Realizing the Promise of Automated Exposure Notification (AEN) Technology to Control the Spread of COVID-19: Recommendations for Smartphone App Deployment, Use, and Iterative Assessment

TL;DR

This paper provides guidelines and recommendations for deploying, using, and assessing Automated Exposure Notification (AEN) apps to effectively control COVID-19 spread, emphasizing privacy, human factors, and iterative improvements.

Contribution

It offers a practical design, human factors considerations, and assessment strategies for AEN system deployment, including a case study of Pennsylvania's app.

Findings

Low user adoption impacts effectiveness

Human factors influence app usage and compliance

Iterative assessment can improve deployment outcomes

Abstract

By using modern cryptographic techniques, privacy-preserving Automated Exposure Notification (AEN) technologies offer the promise of mitigating disease spread by automatically recording contacts between people over the incubation period while maintaining individual data privacy. Today, public health departments in States and other countries around the world are deploying AEN systems at a rapid pace. Though many organizations conducted research prior to deploying apps, experience around the world shows that contact-tracing apps are installed and used at relatively low levels. This whitepaper is intended to provide usable information for States who are considering the deployment of an AEN system, as well as to guide ongoing improvements for States that have already deployed. We outline the human factors considerations related to employing AEN systems with the ultimate goal of controlling the spread of COVID-19, including the GAEN consortium Exposure Notifications (EN) Express tool. We will also provide a practical design and implementation guide for States and others designing and deploying AEN systems, as well as a set of recommendations for assessing deployment of contact tracing apps and targeting areas of concern to improve efficacy of use during and after initial deployment. As a case study, we consider the commercial app deployed by the state of Pennsylvania (PA) and the ongoing efforts to drive user adoption there.