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

2022/05/20 by Felix Drinkall, Stefan Zohren, Drinkall, Felix +3
Social Sciences · Medicine · #Misinformation and Its Impacts #Data-Driven Disease Surveillance

paper · pdf · doi:10.48550/arxiv.2205.10408

Abstract

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.

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