A Deep Learning Approach for COVID-19 Trend Prediction
This work addresses the need for accurate epidemic trend predictions for public health planning, but it appears incremental as it applies existing methods to a specific new dataset.
The researchers tackled the problem of forecasting COVID-19 spread trends in the United States by developing a deep learning model that incorporates demographic and time-series data, achieving promising prediction results.
In this work, we developed a deep learning model-based approach to forecast the spreading trend of SARS-CoV-2 in the United States. We implemented the designed model using the United States to confirm cases and state demographic data and achieved promising trend prediction results. The model incorporates demographic information and epidemic time-series data through a Gated Recurrent Unit structure. The identification of dominating demographic factors is delivered in the end.