COVINet: a deep learning-based and interpretable prediction model for the county-wise trajectories of COVID-19 in the United States.

IF 1.2 4区 数学 Q2 STATISTICS & PROBABILITY
Journal of Applied Statistics Pub Date : 2024-10-08 eCollection Date: 2025-01-01 DOI:10.1080/02664763.2024.2412284
Yukang Jiang, Ting Tian, Wenting Zhou, Yuting Zhang, Zhongfei Li, Xueqin Wang, Heping Zhang
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引用次数: 0

Abstract

The devastating impact of COVID-19 on the United States has been profound since its onset in January 2020. Predicting the trajectory of epidemics accurately and devising strategies to curb their progression are currently formidable challenges. In response to this crisis, we propose COVINet, which combines the architecture of Long Short-Term Memory and Gated Recurrent Unit, incorporating actionable covariates to offer high-accuracy prediction and explainable response. First, we train COVINet models for confirmed cases and total deaths with five input features, and compare Mean Absolute Errors (MAEs) and Mean Relative Errors (MREs) of COVINet against ten competing models from the United States CDC in the last four weeks before April 26, 2021. The results show COVINet outperforms all competing models for MAEs and MREs when predicting total deaths. Then, we focus on prediction for the most severe county in each of the top 10 hot-spot states using COVINet. The MREs are small for all predictions made in the last 7 or 30 days before March 23, 2023. Beyond predictive accuracy, COVINet offers high interpretability, enhancing the understanding of pandemic dynamics. This dual capability positions COVINet as a powerful tool for informing effective strategies in pandemic prevention and governmental decision-making.

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来源期刊
Journal of Applied Statistics
Journal of Applied Statistics 数学-统计学与概率论
CiteScore
3.40
自引率
0.00%
发文量
126
审稿时长
6 months
期刊介绍: Journal of Applied Statistics provides a forum for communication between both applied statisticians and users of applied statistical techniques across a wide range of disciplines. These areas include business, computing, economics, ecology, education, management, medicine, operational research and sociology, but papers from other areas are also considered. The editorial policy is to publish rigorous but clear and accessible papers on applied techniques. Purely theoretical papers are avoided but those on theoretical developments which clearly demonstrate significant applied potential are welcomed. Each paper is submitted to at least two independent referees.
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