datasets describing contacts between individuals in several contexts, with high spatial and temporal resolution, to feed numerical simulations of a compartmental model for COVID-19. We find that the obtained reduction of epidemic size has a robust behavior: this benefit is linear in the fraction of contacts recalled during MCT, and quadratic in the app adoption, with no threshold effect. The combination of tracing strategies can yield important benefits, and the cost (number of quarantines) vs. benefit curve has a typical parabolic shape, independent on the type of tracing, with a high benefit and low cost if app adoption and MCT efficiency are high enough. Our numerical results are qualitatively confirmed by analytical results on simplified models. These results may inform the inclusion of MCT and DCT within COVID-19 response plans.

Bibliographic Details

Provide Feedback

Have ideas for a new metric? Would you like to see something else here?Let us know