Digital proximity tracing on empirical contact networks for pandemic control

Abstract

<jats:p>Digital contact tracing is increasingly considered as a tool to control infectious disease outbreaks. As part of a broader test, trace, isolate, and quarantine strategy, digital contract tracing apps have been proposed to alleviate lock-downs, and to return societies to a more normal situation in the ongoing COVID-19 crisis. Early work evaluating digital contact tracing did not consider important features and heterogeneities present in real-world contact patterns which impact epidemic dynamics. Here, we fill this gap by considering a modeling framework informed by empirical high-resolution contact data to analyze the impact of digital contact tracing apps in the COVID-19 pandemic. We investigate how well contact tracing apps, coupled with the quarantine of identified contacts, can mitigate the spread of COVID-19 in realistic scenarios such as a university campus, a workplace, or a 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. It is possible to avoid this effect by considering less strict policies, which only consider contacts with longer exposure and at shorter distance to be at risk. Our results also show that isolation and tracing can help keep re-emerging outbreaks under control provided that hygiene and social distancing measures limit the reproductive number to 1.5. Moreover, we confirm that a high level of app adoption is crucial to make digital contact tracing an effective measure. Our results may inform app-based contact tracing efforts currently being implemented across several countries worldwide.</jats:p>

Anatomy of digital contact tracing: role of age, transmission setting, adoption and case detection

“…Third, clustering effects are partially captured by the model thanks to the repetition of contacts, but effects may be larger in real contact patterns. Results obtained on real contact data, however, are similar to ours obtained on synthetically reconstructed contacts [15]. Eventually, parameterized with data from a social contact survey [25], the model cannot account for crowding events.…”

“…Under realistic hypotheses, the intervention would not be able alone to bring the epidemic under control in a scenario where transmission is high [15], [16], mainly due to the strong role of asymptomatic transmission in fueling the epidemic [1], [4]. We considered also lower values of R as recent studies suggest that the combination of facemask, physical distance and hand hygiene may substantially hinder SARS-COV-2 transmission [32].…”

Anatomy of digital contact tracing: Role of age, transmission setting, adoption, and case detection

“…Under realistic hypotheses, the intervention would not be able alone to bring the epidemic under control in a scenario where transmission is high (18,19,26), mainly due to the strong role of asymptomatic transmission in fueling the epidemic (11,12,43). We explored different values of the reproductive number R , to effectively account for non-pharmaceutical measures mitigating the epidemic and for the adoption of preventive measures substantially hindering SARS-COV-2 transmission.…”

“…Previous projections on the impact of contact tracing rely on a similar approach in some cases (17,24,26). Other works make use of high resolution data (18,19,22), that are more reliable than contact surveys, but are restricted to specific settings or population groups. Despite the difference in the data source and approach, the results of these studies are consistent and in agreement with our work on the overall impact of the intervention.…”

“…Several modelling studies have quantified the impact of contact tracing (17)(18)(19)(20)(21)(22)(23)(24)(25)(26), with some of them addressing specific aspects of digital contact tracing (18)(19)(20)(21)(22)(23). Still the interplay between age and setting heterogeneity in determining the efficacy of this intervention is largely unexplored.…”