学术文献库

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. 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. 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 - beyond what is required to fulfill its primary notification role - may facilitate an understanding of performance and effectiveness of planned and deployed DPT apps. Therefore, the terminology and a classification matrix 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 that is not part of the original DPT design. Ultimately, the integration of further information for epidemic monitoring into DPT involves a trade-off between data granularity and linkage on the one hand, and privacy on the other. Decision-makers should be aware of the trade-off and take it into account when planning and developing DPT notification and monitoring systems or intending to assess the added value of DPT relative to existing contact tracing systems.