Your browser doesn't support javascript.
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.
  • Lueks W; Security and Privacy Engineering Laboratory, School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
  • Benzler J; Robert Koch Institute, Berlin, Germany.
  • Bogdanov D; Cybernetica AS, Tartu, Estonia.
  • Kirchner G; Robert Koch Institute, Berlin, Germany.
  • Lucas R; Medical School and Institute of Public Health (EPIUnit), Universidade Do Porto, Porto, Portugal.
  • Oliveira R; Institute for Systems and Computer Engineering, Technology and Science & University of Minho, Porto, Portugal.
  • Preneel B; Department of Electrical Engineering, Katholieke Universiteit Leuven and IMEC, Leuven, Belgium.
  • Salathé M; Digital Epidemiology Laboratory, School of Life Sciences, School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, Global Health Institute, Geneva, Switzerland.
  • Troncoso C; Security and Privacy Engineering Laboratory, School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
  • von Wyl V; Digital and Mobile Health Group, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
Front Digit Health ; 3: 677929, 2021.
Article in English | MEDLINE | ID: covidwho-1497053
ABSTRACT
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.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Prognostic study Language: English Journal: Front Digit Health Year: 2021 Document Type: Article Affiliation country: Fdgth.2021.677929

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Prognostic study Language: English Journal: Front Digit Health Year: 2021 Document Type: Article Affiliation country: Fdgth.2021.677929