COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 Prediction

• URL: http://arxiv.org/abs/2105.00620v1
• Date: Mon, 3 May 2021 04:00:59 GMT
• Title: COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 Prediction
• Authors: Siawpeng Er, Shihao Yang, Tuo Zhao
• Abstract summary: This paper proposes a method named COURAGE to generate a short-term prediction of 2-week-ahead COVID-19 related deaths for each county in the United States. 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.
• Score: 29.919578191688274
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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