Accepted for/Published in: JMIR Public Health and Surveillance
Date Submitted: Apr 15, 2020
Date Accepted: Jul 28, 2020
Date Submitted to PubMed: Jul 31, 2020
Privacy-assured, Freely Available COVID-19 Symptom Tracker for Public Safety During Reopening: Feasibility and Functionality with Rapid Deployment
ABSTRACT
Background:
There has been an exponential increase in SARS-CoV-2 related COVID-19 cases in many parts of the United States. Not all individuals require hospitalization, but monitoring those with illness being managed under home isolation and exposed individuals under home quarantine is a critical component of the pandemic response to prevent ongoing transmission. Traditional contact tracing mechanisms are not structured to be implemented at the scale needed to successfully address the SARS-CoV-2 pandemic. Under the current mitigation and containment plans including during the re-opening of businesses, institutions and agencies that have not traditionally participated in the role of disease prevention are also tasked with ensuring the safety of their workers and community at large. Systems to support agencies and institutions faced with these challenges are critically needed.
Objective:
Currently available symptom trackers use a direct to consumer approach and have generally sought to promote testing or healthcare uptake, but most also collect personal identifiers and have raised privacy concerns. We designed a symptom monitoring and reporting system for rapid deployment in the context of the SARS-CoV-2 pandemic to support contact tracing and monitoring efforts (www.covidgu.org, released March 20, 2020).
Methods:
We conducted a pilot study among consenting Georgetown University medical students. Students were informed about the opportunity to participate through an email sent from the Georgetown University School of Medicine. Interested participants were referred to a description of the study using Qualtrics. Individuals who agreed to participate were given an unique identifier generated in Qualtrics and were given instructions to enter symptoms twice daily for a three day period.
Results:
A total of forty eight individuals entered data into the system from March 31 – April 5, 2020. One person reported active COVID-19 infection, and had no symptoms by the end of the monitoring period. The remainder of the participants did not report any symptoms. Only data with our beta test unique entity identifier was generated in the summary reports.
Conclusions:
The system was designed using a public-private-academia model, and harnesses insights on privacy and data sharing to avoid regulatory and legal hurdles to rapid adaption by entities tasked with maintaining public safety. No personal identifiers are collected in the system, and an efficient on-boarding process and limited terms of use provides institutions and agencies ready access to this free system. The reporter mechanism safeguards access to data specific to each entity. Entities have immediate and on-demand access to data entered by their constituents organized in summary reports that highlight actionable data. Our pilot study demonstrated feasibility and ease of use with automated instructions. Further refinements based on early adapters of the system have been made and include release of a Spanish language version. Clinical Trial: N/A
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.