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Measurement-based evaluation of Google/Apple Exposure Notification API for proximity detection in a light-rail tram.
Leith, Douglas J; Farrell, Stephen.
  • Leith DJ; School of Computer Science & Statistics, Trinity College Dublin, Dublin, Ireland.
  • Farrell S; School of Computer Science & Statistics, Trinity College Dublin, Dublin, Ireland.
PLoS One ; 15(9): e0239943, 2020.
Article in English | MEDLINE | ID: covidwho-808387
ABSTRACT
We report on the results of a Covid-19 contact tracing app measurement study carried out on a standard design of European commuter tram. Our measurements indicate that in the tram there is little correlation between Bluetooth received signal strength and distance between handsets. We applied the detection rules used by the Italian, Swiss and German apps to our measurement data and also characterised the impact on performance of changes in the parameters used in these detection rules. We find that the Swiss and German detection rules trigger no exposure notifications on our data, while the Italian detection rule generates a true positive rate of 50% and a false positive rate of 50%. Our analysis indicates that the performance of such detection rules is similar to that of triggering notifications by randomly selecting from the participants in our experiments, regardless of proximity.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Transportation / Contact Tracing / Disease Notification / Mobile Applications Type of study: Diagnostic study / Experimental Studies / Randomized controlled trials Limits: Humans Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2020 Document Type: Article Affiliation country: Journal.pone.0239943

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Transportation / Contact Tracing / Disease Notification / Mobile Applications Type of study: Diagnostic study / Experimental Studies / Randomized controlled trials Limits: Humans Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2020 Document Type: Article Affiliation country: Journal.pone.0239943