  Using a random 10% sample of tweets authored from 2019-09-01 through
2020-04-30, we analyze the dynamic behavior of words (1-grams) used on Twitter
to describe the ongoing COVID-19 pandemic. Across 24 languages, we find two
distinct dynamic regimes: One characterizing the rise and subsequent collapse
in collective attention to the initial Coronavirus outbreak in late January,
and a second that represents March COVID-19-related discourse. Aggregating
countries by dominant language use, we find that volatility in the first
dynamic regime is associated with future volatility in new cases of COVID-19
roughly three weeks (average 22.49 $\pm$ 3.26 days) later. Our results suggest
that surveillance of change in usage of epidemiology-related words on social
media may be useful in forecasting later change in disease case numbers, but we
emphasize that our current findings are not causal or necessarily predictive.
