2020 •
COVID-19 Spreading Prediction with Enhanced SEIR Model
Authors:
Yixiao Ma, Zixuan Xu, Ziwei Wu, Yong Bai
Abstract:
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 β and γ 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 (...)
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 β and γ 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. (Read More)
2020 International Conference on Artificial Intelligence and Computer Engineering (ICAICE) ·
2020
Statistics |
Econometrics |
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