  OBJECTIVE: Our objective is to evaluate the possibility of using cough audio
recordings (spontaneous or simulated) to detect sound patterns in people who
are diagnosed with COVID-19. The research question that led our work was: what
is the sensitivity and specificity of a machine learning based COVID-19 cough
classifier, using RT-PCR tests as gold standard?
  SETTING: The audio samples that were collected for this study belong to
individuals who were swabbed in the City of Buenos Aires in 20 public and 1
private facilities where RT-PCR studies were carried out on patients suspected
of COVID, and 14 out-of-hospital isolation units for patients with confirmed
COVID mild cases. The audios were collected through the Buenos Aires city
government WhatsApp chatbot that was specifically designed to address citizen
inquiries related to the coronavirus pandemic (COVID-19).
  PARTICIPANTS: The data collected corresponds to 2821 individuals who were
swabbed in the City of Buenos Aires, between August 11 and December 2, 2020.
Individuals were divided into 1409 that tested positive for COVID-19 and 1412
that tested negative. From this sample group, 52.6% of the individuals were
female and 47.4% were male. 2.5% were between the age of 0 and 20 , 61.1%
between the age of 21 and 40 , 30.3% between the age of 41 and 60 and 6.1% were
over 61 years of age.
  RESULTS: Using the dataset of 2821 individuals our results showed that the
neural network classifier was able to discriminate between the COVID-19
positive and the healthy coughs with an accuracy of 88%. This accuracy obtained
during the training process was later tested and confirmed with a second
dataset corresponding to 492 individuals.
