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Proactive Contact Tracing.

Prateek Gupta1,2,3, Tegan Maharaj1,4, Martin Weiss1,4

  • 1Montréal Institute of Learning Algorithms (Mila), Montréal, Québec, Canada.

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|March 13, 2023
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Summary
This summary is machine-generated.

Proactive Contact Tracing (PCT) apps predict infectiousness using multiple data sources, reducing COVID-19 spread more effectively than traditional methods. This approach minimizes unnecessary quarantines, improving public health outcomes and economic activity.

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Area of Science:

  • Epidemiology
  • Computer Science
  • Behavioral Science

Background:

  • Digital contact tracing (DCT) apps are used for epidemic control but often rely heavily on testing, leading to delayed interventions.
  • Current DCT methods can cause widespread quarantines ('pingdemic'), impacting mental health and the economy.
  • Over-reliance on testing and broad quarantine recommendations reduce DCT app effectiveness and public compliance.

Purpose of the Study:

  • To propose a novel Proactive Contact Tracing (PCT) framework using multiple data sources for more accurate infectiousness prediction.
  • To develop and evaluate an interpretable PCT algorithm (Rule-based PCT) through multi-disciplinary collaboration.
  • To compare the performance of PCT against binary contact tracing (BCT) and household quarantine (HQ) using agent-based modeling.

Main Methods:

  • Developed a PCT framework incorporating self-reported symptoms and contact messages to estimate infectiousness.
  • Created an interpretable Rule-based PCT algorithm.
  • Utilized agent-based modeling to simulate and compare PCT, BCT, and HQ under various parameters.
  • Conducted extensive sensitivity analysis on user behavior, policy, and virology.

Main Results:

  • Rule-based PCT demonstrated greater efficiency in controlling disease spread compared to BCT across diverse scenarios.
  • PCT significantly reduced Disability Adjusted Life Years and Temporary Productivity Loss, indicating superior cost-effectiveness over BCT.
  • PCT enables earlier notification of potentially infected users and prevention of onward transmission by leveraging anonymized infectiousness estimates.

Conclusions:

  • Proactive Contact Tracing (PCT) offers a more efficient and cost-effective approach to epidemic management than traditional binary contact tracing.
  • PCT's ability to predict and preemptively address infectiousness can mitigate disease spread while minimizing population mobility restrictions.
  • PCT-based applications represent a valuable tool for future epidemic preparedness and response.