  Through the use of cutting-edge unsupervised classification techniques from
statistics and machine learning, we characterise symptom phenotypes among
symptomatic SARS-CoV-2 PCR-positive community cases. We first analyse each
dataset in isolation and across age bands, before using methods that allow us
to compare multiple datasets. While we observe separation due to the total
number of symptoms experienced by cases, we also see a separation of symptoms
into gastrointestinal, respiratory and other types, and different symptom
co-occurrence patterns at the extremes of age. In this way, we are able to
demonstrate the deep structure of symptoms of COVID-19 without usual biases due
to study design. This is expected to have implications for the identification
and management of community SARS-CoV-2 cases and could be further applied to
symptom-based management of other diseases and syndromes.
