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Toward a Common Performance and Effectiveness Terminology for Digital Proximity Tracing Applications
Digital proximity tracing (DPT) for Sars-CoV-2 pandemic mitigation is a complex intervention with the primary goal to notify app users about possible risk exposures to infected persons. DPT not only relies on the technical functioning of the proximity tracing application and its backend server, but...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8521913/ https://www.ncbi.nlm.nih.gov/pubmed/34713149 http://dx.doi.org/10.3389/fdgth.2021.677929 |
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author | Lueks, Wouter Benzler, Justus Bogdanov, Dan Kirchner, Göran Lucas, Raquel Oliveira, Rui Preneel, Bart Salathé, Marcel Troncoso, Carmela von Wyl, Viktor |
author_facet | Lueks, Wouter Benzler, Justus Bogdanov, Dan Kirchner, Göran Lucas, Raquel Oliveira, Rui Preneel, Bart Salathé, Marcel Troncoso, Carmela von Wyl, Viktor |
author_sort | Lueks, Wouter |
collection | PubMed |
description | Digital proximity tracing (DPT) for Sars-CoV-2 pandemic mitigation is a complex intervention with the primary goal to notify app users about possible risk exposures to infected persons. DPT not only relies on the technical functioning of the proximity tracing application and its backend server, but also on seamless integration of health system processes such as laboratory testing, communication of results (and their validation), generation of notification codes, manual contact tracing, and management of app-notified users. Policymakers and DPT operators need to know whether their system works as expected in terms of speed or yield (performance) and whether DPT is making an effective contribution to pandemic mitigation (also in comparison to and beyond established mitigation measures, particularly manual contact tracing). Thereby, performance and effectiveness are not to be confused. Not only are there conceptual differences but also diverse data requirements. For example, comparative effectiveness measures may require information generated outside the DPT system, e.g., from manual contact tracing. This article describes differences between performance and effectiveness measures and attempts to develop a terminology and classification system for DPT evaluation. We discuss key aspects for critical assessments of whether the integration of additional data measurements into DPT apps may facilitate understanding of performance and effectiveness of planned and deployed DPT apps. Therefore, the terminology and a classification system may offer some guidance to DPT system operators regarding which measurements to prioritize. DPT developers and operators may also make conscious decisions to integrate measures for epidemic monitoring but should be aware that this introduces a secondary purpose to DPT. Ultimately, the integration of further information (e.g., regarding exact exposure time) into DPT involves a trade-off between data granularity and linkage on the one hand, and privacy on the other. More data may lead to better epidemiological information but may also increase the privacy risks associated with the system, and thus decrease public DPT acceptance. Decision-makers should be aware of the trade-off and take it into account when planning and developing DPT systems or intending to assess the added value of DPT relative to the existing contact tracing systems. |
format | Online Article Text |
id | pubmed-8521913 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85219132021-10-27 Toward a Common Performance and Effectiveness Terminology for Digital Proximity Tracing Applications Lueks, Wouter Benzler, Justus Bogdanov, Dan Kirchner, Göran Lucas, Raquel Oliveira, Rui Preneel, Bart Salathé, Marcel Troncoso, Carmela von Wyl, Viktor Front Digit Health Digital Health Digital proximity tracing (DPT) for Sars-CoV-2 pandemic mitigation is a complex intervention with the primary goal to notify app users about possible risk exposures to infected persons. DPT not only relies on the technical functioning of the proximity tracing application and its backend server, but also on seamless integration of health system processes such as laboratory testing, communication of results (and their validation), generation of notification codes, manual contact tracing, and management of app-notified users. Policymakers and DPT operators need to know whether their system works as expected in terms of speed or yield (performance) and whether DPT is making an effective contribution to pandemic mitigation (also in comparison to and beyond established mitigation measures, particularly manual contact tracing). Thereby, performance and effectiveness are not to be confused. Not only are there conceptual differences but also diverse data requirements. For example, comparative effectiveness measures may require information generated outside the DPT system, e.g., from manual contact tracing. This article describes differences between performance and effectiveness measures and attempts to develop a terminology and classification system for DPT evaluation. We discuss key aspects for critical assessments of whether the integration of additional data measurements into DPT apps may facilitate understanding of performance and effectiveness of planned and deployed DPT apps. Therefore, the terminology and a classification system may offer some guidance to DPT system operators regarding which measurements to prioritize. DPT developers and operators may also make conscious decisions to integrate measures for epidemic monitoring but should be aware that this introduces a secondary purpose to DPT. Ultimately, the integration of further information (e.g., regarding exact exposure time) into DPT involves a trade-off between data granularity and linkage on the one hand, and privacy on the other. More data may lead to better epidemiological information but may also increase the privacy risks associated with the system, and thus decrease public DPT acceptance. Decision-makers should be aware of the trade-off and take it into account when planning and developing DPT systems or intending to assess the added value of DPT relative to the existing contact tracing systems. Frontiers Media S.A. 2021-08-05 /pmc/articles/PMC8521913/ /pubmed/34713149 http://dx.doi.org/10.3389/fdgth.2021.677929 Text en Copyright © 2021 Lueks, Benzler, Bogdanov, Kirchner, Lucas, Oliveira, Preneel, Salathé, Troncoso and von Wyl. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Digital Health Lueks, Wouter Benzler, Justus Bogdanov, Dan Kirchner, Göran Lucas, Raquel Oliveira, Rui Preneel, Bart Salathé, Marcel Troncoso, Carmela von Wyl, Viktor Toward a Common Performance and Effectiveness Terminology for Digital Proximity Tracing Applications |
title | Toward a Common Performance and Effectiveness Terminology for Digital Proximity Tracing Applications |
title_full | Toward a Common Performance and Effectiveness Terminology for Digital Proximity Tracing Applications |
title_fullStr | Toward a Common Performance and Effectiveness Terminology for Digital Proximity Tracing Applications |
title_full_unstemmed | Toward a Common Performance and Effectiveness Terminology for Digital Proximity Tracing Applications |
title_short | Toward a Common Performance and Effectiveness Terminology for Digital Proximity Tracing Applications |
title_sort | toward a common performance and effectiveness terminology for digital proximity tracing applications |
topic | Digital Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8521913/ https://www.ncbi.nlm.nih.gov/pubmed/34713149 http://dx.doi.org/10.3389/fdgth.2021.677929 |
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