Measurement-based evaluation of Google/Apple Exposure Notification API for proximity detection in a light-rail tram
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 det...
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| Veröffentlicht in: | PloS one 2020-09, Vol.15 (9), p.e0239943-e0239943, Article 0239943 |
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| creator | Leith, Douglas J. Farrell, Stephen |
| description | 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. |
| doi_str_mv | 10.1371/journal.pone.0239943 |
| format | Article |
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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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0239943</identifier><identifier>PMID: 32997724</identifier><language>eng</language><publisher>SAN FRANCISCO: Public Library Science</publisher><subject>Betacoronavirus ; Bluetooth ; Computer and Information Sciences ; Computer science ; Contact tracing ; Contact Tracing - methods ; Coronavirus Infections - transmission ; Coronaviruses ; COVID-19 ; Design standards ; Disease Notification - methods ; Distribution ; Earth Sciences ; Engineering and Technology ; Epidemics ; Evaluation ; Experiments ; Exposure ; False Positive Reactions ; Germany ; Humans ; Italy ; Medicine and Health Sciences ; Mobile Applications ; Multidisciplinary Sciences ; Pandemics ; People and Places ; Physical Sciences ; Pneumonia, Viral - transmission ; Public transportation ; SARS-CoV-2 ; Science & Technology ; Science & Technology - Other Topics ; Science Policy ; Signal strength ; Social Sciences ; Switzerland ; Technology application ; Transportation</subject><ispartof>PloS one, 2020-09, Vol.15 (9), p.e0239943-e0239943, Article 0239943</ispartof><rights>COPYRIGHT 2020 Public Library of Science</rights><rights>2020 Leith, Farrell. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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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.</description><subject>Betacoronavirus</subject><subject>Bluetooth</subject><subject>Computer and Information Sciences</subject><subject>Computer science</subject><subject>Contact tracing</subject><subject>Contact Tracing - methods</subject><subject>Coronavirus Infections - transmission</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>Design standards</subject><subject>Disease Notification - methods</subject><subject>Distribution</subject><subject>Earth Sciences</subject><subject>Engineering and Technology</subject><subject>Epidemics</subject><subject>Evaluation</subject><subject>Experiments</subject><subject>Exposure</subject><subject>False Positive Reactions</subject><subject>Germany</subject><subject>Humans</subject><subject>Italy</subject><subject>Medicine and Health Sciences</subject><subject>Mobile Applications</subject><subject>Multidisciplinary Sciences</subject><subject>Pandemics</subject><subject>People and Places</subject><subject>Physical Sciences</subject><subject>Pneumonia, Viral - 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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%. 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| subjects | Betacoronavirus Bluetooth Computer and Information Sciences Computer science Contact tracing Contact Tracing - methods Coronavirus Infections - transmission Coronaviruses COVID-19 Design standards Disease Notification - methods Distribution Earth Sciences Engineering and Technology Epidemics Evaluation Experiments Exposure False Positive Reactions Germany Humans Italy Medicine and Health Sciences Mobile Applications Multidisciplinary Sciences Pandemics People and Places Physical Sciences Pneumonia, Viral - transmission Public transportation SARS-CoV-2 Science & Technology Science & Technology - Other Topics Science Policy Signal strength Social Sciences Switzerland Technology application Transportation |
| title | Measurement-based evaluation of Google/Apple Exposure Notification API for proximity detection in a light-rail tram |
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