TemPredict at UCSD
By the time the COVID-19 pandemic forced shelter in place warnings around California, the virus was already well past containment. First-line medical responders were already running short of space and supplies, and yet the pandemic was still in it's early stages. The situation got bad fast, in large part due to a lack of actionable information. Who is sick, how sick, where should resources go, when should a care giver step back and isolate?
Using wearable sensors to generate continuous temperature recordings and nightly heart rate, heart rate variability, and respiration rate recordings, collaborators at UCSF gathered physiological data and combined these with self-reported daily symptom questionnaire responses. Roughly 50,000 people joined the effort, donating months of continuous data and over a million symptom reports. The goal is now to build models of illness progression, and more importantly, onset and severity prediction.
As the lead at UCSD, I am collaborating with Dr. Ilkay Altintas at SDSC to make our data available in a convenient, well-structured model to enable easy collaborations. Beyond this crisis, we want our learnings to serve as a template for how large biological time series data can be managed to optimize discovery. More efforts like this one are coming, and knowing how to handle all that data is its own challenge, aside from analyzing it for biomedical applications.
We welcome those interested in aiding either the data management or biomedical analysis efforts.
The first TemPredict publication came out in 2020, showing for the first time that wearable devices should be used to support fever detection in broad, distributed populations.
Many more papers will come out of these data, both from us, and from collaborator efforts. As they come out, in press or in pre-print servers, I'll update this list (when I remember - but see my google scholar feed for a presumably better list).