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.

翻译：COVID-19全球传播是新型冠状病毒SARS-CoV-2造成的疾病,它对人类构成重大威胁。随着COVID-19的形势继续演变,预测局部疾病的严重程度对于先进的资源分配至关重要。本文件提出一种名为COURAGE(COUty aggRegation mixation AuGmEnitation)的方法,以利用现代深层次学习技术,对美国每个州两周头COVID-19相关死亡进行短期预测。具体地说,我们的方法采用了一种自然语言处理(称为变异器模型)的自我注意模式,在时间序列内捕捉短期和长期依赖性,同时享有计算效率。我们的模型充分利用了COVID-19相关案例、死亡、社区流动趋势和人口信息等公开可得的信息,并能够产生州一级预测,作为相应的县级预测的汇总。我们的数字实验表明,我们的模型在公开的基准模型中取得了最新业绩。