Sampling Strategy of COVID-19 Based on Social Network Information
ZUO Zijian1, ZHANG Lin2, WU Ye3, XU Xiaoke3
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
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.
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.
ZUO Zijian, ZHANG Lin, WU Ye, XU Xiaoke. Sampling Strategy of COVID-19 Based on Social Network Information. Complex Systems and Complexity Science, 2023, 20(3): 20-26.
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