The goal of this project is to develop an artificial intelligence-based data analytics and cloud computing platform, paired with U.S. Food and Drug Administration (FDA)-cleared wearable devices, to create a personalized baseline index that could indicate a change in health status for patients who have tested COVID-19 positive. The project involves the development and validation of a COVID-19 Decompensation Index (CDI) that builds off physIQ’s existing wearable biosensor-derived analytics platform. Data will be collected from 400 human subjects that are both pre-hospitalization subjects (found to be positive for COVID-19) and subjects that have been hospitalized and treated for COVID and then discharged. This combined population will consist of COVID-19 decompensation cases (event cases) and cases for which COVID-19 did not result in any kind of decompensation (non-event cases). The 400-patient dataset will be partitioned into a training subset and a testing subset. Performance will be assessed using receiver operator characteristics (ROC) area under the curve (AUC) as the metric of performance. Data collected under this project will be deidentified and securely transmitted to an NIH data hub.