  Millions of people have died worldwide from COVID-19. In addition to its high
death toll, COVID-19 has led to unbearable suffering for individuals and a huge
global burden to the healthcare sector. Therefore, researchers have been trying
to develop tools to detect symptoms of this human-transmissible disease
remotely to control its rapid spread. Coughing is one of the common symptoms
that researchers have been trying to detect objectively from smartphone
microphone-sensing. While most of the approaches to detect and track cough
symptoms rely on machine learning models developed from a large amount of
patient data, this is not possible at the early stage of an outbreak. In this
work, we present an incremental transfer learning approach that leverages the
relationship between healthy peoples' coughs and COVID-19 patients' coughs to
detect COVID-19 coughs with reasonable accuracy using a pre-trained healthy
cough detection model and a relatively small set of patient coughs, reducing
the need for large patient dataset to train the model. This type of model can
be a game changer in detecting the onset of a novel respiratory virus.
