Abstract
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.
Highlights
COVID-19 has been spreading globally and affected almost every country since 2020
For county-level predictions, we compare the predictions from our COURAGE model with its two member models—the County model and the Mixup model
We present the performance of each model across multiple non-overlapping periods
Summary
COVID-19 has been spreading globally and affected almost every country since 2020. In the United States (US), the COVID-19 pandemic started spreading in January 2020, and in March, the daily number of confirmed cases and number of deaths rose to an alarming stage[1]. Based on the publicly available data, researchers have built predictive models to study the disease dynamics. Two key measurements used by various research groups in the study of the spread of COVID-19 are the number of confirmed cases and the number of deaths. Both measurements serve to measure the disease dynamics of COVID-19. Some datasets[7] only present confirmed cases as the number of people having positive results for a completed polymerase chain reaction test Such coarsegrained testing numbers may undercount the true spread of the COVID-19, while introducing a significant bias to predictive models. Such a model could help both the state and local governments to make an informed decision based on the predictions at the corresponding state and county levels
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