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

• URL: http://arxiv.org/abs/2205.10408v1
• Date: Fri, 20 May 2022 18:59:04 GMT
• Title: Forecasting COVID-19 Caseloads Using Unsupervised Embedding Clusters of Social Media Posts
• Authors: Felix Drinkall, Stefan Zohren and Janet B. Pierrehumbert
• Abstract summary: We present a novel approach incorporating transformer-based language models into infectious disease modelling. 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.
• Score: 14.201816626446885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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