During epidemics, how effective are digital contact tracing technologies for identifying secondary cases and close contacts?

Burch, Jane; Jackson, Christopher

doi:10.1002/cca.3262

Cochrane Clinical Answers

2020

DOI: 10.1002/cca.3262

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During epidemics, how effective are digital contact tracing technologies for identifying secondary cases and close contacts?

Jane Burch¹,

Christopher Jackson²

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2021

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“…While the advantage of digital over manual contact tracing in detecting secondary cases and interrupting chains of contagion remains disputed in general ( 9 , 10 ), digital tracing has been proposed in the case of COVID-19 due to the fundamental role of presymptomatic and asymptomatic individuals in the spreading dynamics ( 1 , 11 ). To this end, a number of smartphone applications have been released ( 12 – 17 ) that leverage proximity signals to provide a fast and scalable approach for identifying all recent contacts.…”

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confidence: 99%

Epidemic mitigation by statistical inference from contact tracing data

Baker

Biazzo

Braunstein

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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Contact tracing is an essential tool to mitigate the impact of a pandemic, such as the COVID-19 pandemic. In order to achieve efficient and scalable contact tracing in real time, digital devices can play an important role. While a lot of attention has been paid to analyzing the privacy and ethical risks of the associated mobile applications, so far much less research has been devoted to optimizing their performance and assessing their impact on the mitigation of the epidemic. We develop Bayesian inference methods to estimate the risk that an individual is infected. This inference is based on the list of his recent contacts and their own risk levels, as well as personal information such as results of tests or presence of syndromes. We propose to use probabilistic risk estimation to optimize testing and quarantining strategies for the control of an epidemic. Our results show that in some range of epidemic spreading (typically when the manual tracing of all contacts of infected people becomes practically impossible but before the fraction of infected people reaches the scale where a lockdown becomes unavoidable), this inference of individuals at risk could be an efficient way to mitigate the epidemic. Our approaches translate into fully distributed algorithms that only require communication between individuals who have recently been in contact. Such communication may be encrypted and anonymized, and thus, it is compatible with privacy-preserving standards. We conclude that probabilistic risk estimation is capable of enhancing the performance of digital contact tracing and should be considered in the mobile applications.

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mentioning

confidence: 99%

Epidemic mitigation by statistical inference from contact tracing data

Baker

Biazzo

Braunstein

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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During epidemics, how effective are digital contact tracing technologies for identifying secondary cases and close contacts?

Cited by 1 publication

References 1 publication

Epidemic mitigation by statistical inference from contact tracing data

Epidemic mitigation by statistical inference from contact tracing data

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