Understanding the Impact of Digital Contact Tracing during the COVID-19 Pandemic

Angelique Burdinski, Dirk Brockmann, Benjamin Frank Maier

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.

During the ongoing coronavirus disease 2019 (COVID-19) pandemic, the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) caused over 500 million confirmed infections and more than 6 million deaths worldwide up to June 23, 2022. Among other pivotal measures to mitigate or contain the disease’s spread, the most common one is testing and isolation of symptomatic individuals.


To address the central questions around the efficacy of DCT, we designed a stochastic dynamic infectious-disease model that simulates spread on networks and is based on the generic susceptible-exposed-infectious-recovered/removed (SEIR) compartmental model, capturing the central mechanisms contributing to the outcome of DCT applications. The infectious state is split into subclasses to account for presymptomatic (IP), asymptomatic (IA) and symptomatic (IS) infectious individuals.

Contrary to the positive expectations DCT has raised initially, we conclude that our model, parameterized with less optimistic values, indicates that its impact on the reduction of COVID-19 outbreaks is rather supportive, which is in line with what was observed in the real world, where, for instance, no European country that introduced DCT was able to contain future outbreaks of COVID-19 without falling back to harsher NPIs.

B.F.M. thanks F. Klimm and F. Schlosser for helpful discussions. D.B. would like to thank I. Mortimer for valuable comments on the manuscript.

Citation: Burdinski A, Brockmann D, Maier BF (2022) Understanding the impact of digital contact tracing during the COVID-19 pandemic. PLOS Digit Health 1(12): e0000149. https://doi.org/10.1371/journal.pdig.0000149

Editor: Michele Tizzoni, ISI Foundation: Fondazione ISI - Istituto per l’lnterscambio Scientifico, ITALY

Received: March 24, 2022; Accepted: October 23, 2022; Published: December 6, 2022.

Copyright: © 2022 Burdinski et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The Python code used for simulation, analysis, and figures is available at https://doi.org/10.5281/zenodo.5093499.

Funding: B.F.M. is financially supported as an Add-On Fellow for Interdisciplinary Life Science by the Joachim Herz Stiftung.

Competing interests: The authors declare no competing interests.