Deep COVID-19 Forecasting for Multiple States with Data Augmentation

TL;DR

This paper introduces a deep learning model using transformers, ensemble methods, and innovative data augmentation to improve state-level COVID-19 trend forecasting in the US and Germany, achieving top results.

Contribution

It presents a novel data augmentation technique and a transformer-based ensemble model for COVID-19 forecasting with enhanced training and validation capabilities.

Findings

Achieved top performance on COVID-19 Forecast Hub for US and Germany.

Developed a data augmentation method that improves training data quality.

Demonstrated the effectiveness of transformer models in pandemic trend prediction.

Abstract

In this work, we propose a deep learning approach to forecasting state-level COVID-19 trends of weekly cumulative death in the United States (US) and incident cases in Germany. This approach includes a transformer model, an ensemble method, and a data augmentation technique for time series. We arrange the inputs of the transformer in such a way that predictions for different states can attend to the trends of the others. To overcome the issue of scarcity of training data for this COVID-19 pandemic, we have developed a novel data augmentation technique to generate useful data for training. More importantly, the generated data can also be used for model validation. As such, it has a two-fold advantage: 1) more actual observations can be used for training, and 2) the model can be validated on data which has distribution closer to the expected situation. Our model has achieved some of the best state-level results on the COVID-19 Forecast Hub for the US and for Germany.