SAN MATEO, Calif., July 17, 2019 – Evidation Health, a health and measurement company that helps innovative life sciences and health care companies understand how everyday behaviors and health interact, has received an award from the U.S. Department of Health & Human Services (HHS) ASPR/BARDA/DRIVe to build upon its prior flu research and create a machine learning model to forecast influenza prior to the onset of symptoms.
Evidation’s research will utilize person-generated health data and population-based models to improve real-time respiratory infection tracking at the individual and population level. Evidation’s previous research indicates that significant changes in physical activity and sleep patterns closely track the onset of influenza symptoms and lasts up to seven days after symptoms have resolved.
This research has the potential to pave the way for an extensive clinical trial that will enable individuals to monitor their respiratory disease symptoms and take precautions against the spread of such diseases, potentially days before symptoms appear.
“Current methods for monitoring the flu mostly rely on tracking medically attended events and typically data becomes available several days after symptom onset,” said Luca Foschini, Ph.D., Evidation’s co-founder and chief data scientist. “This new project will use novel behavioral and physiological data to more effectively track when and where people may get the flu.”
The up to $749,000 award is part of HHS’s Assistant Secretary for Preparedness and Response (ASPR) Biomedical Advanced Research and Development Authority’s (BARDA), Division of Research, Innovation, and Ventures (DRIVe) Early Notification to Act Control and Treat (ENACT) program. Evidation’s award is part of the ‘Powered by DRIVe’ initiative to invest in disruptive innovations that will protect Americans against health security threats.
Machine learning, made possible with the Evidation data platform, will analyze behavior data, including sleep and activity patterns, to understand changes associated with clinically validated diagnoses. The models will also distinguish between respiratory virus infections and influenza.