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.

翻译：全球已有数百万人因COVID-19死亡。除高致死率外，该疾病还给患者个体带来难以承受的痛苦，并给医疗系统造成巨大的全球性负担。为此，研究人员致力于开发可远程检测这一人传人疾病症状的工具，以控制其快速传播。咳嗽作为常见症状之一，一直是研究人员试图通过智能手机麦克风传感进行客观检测的重点对象。现有咳嗽症状检测与追踪方法大多依赖基于大量患者数据训练的机器学习模型，这在疫情爆发初期难以实现。本研究提出一种增量式迁移学习方法，通过利用健康人群咳嗽与COVID-19患者咳嗽之间的关联性，基于预训练的健康咳嗽检测模型和相对少量的患者咳嗽数据，以合理准确度检测COVID-19咳嗽症状，从而降低模型训练对大规模患者数据集的需求。此类模型有望成为检测新型呼吸道病毒爆发的变革性工具。