防疫措施下SIR网络的新型冠状病毒传播预测

Prediction of COVID-19’s Transmission Based on SIR Network Model under Epidemic Prevention Measures

摘要: 文章考虑到人与人接触的复杂性以及不完全随机性，利用SIR网络在模拟数据集上对疫情趋势进行30天预测。同时画出感染者、易感者和康复者的数量随时间的变化趋势以及新型冠状病毒在整个人群接触关系网络中的传播情况。然后，分别结合四种常见的防疫措施，通过与无任何防疫措施的情况进行对比分析，得出防疫措施在阻止疫情在人群中扩散和恶化起到了关键作用。对比在不同的防疫措施下感染者、易感者、康复者和累计确诊病例的数量以及分析病毒在人群接触关系网络中的传播情况，得出不同防疫措施对疫情的影响不尽相同，其中对抑制病毒在人群中扩散最有效的是运用行程码的防疫措施。但总体来说，各种防疫措施均对控制疫情扩散起到积极作用。

Abstract: Considering the complexity and incomplete randomness of human contact, this paper uses the SIR network model to predict the epidemic trend for 30 days on the simulated data set. At the same time, the trend of the number of infections, susceptible people, and recovered people over time and the spread of COVID-19 in the whole population contact network are drawn. Then, combined with four common epidemic prevention measures, through the comparative analysis of the situation without any epidemic prevention measures, it is concluded that epidemic prevention measures have played a key role in preventing the spread and deterioration of the epidemic in the popula-tion. Comparing the number of infections, susceptible people, recovered people, and cumulative confirmed cases under different epidemic prevention measures, and analyzing the transmission of the virus in the population contact network, it is concluded that different epidemic prevention measures have different effects on the epidemic situation. Among them, the most effective epidemic prevention measure to inhibit the spread of the virus in the population is the epidemic prevention measure using a travel code. However, in general, various epidemic prevention measures have played a positive role in controlling the spread of the epidemic.