# COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 Prediction

arXiv: https://arxiv.org/abs/2105.00620 · HTML: https://mlsanity.com/paper/2105.00620/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 63 references that are in the map.
- First posted on arXiv 3 May 2021.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Self-Attention with Relative Position Representations](https://mlsanity.com/paper/1803.02155/) (2018)
- [Transformer Hawkes Process](https://mlsanity.com/paper/2002.09291/) (2020)
- [Augmenting Data with Mixup for Sentence Classification: An Empirical Study](https://mlsanity.com/paper/1905.08941/) (2019)
- [Interpretable Sequence Learning for COVID-19 Forecasting](https://mlsanity.com/paper/2008.00646/) (2020)
- [Inter-Series Attention Model for COVID-19 Forecasting](https://mlsanity.com/paper/2010.13006/) (2020)
- [Google COVID-19 Community Mobility Reports: Anonymization Process Description (version 1.1)](https://mlsanity.com/paper/2004.04145/) (2020)
- [DeepGLEAM: A hybrid mechanistic and deep learning model for COVID-19 forecasting](https://mlsanity.com/paper/2102.06684/) (2021)
- [Curating a COVID-19 data repository and forecasting county-level death counts in the United States](https://mlsanity.com/paper/2005.07882/) (2020)
- [A Time-dependent SIR model for COVID-19 with Undetectable Infected Persons](https://mlsanity.com/paper/2003.00122/) (2020)
- [Tracking COVID-19 using online search](https://mlsanity.com/paper/2003.08086/) (2020)
- [The Limits to Learning a Diffusion Model](https://mlsanity.com/paper/2006.06373/) (2020)

## Built on by (most cited first, 1 of 1)

- [Leveraging Wastewater Monitoring for COVID-19 Forecasting in the US: a Deep Learning study](https://mlsanity.com/paper/2212.08798/) (2022)

## Abstract (arXiv)

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.