Digital proximity tracing on empirical contact networks for pandemic control

Abstract

Digital contact tracing is a relevant tool to control infectious disease outbreaks, including the COVID-19 epidemic. Early work evaluating digital contact tracing omitted important features and heterogeneities of real-world contact patterns influencing contagion dynamics. We fill this gap with a modeling framework informed by empirical high-resolution contact data to analyze the impact of digital contact tracing in the COVID-19 pandemic. We investigate how well contact tracing apps, coupled with the quarantine of identified contacts, can mitigate the spread in real environments. We find that restrictive policies are more effective in containing the epidemic but come at the cost of unnecessary large-scale quarantines. Policy evaluation through their efficiency and cost results in optimized solutions which only consider contacts longer than 15–20 minutes and closer than 2–3 meters to be at risk. Our results show that isolation and tracing can help control re-emerging outbreaks when some conditions are met: (i) a reduction of the reproductive number through masks and physical distance; (ii) a low-delay isolation of infected individuals; (iii) a high compliance. Finally, we observe the inefficacy of a less privacy-preserving tracing involving second order contacts. Our results may inform digital contact tracing efforts currently being implemented across several countries worldwide.

Effect of manual and digital contact tracing on COVID-19 outbreaks: a study on empirical contact data

“…As traditional MCT is labour intensive and limited by people's ability to correctly remember contacts, app-based DCT is seen as a potentially useful complement and has been deployed in several countries [18,21,47]. Its actual efficiency, however, has been debated, in particular with respect to the level of adoption needed for it to make a difference [18,29,30,47,48].…”

“…We note here that these datasets were collected in non-pandemic situations. No such data are currently available to investigate how contact patterns have changed owing to restrictions and individual risk perception [43], and this has thus typically been modelled by effectively reduced transmission rates [4,29,30]. We consider here post-lockdown scenarios in which contact patterns and, behaviours could return to (almost) normal, and for completeness, we will also consider both reduced effective transmission rates as well as other ways to take such potential changes into account, such as random reduction of contacts or suppression of fleeting contacts.…”

“…We consider here post-lockdown scenarios in which contact patterns and, behaviours could return to (almost) normal, and for completeness, we will also consider both reduced effective transmission rates as well as other ways to take such potential changes into account, such as random reduction of contacts or suppression of fleeting contacts. We also note that these data have been recently used [29] to derive parameters for the approach described in [18], and to determine-within that approach-which app-based contact tracing policies might be most effective.…”

Contact Tracing Apps: Lessons Learned on Privacy, Autonomy, and the Need for Detailed and Thoughtful Implementation

“…The best policies and parameters based on this reasoning seek to minimize false negatives first, as these will result in further untracked community spread. These strategies, however, will likely result in a high number of false positives due to prioritization of high-sensitivity, low-specificity methods [27][28][29].…”

“…False positives may result through a variety of means, but initial planning to prevent false positives must begin with the technical strategy chosen and the policy-based design of initial parameters. The definition of close contact (6 feet or 2 meters, 15 minutes) most commonly put forward by public health entities has been argued to be too coarse for mass tracing, as evidenced by the high number of false positives seen with manual contact tracing and that this definition has resulted in decreased accuracy with digital contact tracing as well [ 27 , 28 ]. Likewise, as will be discussed further below, the technical strategies employed, such as GPS or Bluetooth, will directly impact the accuracy and efficacy of an app [ 11 , 30 ].…”