东北大学学报（自然科学版）

东北大学学报（自然科学版） ›› 2022, Vol. 43 ›› Issue (8): 1065-1073.DOI: 10.12068/j.issn.1005-3026.2022.08.001

• 信息与控制 •    下一篇

考虑舆情的分时段SIQR模型的新冠疫情分析与预测

鲍玉斌1， 刘济霆1， 李效宇2   

  1. (1. 东北大学 计算机科学与工程学院， 辽宁 沈阳110169； 2. 东北大学 机器人科学与工程学院， 辽宁 沈阳110169)
  • 修回日期:2021-07-29 接受日期:2021-07-29 发布日期:2022-08-11
  • 通讯作者: 鲍玉斌
  • 作者简介:鲍玉斌(1968-)，男， 吉林集安人，东北大学教授.
  • 基金资助:
    国家自然科学基金资助项目(62072084).

Time-Divided SIQR Model for COVID-19 Analysis and Prediction in Consideration of Public Opinion

BAO Yu-bin1， LIU Ji-ting1， LI Xiao-yu2   

  1. 1. School of Computer Science & Engineering， Northeastern University， Shenyang 110169， China； 2. School of Robot Science & Engineering， Northeastern University， Shenyang 110169， China.
  • Revised:2021-07-29 Accepted:2021-07-29 Published:2022-08-11
  • Contact: BAO Yu-bin
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摘要： 为了科学地研判本土新冠疫情复发时的发展趋势，有效控制和防止疫情蔓延，本文将疫情传播分为三个时段，并综合考虑舆情与防控措施对各时段的干预效果，建立了一种分时段的具有非线性参数的新型冠状病毒肺炎传染模型.在真实数据集上的仿真结果表明: 对感染者排查力度的快速增强会显著干预疫情扩散，可使受感染人数增速减缓的拐点提前到来.相比其他模型，所提模型对大连2020年12月疫情的预测结果不仅均方误差小，而且曲线的变化趋势更贴近现实情况.此外，本文对具有长自由传播期和强聚集性的疫情进行了仿真，预测误差与同类模型相比较小.这表明本模型具有一定的通用性与健壮性.

关键词: 新型冠状病毒肺炎；传染病模型；舆情因素；仿真模拟；预测

Abstract: In order to scientifically identify and judge the trend of local COVID-19 recurrence， and to control and prevent the spread of its epidemic， a novel time-divided SIQR coronavirus pneumonia infection model with nonlinear parameters is established. It divides the epidemic spread into three periods， and comprehensively considers the intervention effect of public opinion and prevention measures on each period. The simulation results on several real datasets show that the rapid enhancement of the screening of infected and uncontrolled people will significantly interfere with the spread of the epidemic and make the inflection point of slowing down the number of infections come early. The prediction results of the epidemic situation at Dalian in December 2020 by the proposed model not only have smaller mean square error， but also the change trend of the predication curve is closer to the reality compared with other baseline models. Additionally， the epidemic situation with longer free transmission period and stronger aggregation is simulated by the proposed model. The prediction error is also smaller than that of the compared models on the real data. This shows that the model has certain universality and robustness.

Key words: COVID-19; epidemic model; public opinion factors; simulation; prediction

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