Mining Project: Evaluation of Mobile Applications for Digital Contact Tracing
To develop guidelines for the design, implementation, and evaluation of digital contact tracing tools through laboratory and field testing under occupational settings and to investigate human factor considerations for adopting such tools.
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 will evaluate the viability of these technologies for contact tracing in grocery store and food manufacturing settings and will develop guidance on these engineering and human factors considerations.
Guidelines will be developed in several key technical areas, including methods of minimizing the rates of false positives and false negatives due to factors such as multipath interference, spatial blockage between devices, performance differences between use indoors versus outdoors, performance differences between phone models, slow update rates, and the ability of signals to pass through walls which creates the possibility of detecting someone in an adjacent room or on another floor. In addition to these technical considerations, the project will conduct human factors investigations to understand perceptions of this technology. It is suspected that an important topic in this guidance will be the design of systems to protect privacy while still providing useful data. 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 guidance generated under this study will include guidance on how to balance these concerns to protect user privacy while still providing useful data for public health research.
In this project, the above issues will be addressed by way of three research aims, as follows:
- To identify performance requirements for digital contact tracing applications
- To quantify the performance of representative digital contact tracing applications under laboratory conditions and in occupational settings
- To use the technology acceptance model (TAM) to identify perceptions that affect technology adoption.
The first two aims will be accomplished through laboratory experiments not involving human subjects, while the third aim will be accomplished through a three-phase study involving interviews and surveys with grocery store and food manufacturing workers. In the first phase, the team will conduct formative research by completing elicitation interviews with grocery store and food manufacturing workers to identify their perceptions of and experiences with contact tracing applications. In the second phase, the team will transcribe and analyze the interviews. The interview results will be used with the TAM to develop a survey instrument to be used in the third phase. In the third phase, this survey will be administered to grocery store and food manufacturing workers.
The anticipated outcomes of this research will be centered on guidance developed from quantitative and qualitative findings that can be used to provide recommendations on the implementation and use of digital contact tracing technologies. The guidance developed from the technology evaluations and behavioral investigations will provide the public with timely and critical information to promote health and safety practices on a global scale. The outcomes will be tracked through documented use of the guidance in developed applications and through data on the adoption and use of such applications.
- Best Practices for Dust Control in Coal Mining
- Best Practices for Dust Control in Coal Mining. Second edition.
- Best Practices for Dust Control in Metal/Nonmetal Mining
- Criteria for a Recommended Standard: Occupational Exposure to Respirable Coal Mine Dust: Occupational Exposure to Respirable Coal Mine Dust
- Development of a Field Method for Measuring Manganese in Welding Fume
- Diesel Exhaust
- Evaluation of the Approach to Respirable Quartz Exposure Control in U.S. Coal Mines
- Feasibility Testing of a Near Real Time Respirable Silica Monitor