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


We present a novel approach incorporating transformer-based language models into infectious disease modelling. Text-derived features are quantified by tracking high-density clusters of sentence-level representations of Reddit posts within specific US states' COVID-19 subreddits. We benchmark these clustered embedding features against features extracted from other high-quality datasets. In a threshold-classification task, we show that they outperform all other feature types at predicting upward trend signals, a significant result for infectious disease modelling in areas where epidemiological data is unreliable. Subsequently, in a time-series forecasting task we fully utilise the predictive power of the caseload and compare the relative strengths of using different supplementary datasets as covariate feature sets in a transformer-based time-series model.

Jul 10, 2022 12:00 AM — Jul 15, 2022 12:00 AM
Hyatt Regency Seattle
808 Howell Street, Seattle, Washington 98101
Felix Drinkall
Felix Drinkall
PhD Candidate and British Rowing Athlete

My main research interest is the intersection between Natural Language Processing and Time Series Forecasting.