Digital proximity tracing on empirical contact networks for pandemic control
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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.
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SciScore for 10.1101/2020.05.29.20115915: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Digital contact tracing: insights and limitations: The general model that we developed for studying the effect of isolation and contact tracing on controlling the COVID-19 epidemic is inspired from the work of Fraser et al.42 The main distinctive characteristics that we have introduced are the following: (i) a general mathematical model that allows to evaluate the evolution of an epidemic in the presence of isolation and DCT at finite time; the …
SciScore for 10.1101/2020.05.29.20115915: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Digital contact tracing: insights and limitations: The general model that we developed for studying the effect of isolation and contact tracing on controlling the COVID-19 epidemic is inspired from the work of Fraser et al.42 The main distinctive characteristics that we have introduced are the following: (i) a general mathematical model that allows to evaluate the evolution of an epidemic in the presence of isolation and DCT at finite time; the evaluation of tracing efficiency by means of a numerical simulation on real contact data, and no more as an arbitrary parameter of the model; (iii) the dependence of infectiousness on the actual duration and physical proximity of contacts; and (iv) consequently, the design of appropriate policies. The functional shape of the infectiousness that we devised is composed by three dependencies: the time since primary infection ω(τ), the duration of a contact ωexposure(e), and its proximity ωdist(ss). The first is originally suggested by Ferretti et al.,17 while the other two were introduced in this work. We have shown that the implemented model is robust to changes of all three contributions, see Supplementary Note 1.2 and Supplementary Note 1.3. Our results suggest that an insufficient app adoption may render any digital tracing effort helpless on its own, if the reproductive number is too high. In view of these results, bridging the gap between a realistic app adoption and the larger tracing capability required to contain the disease appe...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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