Vaccine Prioritisation Using Bluetooth Exposure Notification Apps
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Abstract
After vaccinating health care workers and vulnerable groups against COVID-19, authorities will need to decide how to vaccinate everyone else. Prioritising individuals with more contacts can be disproportionately effective, in theory, but identifying these individuals is difficult. Here we show that the technology underlying Bluetooth exposure notification applications, such as used for digital contact tracing, can be leveraged to prioritise vaccination based on individual contact data. Our approach is based on the insight that these apps also act as local sensing devices measuring each user’s total exposure time to other users, thereby enabling the implementation of a previously impossible strategy that prioritises potential super-spreaders. Furthermore, by generalising percolation theory and introducing a novel measure of vaccination efficiency, we demonstrate that this “hot-spotting” strategy can achieve herd immunity with up to half as many vaccines as a non-targeted strategy, and is attractive even for relatively low rates of app usage.
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SciScore for 10.1101/2020.12.14.20248186: (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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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…
SciScore for 10.1101/2020.12.14.20248186: (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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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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