  The spread of COVID-19 has become a significant and troubling aspect of
society in 2020. With millions of cases reported across countries, new
outbreaks have occurred and followed patterns of previously affected areas.
Many disease detection models do not incorporate the wealth of social media
data that can be utilized for modeling and predicting its spread. In this case,
it is useful to ask, can we utilize this knowledge in one country to model the
outbreak in another? To answer this, we propose the task of cross-lingual
transfer learning for epidemiological alignment. Utilizing both macro and micro
text features, we train on Italy's early COVID-19 outbreak through Twitter and
transfer to several other countries. Our experiments show strong results with
up to 0.85 Spearman correlation in cross-country predictions.
