This study is designed to be administered in a decentralized manner to collect self-reported COVID-19 symptoms, cough sample recordings and descriptions of recent medical treatment to investigate the following:~Whether patterns unique to COVID-19 disease can be detected in cough sounds collected over time;~The incidence of COVID-19 and other lung diseases within individuals who test negative for COVID-19;~Whether existing machine learning algorithms can detect the presence of disease in the lower respiratory tract in participants; and~Whether distinct cough sound patterns in COVID-19 disease are associated with the requirement of medical treatment for COVID-19 disease. Through the administration of Study 1, a list of potential participants will be created for whom the result of a recent rt-qPCR COVID-19 test is known. Participants have been consented to allow for outreach regarding further studies. Cough samples and answers to medical questions have been collected from these individuals at the time of testing, and the aim of Study 2 is to gather further cough samples and questionnaire answers over a period of 25 days.
Decentralized clinical study designed to collect further cough sounds, self-reported symptoms, and medical treatment questionnaires from participants enrolled on the COVID-Cough Study (Study 1).~The aim of this further data collection study (Study 2) is to:~develop an understanding of changes in cough sounds associated with COVID-19 and how they alter during the disease;~develop an understanding of other causes of COVID-19-like symptoms and their associated cough sound patterns; and~gain a broader understanding of the clinical outcomes of individuals who present for COVID-19 testing.