COVID-19 Spreading Prediction with Enhanced SEIR Model

被引：1

作者：

Ma, Yixiao ^{[1
]}

Xu, Zixuan ^{[1
]}

Wu, Ziwei ^{[1
]}

Bai, Yong ^{[1
]}

机构：

[1] Hainan Univ, Sch Informat & Commun Engn, Haikou 570228, Hainan, Peoples R China

来源：

2020 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND COMPUTER ENGINEERING (ICAICE 2020) | 2020年

基金：

中国国家自然科学基金;

关键词：

COVID-19; SIR; SEIR; epidemiological model;

D O I：

10.1109/ICAICE51518.2020.00080

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The COVID-19 epidemic broke out at the end of 2019 and developed into a global infectious disease in early 2020. In order to understand the spreading trend of the epidemic, we propose an enhanced epidemiology predictive model-eSEIR model by improving the well-mixed SEIR model on the infectious disease dynamics. The eSEIR model incorporates an optimization method to calculate beta and gamma parameters. Our proposed model is verified using the epidemic data in Italy and China with reduced RMSE (root mean square error) of the predicted curves, and is used to predict the potential epidemic progress in the United States.

引用

页码：383 / 386

页数：4

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