A Deep Learning Approach for COVID-19 Trend Prediction

TL;DR

This paper presents a deep learning model using demographic and epidemic data to forecast COVID-19 trends in the US, achieving promising prediction accuracy and identifying key demographic factors.

Contribution

It introduces a novel deep learning approach combining demographic and time-series data for COVID-19 trend prediction in the US.

Findings

Accurate COVID-19 trend forecasts in the US.

Identification of key demographic factors influencing spread.

Effective use of GRU-based model for epidemic prediction.

Abstract

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.