新冠肺炎疫情趋势预测模型

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Title:

Epidemics trend prediction model of COVID-19

作者:

甘雨, 吴雨, 王建勇

四川大学 计算机学院，四川 成都 610065

Author(s):

GAN Yu, WU Yu, WANG Jianyong

College of Computer Science, Sichuan University, Chengdu 610065, China

关键词:

新型冠状病毒; SEIR模型; 长短期记忆网络; 智能系统; 预测模型; 实时预测; 神经网络; 深度学习

Keywords:

COVID-19; SEIR model; LSTM; intelligent systems; prediction model; real time prediction; neural networks; deep learning

分类号:

TP391.4

DOI:

10.11992/tis.202008037

摘要:

2019年新型冠状病毒肺炎(corona virus disease 2019，COVID-19)的爆发对人们的健康和生活造成了极大的危害和影响。预测疫情的发展趋势可帮助人们提前制定应对措施。SEIR模型是经典的传染病模型之一，由于该模型中病毒传染率为常数，难以对新冠肺炎传播情况进行准确建模并完成疫情趋势预测。针对此问题，本文提出基于长短期记忆网络(long short-term memory，LSTM)的病毒传染率预测方法，并将其与SEIR模型结合，建立新冠肺炎疫情趋势预测模型(LSTM-SEIR network, LS-Net)。为了验证本文提出的方法，收集了国内多个省市官方公布的疫情数据进行实验。实验结果表明，本文提出的LS-Net可对疫情发展趋势进行有效预测，并优于传统SEIR模型。

Abstract:

The outbreak of coronavirus disease 2019 (COVID-19) has threatened and brought a serious impact on the health and daily life of people. If people are warned beforehand about the speed of the disease, they are able to take necessary preventive measures. As one of the most classical epidemic models, the SEIR model can hardly model the spread of COVID-19 and predict its trend because the rate of transmission is constant, which is one of the required parameters of the SEIR model. Aiming at this problem, a dynamic prediction method of the rate of transmission is derived based on long short-term memory (LSTM). An LSTM-SEIR network (LS-Net) is then proposed based on the LSTM and SEIR models to predict the trend of the COVID-19 epidemic. To validate the LS-Net, official epidemiological data released from different domestic areas are collected. The experimental results show that LS-Net can predict the spread of COVID-19 validly and with better performance compared with that of the traditional SEIR model.