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Using a random 10% sample of tweets authored from 2019-09-01 through2020-04-30, we analyze the dynamic behavior of words (1-grams) used on Twitterto describe the ongoing COVID-19 pandemic. Across 24 languages, we find twodistinct dynamic regimes: One characterizing the rise and subsequent collapsein collective attention to the initial Coronavirus outbreak in late January,and a second that represents March COVID-19-related discourse. Aggregatingcountries by dominant language use, we find that volatility in the firstdynamic regime is associated with future volatility in new cases of COVID-19roughly three weeks (average 22.49 $\pm$ 3.26 days) later. Our results suggestthat surveillance of change in usage of epidemiology-related words on socialmedia may be useful in forecasting later change in disease case numbers, but weemphasize that our current findings are not causal or necessarily predictive.

Divergent modes of online collective attention to the COVID-19 pandemic are associated with future caseload variance