Mining Project: Evaluation of Mobile Applications for Digital Contact Tracing

Research Summary

Coronavirus disease 2019 (COVID-19) is characterized as a respiratory illness that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) and causes symptoms including shortness of breath, fever, cough, chills, muscle pain, loss of taste or smell, or sore throat. The virus primarily spreads from person-to-person through close contact and exposure to droplets from infected, symptomatic, and asymptomatic individuals. While many were required to stay at home due to state-issued orders, essential workers have continued to work in public settings where there is an increased risk for exposure to COVID-19.

Workers in the grocery store and food manufacturing industries have unique considerations for exposure. Grocery store workers may come in contact with customers throughout their workday. In the event that a grocery store worker is diagnosed with COVID-19, it would be practically impossible to identify which individuals that person had been in close contact with. While food manufacturing workers do not regularly interact with the public, densely populated work facilities may make it difficult to trace and prevent virus spread among workers.

Effective contact tracing is critical in the response to a pandemic, and with the COVID-19 pandemic, new tools have emerged that aid contact tracing efforts by automatically logging contacts based on the relative proximity of mobile electronic devices. While mobile applications for contact tracing offer a potential tool to aid in limiting the spread of the disease, there is currently limited information on the functionality, effectiveness, and acceptance of these emerging technologies. Limited research on digital contact tracing was conducted with past epidemics, but much research has been done recently as COVID-19 has spread. Still, with the emerging nature of this technology, little is known about the acceptance, benefits, and potential consequences of using digital contact tracing. Success of digital contact tracing technologies depends on widespread adoption, raising concerns that the impact of the applications will be limited by low adoption rates. This project evaluated the viability of these technologies for contact tracing in grocery store and food manufacturing settings with the aim of developing guidance on these engineering and human factors considerations.

Research results from this project can serve as guidance in several key technical areas, including factors that affect the rates of false positives and false negatives, such as multipath interference, spatial blockage between devices, performance differences between use indoors versus outdoors, and performance differences between phone models. In addition to these technical considerations, the project team conducted human factors investigations to understand perceptions related to this technology. A key factor in the effectiveness of this technology will be technology adoption, which is strongly affected by user trust and concerns over privacy. The results of this study can help inform ways to balance concerns, such as the protection of user privacy with providing useful data for public health research.

In this project, the above issues were addressed by way of three research aims, as follows:

1. To identify performance requirements for digital contact tracing applications
2. To quantify the performance of representative digital contact tracing applications under laboratory conditions and in occupational settings
3. To use the technology acceptance model (TAM) to identify perceptions that affect technology adoption.

The first two aims were accomplished through laboratory experiments not involving human subjects, while the third aim was accomplished through a study involving a survey based on the TAM with grocery store and food manufacturing workers.

The qualitative and quantitative findings of this research can provide guidance to provide recommendations on the implementation and use of digital contact tracing technologies for this and future pandemics.