  The global spread of COVID-19, the disease caused by the novel coronavirus
SARS-CoV-2, has cast a significant threat to mankind. As the COVID-19 situation
continues to evolve, predicting localized disease severity is crucial for
advanced resource allocation. This paper proposes a method named COURAGE
(COUnty aggRegation mixup AuGmEntation) to generate a short-term prediction of
2-week-ahead COVID-19 related deaths for each county in the United States,
leveraging modern deep learning techniques. Specifically, our method adopts a
self-attention model from Natural Language Processing, known as the transformer
model, to capture both short-term and long-term dependencies within the time
series while enjoying computational efficiency. Our model fully utilizes
publicly available information of COVID-19 related confirmed cases, deaths,
community mobility trends and demographic information, and can produce
state-level prediction as an aggregation of the corresponding county-level
predictions. Our numerical experiments demonstrate that our model achieves the
state-of-the-art performance among the publicly available benchmark models.
