ABSTRACT:

Contact tracing, both manual and potentially with digital apps, is considered a key ingredient in the control of infectious disease outbreaks, and in the strategies making it possible to alleviate the lock-down and to return to a quasi-normal functioning of society in the COVID-19 crisis.

However, the current leading modeling framework for evaluating contact tracing is highly stylized, lacking important features and heterogeneities present in real-world contact patterns that are relevant for epidemic dynamics.

Here, we fill this gap by considering a modeling framework extensively informed by real-world, high-resolution contact data to analyze the impact of digital (app-based) containment strategies for the COVID-19 pandemic. More specifically, we investigate how well contact tracing apps, coupled with the quarantine of identified contacts, can mitigate a spread in realistic scenarios such as a university campus, a workplace, or an high school.

We find that restrictive policies are more effective in confining the epidemics but come at the cost of quarantining a large part of the population with a consequent social cost, and that it is possible to design less limiting policies in order to avoid this effect. Our results also show that isolation and tracing alone are unlikely to be sufficient to keep an outbreak under control, and additional measures need to be implemented simultaneously.

Moreover, we confirm that a high level of app adoption is crucial to make contact tracing an effective measure.

Finally, we find that strategies focusing on long exposure times, even for weak links, perform better than approaches that emphasize close-range contacts for shorter time-periods. Our results have implications for the app-based contact tracing efforts currently being implemented across several countries worldwide.

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