Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment

Sharon Levy, William Yang Wang

Keywords: COVID-19, cross-lingual, transfer learning, social media

TL;DR: We propose cross-lingual transfer learning with tweets for epidemiological outbreak alignment across countries and obtain up to 0.85 Spearman correlation in cross-country predictions.

Abstract: 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. 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.