  With the prevailing efforts to combat the coronavirus disease 2019 (COVID-19)
pandemic, there are still uncertainties that are yet to be discovered about its
spread, future impact, and resurgence. In this paper, we present a three-stage
data-driven approach to distill the hidden information about COVID-19. The
first stage employs a Bayesian network structure learning method to identify
the causal relationships among COVID-19 symptoms and their intrinsic
demographic variables. As a second stage, the output from the Bayesian network
structure learning, serves as a useful guide to train an unsupervised machine
learning (ML) algorithm that uncovers the similarities in patients' symptoms
through clustering. The final stage then leverages the labels obtained from
clustering to train a demographic symptom identification (DSID) model which
predicts a patient's symptom class and the corresponding demographic
probability distribution. We applied our method on the COVID-19 dataset
obtained from the Centers for Disease Control and Prevention (CDC) in the
United States. Results from the experiments show a testing accuracy of 99.99%,
as against the 41.15% accuracy of a heuristic ML method. This strongly reveals
the viability of our Bayesian network and ML approach in understanding the
relationship between the virus symptoms, and providing insights on patients'
stratification towards reducing the severity of the virus.
