  Our main contributions are as follows: (I) We demonstrate the first attempt
to diagnose COVID-19 using end-to-end deep learning from a crowd-sourced
dataset of audio samples, achieving ROC-AUC of 0.846; (II) Our model, the
COVID-19 Identification ResNet, (CIdeR), has potential for rapid scalability,
minimal cost and improving performance as more data becomes available. This
could enable regular COVID-19 testing at apopulation scale; (III) We introduce
a novel modelling strategy using a custom deep neural network to diagnose
COVID-19 from a joint breath and cough representation; (IV) We release our four
stratified folds for cross parameter optimisation and validation on a standard
public corpus and details on the models for reproducibility and future
reference.
