  During an infectious disease pandemic, it is critical to share electronic
medical records or models (learned from these records) across regions. Applying
one region's data/model to another region often have distribution shift issues
that violate the assumptions of traditional machine learning techniques.
Transfer learning can be a solution. To explore the potential of deep transfer
learning algorithms, we applied two data-based algorithms (domain adversarial
neural networks and maximum classifier discrepancy) and model-based transfer
learning algorithms to infectious disease detection tasks. We further studied
well-defined synthetic scenarios where the data distribution differences
between two regions are known. Our experiments show that, in the context of
infectious disease classification, transfer learning may be useful when (1) the
source and target are similar and the target training data is insufficient and
(2) the target training data does not have labels. Model-based transfer
learning works well in the first situation, in which case the performance
closely matched that of the data-based transfer learning models. Still, further
investigation of the domain shift in real world research data to account for
the drop in performance is needed.
