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Proposes a framework called Proactive Contact Tracing which uses distributed inference of expected Covid-19 infectiousness to provide individualized, private recommendations.

Predicting Infectiousness for Proactive Contact Tracing

ICLR, (2021)

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Abstract

The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries and resulting in widespread lockdowns for emergency containment. Large-scale digital contact tracing (DCT) has emerged as a potential solution to resume economic and social activity while minimizing spread of the virus. Various DCT me...More
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Introduction
  • Until pharmaceutical interventions such as a vaccine become available, control of the COVID-19 pandemic relies on nonpharmaceutical interventions such as lockdown and social distancing.
  • While these have often been successful in limiting spread of the disease in the short term, these restrictive measures have important negative social, mental health, and economic impacts.
  • Relying only on positive test results as a trigger is inefficient for a number of reasons: (i) Tests have high false negative rates (Li et al, 2020); (ii) Tests are administered late, only after symptoms appear, leaving the asymptomatic population, estimated 20%-30% of cases (Gandhi et al, 2020), likely untested; (iii) It is estimated that infectiousness is highest before symptoms appear, well before someone would get a test (Heneghan et al, 2020), allowing them to infect others before being traced, (iv) Results typically take at least 1-2 days, and (v) In many places, tests are in limited supply
Highlights
  • Until pharmaceutical interventions such as a vaccine become available, control of the COVID-19 pandemic relies on nonpharmaceutical interventions such as lockdown and social distancing
  • Digital contact tracing (DCT), a technique to track the spread of the virus among individuals in a population using smartphones, is an attractive potential solution to help reduce growth in the number of cases and thereby allow more economic and social activities to resume while keeping the number of cases low
  • We evaluate the proposed proactive contact tracing (PCT) methods, set transformer (ST)-PCT and DS-PCT, and benchmark them against: testbased binary contact tracing (BCT); a rule-based feature-based contact tracing (FCT) Heuristic method proposed by Gupta et al (2020)4; and a baseline No Tracing (NT) scenario which corresponds to recommendation level 1
  • Recall R is an estimate of how many other agents an infectious agent will infects, i.e. even a numerically small improvement in R could yield an exponential improvement in the number of cases. We find that both PCT methods yield a clear improvement over BCT and the rule-based heuristic, all of which significantly improve over the no-tracing baseline
  • Our results demonstrate the potential benefit of digital contact tracing approaches for saving lives while reducing mobility restrictions and preserving privacy, independently confirming previous reports (Ferretti et al, 2020; Abueg et al, 2020)
  • Of all methods in our study, we find that deep learning based PCT provides the best trade-off between restrictions on mobility and reducing the spread of disease under a range of settings, making it a potentially powerful tool for saving lives in a safe deconfinement
Methods
  • EXP5: In Figure 6, the authors analyze the sensitivity of the various methods to adoption rate, which measures what percent of the population actively uses the CT app.
  • Adoption rate is an important parameter for DCT methods, as it directly determines the effectiveness of an app.
  • The authors visualize the effect of varying the adoption rate on the reproduction number R.
  • To prior work (Abueg et al, 2020), the authors find that all DCT methods improve over the no-tracing baseline even at low adoptions and PCT methods dominate at all levels of adoption.
Results
  • In a model in which 15% of the population participated, the authors found that digital exposure notification systems could reduce infections and deaths by approximately 8% and 6%, effectively complementing traditional contact tracing.
Conclusion
  • The authors' results demonstrate the potential benefit of digital contact tracing approaches for saving lives while reducing mobility restrictions and preserving privacy, independently confirming previous reports (Ferretti et al, 2020; Abueg et al, 2020).
  • Of all methods in the study, the authors find that deep learning based PCT provides the best trade-off between restrictions on mobility and reducing the spread of disease under a range of settings, making it a potentially powerful tool for saving lives in a safe deconfinement
  • This area of research holds many interesting avenues of future work in machine learning, including: (1) The comparison of methods for fitting parameters of the epidemiological simulator data using spatial information, (2) Using reinforcement learning to learn the mapping from estimated infectiousness and individual-level features, and thereby target desirable outcomes for heterogeneous populations.
  • The authors hope this work can play a role in fostering this necessary collaboration
Tables
  • Table1: Adoption Rate vs Uptake: The left column show the total percentage of the population with the app, while the right column shows the proportion of smartphone users with the app
  • Table2: Daily contacts per location type: This table shows for each location l the pre-confinement mean number of daily contacts Cl and the reduction in number of contacts αl based on data collected in the Region of Montréal
  • Table3: Recommendation level and corresponding effective contacts: Recommendation levels and corresponding expected number of daily effective contacts for a location type l
Download tables as Excel
Funding
  • In a model in which 15% of the population participated, we found that digital exposure notification systems could reduce infections and deaths by approximately 8% and 6%, effectively complementing traditional contact tracing
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Tegan Maharaj
Tegan Maharaj
Martin Weiss
Martin Weiss
Tristan Deleu
Tristan Deleu
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