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.
@article{arxiv.2008.05644,
title = {A Deep Learning Approach for COVID-19 Trend Prediction},
author = {Tong Yang and Long Sha and Justin Li and Pengyu Hong},
journal= {arXiv preprint arXiv:2008.05644},
year = {2020}
}
Comments
7 pages, 11 figures, accepted by KDD 2020 epiDAMIK workshop