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Millions of people have died worldwide from COVID-19. In addition to its highdeath toll, COVID-19 has led to unbearable suffering for individuals and a hugeglobal burden to the healthcare sector. Therefore, researchers have been tryingto develop tools to detect symptoms of this human-transmissible diseaseremotely to control its rapid spread. Coughing is one of the common symptomsthat researchers have been trying to detect objectively from smartphonemicrophone-sensing. While most of the approaches to detect and track coughsymptoms rely on machine learning models developed from a large amount ofpatient data, this is not possible at the early stage of an outbreak. In thiswork, we present an incremental transfer learning approach that leverages therelationship between healthy peoples' coughs and COVID-19 patients' coughs todetect COVID-19 coughs with reasonable accuracy using a pre-trained healthycough detection model and a relatively small set of patient coughs, reducingthe need for large patient dataset to train the model. This type of model canbe a game changer in detecting the onset of a novel respiratory virus.

Transfer Learning to Detect COVID-19 Coughs with Incremental Addition of Patient Coughs to Healthy People's Cough Detection Models