  The novel coronavirus (SARS-CoV-2) has led to a pandemic. The current testing
regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2
has been unable to keep up with testing demands, and also suffers from a
relatively low positive detection rate in the early stages of the resultant
COVID-19 disease. Hence, there is a need for an alternative approach for
repeated large-scale testing of SARS-CoV-2/COVID-19. We propose a framework
called CovidDeep that combines efficient DNNs with commercially available WMSs
for pervasive testing of the virus. We collected data from 87 individuals,
spanning three cohorts including healthy, asymptomatic, and symptomatic
patients. We trained DNNs on various subsets of the features automatically
extracted from six WMS and questionnaire categories to perform ablation studies
to determine which subsets are most efficacious in terms of test accuracy for a
three-way classification. The highest test accuracy obtained was 98.1%. We also
augmented the real training dataset with a synthetic training dataset drawn
from the same probability distribution to impose a prior on DNN weights and
leveraged a grow-and-prune synthesis paradigm to learn both DNN architecture
and weights. This boosted the accuracy of the various DNNs further and
simultaneously reduced their size and floating-point operations.
