Effect of manual and digital contact tracing on COVID-19 outbreaks: a study on empirical contact data
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Abstract
Non-pharmaceutical interventions are crucial to mitigate the COVID-19 pandemic and contain re-emergence phenomena. Targeted measures such as case isolation and contact tracing can alleviate the societal cost of lock-downs by containing the spread where and when it occurs. To assess the relative and combined impact of manual contact tracing (MCT) and digital (app-based) contact tracing, we feed a compartmental model for COVID-19 with high-resolution datasets describing contacts between individuals in several contexts. We show that the benefit (epidemic size reduction) is generically linear in the fraction of contacts recalled during MCT and quadratic in the app adoption, with no threshold effect. The cost (number of quarantines) versus benefit curve has a characteristic parabolic shape, independent of the type of tracing, with a potentially high benefit and low cost if app adoption and MCT efficiency are high enough. Benefits are higher and the cost lower if the epidemic reproductive number is lower, showing the importance of combining tracing with additional mitigation measures. The observed phenomenology is qualitatively robust across datasets and parameters. We moreover obtain analytically similar results on simplified models.
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SciScore for 10.1101/2020.07.24.20159947: (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: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has a number of limitations which are important to make explicit. In particular, the data we use correspond to limited social environments (a university campus and a workplace) and we do not provide an overall general study that includes all possible multiple and differentiated contexts and their mutual interplay. These data …
SciScore for 10.1101/2020.07.24.20159947: (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: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has a number of limitations which are important to make explicit. In particular, the data we use correspond to limited social environments (a university campus and a workplace) and we do not provide an overall general study that includes all possible multiple and differentiated contexts and their mutual interplay. These data moreover do not include interactions between the individuals participating in those studies and the general population. Nevertheless, these datasets correspond to the current state-of-the-art in terms of data describing human interactions, and the results show a robust qualitative behavior for data obtained in different contexts with different data collection infrastructures, both in terms of the epidemic size reduction and how the cost of the intervention depends on the manual contact tracing efficiency and on the app adoption. Although the effect of DCT is only quadratic in the app adoption, we emphasize that there is no threshold effect: any increase in adoption leads to an improvement in the epidemic spread mitigation. This is also true for MCT, showing that the quality of interviews and any improvement in the ability to correctly find contacts of infectious cases brings an important benefit. Overall, DCT, which is also able to trace contacts between individuals who do not know each other, yields an interesting complement to MCT, and the combination of MCT and DCT is able to suppress outbreaks at limited cost if the app adoption and MCT effi...
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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