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We present a novel approach incorporating transformer-based language modelsinto infectious disease modelling. Text-derived features are quantified bytracking high-density clusters of sentence-level representations of Redditposts within specific US states' COVID-19 subreddits. We benchmark theseclustered embedding features against features extracted from other high-qualitydatasets. In a threshold-classification task, we show that they outperform allother feature types at predicting upward trend signals, a significant resultfor infectious disease modelling in areas where epidemiological data isunreliable. Subsequently, in a time-series forecasting task we fully utilisethe predictive power of the caseload and compare the relative strengths ofusing different supplementary datasets as covariate feature sets in atransformer-based time-series model.

Forecasting COVID-19 Caseloads Using Unsupervised Embedding Clusters of Social Media Posts