  Rapidly scaling screening, testing and quarantine has shown to be an
effective strategy to combat the COVID-19 pandemic. We consider the application
of deep learning techniques to distinguish individuals with COVID from
non-COVID by using data acquirable from a phone. Using cough and context
(symptoms and meta-data) represent such a promising approach. Several
independent works in this direction have shown promising results. However, none
of them report performance across clinically relevant data splits.
Specifically, the performance where the development and test sets are split in
time (retrospective validation) and across sites (broad validation). Although
there is meaningful generalization across these splits the performance
significantly varies (up to 0.1 AUC score). In addition, we study the
performance of symptomatic and asymptomatic individuals across these three
splits. Finally, we show that our model focuses on meaningful features of the
input, cough bouts for cough and relevant symptoms for context. The code and
checkpoints are available at https://github.com/WadhwaniAI/cough-against-covid
