Understanding the impact of digital contact tracing during the COVID-19 pandemic

Abstract

Digital contact tracing (DCT) applications have been introduced in many countries to aid the containment of COVID-19 outbreaks. Initially, enthusiasm was high regarding their implementation as a non-pharmaceutical intervention (NPI). However, no country was able to prevent larger outbreaks without falling back to harsher NPIs. Here, we discuss results of a stochastic infectious-disease model that provide insights in how the progression of an outbreak and key parameters such as detection probability, app participation and its distribution, as well as engagement of users impact DCT efficacy informed by results of empirical studies. We further show how contact heterogeneity and local contact clustering impact the intervention’s efficacy. We conclude that DCT apps might have prevented cases on the order of single-digit percentages during single outbreaks for empirically plausible ranges of parameters, ignoring that a substantial part of these contacts would have been identified by manual contact tracing. This result is generally robust against changes in network topology with exceptions for homogeneous-degree, locally-clustered contact networks, on which the intervention prevents more infections. An improvement of efficacy is similarly observed when app participation is highly clustered. We find that DCT typically averts more cases during the super-critical phase of an epidemic when case counts are rising and the measured efficacy therefore depends on the time of evaluation.

Protect Your Score: Contact-Tracing with Differential Privacy Guarantees

“…Statistical contact tracing: Various approaches have been published about statistical contact tracing, especially during the COVID19 pandemic. Burdinski, Brockmann, and Maier (2022) test the efficacy of traditional contact tracing and run simulations, including self-isolation strategies. Li and Saad (2021) use a message-passing approach and analyze an isolation policy based on risk-score estimation.…”

Combatting SARS-CoV-2 With Digital Contact Tracing and Notification: Navigating Six Points of Failure

“…A positive caveat to this fairly bleak assessment of uptake is that if one individual has adopted the app, it is more likely that other members of their social network have also adopted it. In other words, failures at points 1 and 2 are correlated, making the probability of overall failure lower than it would be if each failure were an independent event [ 11 ]. Both for this reason and as part of good marketing practices [ 12 ], it can make sense to look at adoption rates within smaller communities.…”

Combatting SARS-CoV-2 With Digital Contact Tracing and Notification: Navigating Six Points of Failure (Preprint)

“…A positive caveat to this fairly bleak assessment of uptake is that if one individual has adopted the app, it is more likely that other members of their social network have too. In other words, failures at points 1 and 2 are correlated, making the probability of overall failure lower than it would be if each failure were an independent event [10]. Both for this reason, and as part of good marketing practices [11], it can make sense to look at adoption rates within smaller communities.…”