Toward a Common Performance and Effectiveness Terminology for Digital Proximity Tracing Applications.

Area of Science:

• Epidemiology
• Public Health Technology
• Health Systems Research

Background:

• Digital proximity tracing (DPT) is a key tool for Sars-CoV-2 pandemic mitigation, aiming to alert users about potential exposures.
• DPT success depends on technical functionality and integration with health system processes like testing and contact tracing.
• Evaluating DPT requires distinguishing between system performance (speed, yield) and public health effectiveness.

Purpose of the Study:

• To differentiate between performance and effectiveness measures for DPT systems.
• To propose a terminology and classification system for DPT evaluation.
• To guide policymakers and DPT operators in assessing DPT's impact and optimizing its implementation.

Main Methods:

• Conceptual analysis of DPT performance and effectiveness metrics.
• Development of a classification system for DPT evaluation.
• Discussion of data requirements and integration challenges for DPT assessment.

Main Results:

• Performance and effectiveness of DPT are distinct concepts with different data needs.
• A standardized terminology and classification system can aid DPT evaluation.
• Integrating additional data into DPT apps can enhance understanding but raises privacy concerns.

Conclusions:

• Clear evaluation frameworks are crucial for understanding DPT's role in pandemic response.
• DPT system design must balance data granularity for epidemiological insights with user privacy and acceptance.
• Policymakers need to consider the trade-offs when planning and assessing DPT systems.