  The global spread of COVID-19 had severe consequences for public health and
the world economy. The quick onset of the pandemic highlighted the potential
benefits of cheap and deployable pre-screening methods to monitor the
prevalence of the disease in a population. Various researchers made use of
machine learning methods in an attempt to detect COVID-19. The solutions
leverage various input features, such as CT scans or cough audio signals, with
state-of-the-art results arising from deep neural network architectures.
However, larger models require more compute; a pertinent consideration when
deploying to the edge. To address this, we first recreated two models that use
cough audio recordings to detect COVID-19. Through applying network pruning and
quantisation, we were able to compress these two architectures without reducing
the model's predictive performance. Specifically, we were able to achieve an
105.76x and an 19.34x reduction in the compressed model file size with
corresponding 1.37x and 1.71x reductions in the inference times of the two
models.
