County Augmented Transformer For Covid-19 State Hospitalizations Prediction

This paper proposes a method named County Augmented Transformer (CAT) to generate accurate predictions of four-week-ahead COVID-19 related hospitalizations for every states in the United States. The method is based on a self-attention model (known as the transformer model) that is actively used in Natural Language Processing and can capture both short-term and long-term dependencies within the time series. The model utilizes the publicly available information including the COVID-19 related number of confirmed cases, deaths, hospitalizations data, and the household median income data. Numerical experiments demonstrate the strength and the usability of the model as a potential tool for assisting the medical resources allocation.