The global spread of COVID-19, the disease caused by the novel coronavirus SARS-CoV-2, has casted 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 solely utilizes publicly available information for COVID-19 related confirmed cases, deaths, community mobility trends and demographic information, and can produce state-level predictions 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.
【저자키워드】 Infectious diseases, Computer science, Statistics, 【초록키워드】 COVID-19, deep learning, disease severity, death, Community, experiment, language, information, resource, deaths, Efficiency, natural, confirmed cases, novel coronavirus SARS-CoV-2, processing, aggregation, COVID-19 situation, The United States, transformer, spread of COVID-19, caused, the disease, generate, 【제목키워드】 COVID-19,