  The current COVID-19 pandemic is now getting contained, albeit at the cost of
morethan2.3million human lives. A critical phase in any pandemic is the early
detection of cases to develop preventive treatments and strategies. In the case
of COVID-19,several studies have indicated that chest radiography images of the
infected patients show characteristic abnormalities. However, at the onset of a
given pandemic, such asCOVID-19, there may not be sufficient data for the
affected cases to train models for their robust detection. Hence, supervised
classification is ill-posed for this problem because the time spent in
collecting large amounts of data from infected persons could lead to the loss
of human lives and delays in preventive interventions. Therefore, we formulate
the problem of identifying early cases in a pandemic as an anomaly detection
problem, in which the data for healthy patients is abundantly available,
whereas no training data is present for the class of interest (COVID-19 in our
case). To solve this problem, we present several unsupervised deep learning
approaches, including convolutional and adversarially trained autoencoder. We
tested two settings on a publicly available dataset (COVIDx)by training the
model on chest X-rays from (i) only healthy adults, and (ii) healthy and other
non-COVID-19 pneumonia, and detected COVID-19 as an anomaly.
Afterperforming3-fold cross validation, we obtain a ROC-AUC of0.765. These
results are very encouraging and pave the way towards research for ensuring
emergency preparedness in future pandemics, especially the ones that could be
detected from chest X-rays
