  Recent work has shown the potential of using audio data (eg, cough,
breathing, and voice) in the screening for COVID-19. However, these approaches
only focus on one-off detection and detect the infection given the current
audio sample, but do not monitor disease progression in COVID-19. Limited
exploration has been put forward to continuously monitor COVID-19 progression,
especially recovery, through longitudinal audio data. Tracking disease
progression characteristics could lead to more timely treatment.
  The primary objective of this study is to explore the potential of
longitudinal audio samples over time for COVID-19 progression prediction and,
especially, recovery trend prediction using sequential deep learning
techniques.
  Crowdsourced respiratory audio data, including breathing, cough, and voice
samples, from 212 individuals over 5-385 days were analyzed. We developed a
deep learning-enabled tracking tool using gated recurrent units (GRUs) to
detect COVID-19 progression by exploring the audio dynamics of the individuals'
historical audio biomarkers. The investigation comprised 2 parts: (1) COVID-19
detection in terms of positive and negative (healthy) tests, and (2)
longitudinal disease progression prediction over time in terms of probability
of positive tests.
  The strong performance for COVID-19 detection, yielding an AUROC of 0.79, a
sensitivity of 0.75, and a specificity of 0.71 supported the effectiveness of
the approach compared to methods that do not leverage longitudinal dynamics. We
further examined the predicted disease progression trajectory, displaying high
consistency with test results with a correlation of 0.75 in the test cohort and
0.86 in a subset of the test cohort who reported recovery. Our findings suggest
that monitoring COVID-19 evolution via longitudinal audio data has potential in
the tracking of individuals' disease progression and recovery.
