As COVID-19 continues to inflict suffering worldwide, data is a powerful tool to track and combat its spread. Previous research has demonstrated that wearable technologies can detect several physiologic and behavioral changes when a user becomes infected with influenza, from heightened resting heart rate to disturbed sleep. These “digital biomarkers” form a signature of infection that can help public health officials track the spread of infectious diseases such as COVID-19 and target diagnostic testing.

This team expanded on the work of the BIG IDEAs Lab CovIdentify study, which was launched in April 2020 to develop an early detection model for SARS-CoV-2 based on wearables data. They worked to house and develop actionable insights from the data as well as develop a cloud-based infrastructure on Microsoft Azure. One subteam constructed an end-to-end data pipeline to preprocess wearable data for analysis; another built data visualization dashboards that connect to real-time survey data to monitor study activity.

Constructing Cloud-based Infrastructure for COVID-19 Data

Poster by Qi Xuan Khoo, Yvonne Kuo, Tommy Tseng, Amrita Lakhanpal, Sean Fiscus, Peter Cho, Md Mobashir Hasan Shandhi, Ali Roghanizad and Jessilyn Dunn