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基于社交网络信息的新冠疫情抽检策略研究

  • 左子健 ,
  • 张琳 ,
  • 吴晔 ,
  • 许小可 ,
  • 左子健 ,
  • 张琳 ,
  • 吴晔 ,
  • 许小可
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  • 1.大连民族大学信息与通信工程学院,辽宁 大连 116600;
    2. 北京邮电大学理学院, 北京 100876;
    3.北京师范大学 a.计算传播学研究中心, 广东 珠海 519085; b. 新闻传播学院, 北京 100875
左子健(1995),男,河北邢台人,硕士研究生,主要研究方向为社交网络上疾病传播学。

收稿日期: 2022-04-23

  修回日期: 2022-05-22

  网络出版日期: 2023-10-08

基金资助

国家自然科学基金(62173065); 辽宁省自然科学基金(2020-MZLH-22);北京市社会科学基金重点项目(21DTR40)

Sampling Strategy of COVID-19 Based on Social Network Information

  • ZUO Zijian ,
  • ZHANG Lin ,
  • WU Ye ,
  • XU Xiaoke ,
  • ZUO Zijian ,
  • ZHANG Lin ,
  • WU Ye ,
  • XU Xiaoke
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  • 1. College of Information and Communication Engineering, Dalian Minzu University, Dalian 116600, China;
    2. School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China;
    3. a.Computational Communication Research Center, Beijing Normal University, Zhuhai 519087, China; b.School of Journalism and Communication, Beijing Normal University, Beijing 100875, China

Received date: 2022-04-23

  Revised date: 2022-05-22

  Online published: 2023-10-08

摘要

为解决全员定期核酸检测成本高,导致间隔周期长且效率低的问题,给出了基于社交网络信息的抽检策略,将在线社交网络信息与新冠肺炎传播规律融合,建立了带抽检策略的新冠肺炎传播模型。在此基础上,研究了随机抽检策略、熟人监测策略、度大节点目标抽检监测策略三种策略对新冠肺炎传播的影响。研究发现在校园等人群密集接触的环境下,熟人和度大节点目标抽检策略在峰值幅度、峰值时间、早期预警等指标下都好于随机监测策略,减少了新冠病毒传播天数以及感染人数,能更早、更快地控制住疫情,预警效果从高到低依次是度大节点目标监测策略、熟人监测策略、随机监测策略。

本文引用格式

左子健 , 张琳 , 吴晔 , 许小可 , 左子健 , 张琳 , 吴晔 , 许小可 . 基于社交网络信息的新冠疫情抽检策略研究[J]. 复杂系统与复杂性科学, 2023 , 20(3) : 20 -26 . DOI: 10.13306/j.1672-3813.2023.03.003

Abstract

The cost of regular nucleic acid testing for all staff is high, resulting in long intervals and low efficiency. This paper presents a sampling strategy based on social network information, integrates online social network information with the spread of COVID-19, and establishes a novel coronavirus spread model with a sampling strategy. On this basis, this paper studies the impact of three strategies on the spread of new coronary pneumonia, namely random sampling strategy, acquaintance monitoring strategy, and large node target sampling monitoring strategy. The study found that the random monitoring strategy is better than the random monitoring strategy in the three indicators of peak amplitude, peak time, and early warning in the specific situation of the campus environment, reducing the number of days of new coronavirus transmission and the number of infected people, and more. To control the epidemic earlier and faster, the early warning effect is from high to low: target monitoring strategy for large nodes, acquaintance monitoring strategy, and random monitoring strategy.

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