在大学校园使用WiFi基础设施进行本地化COVID-19干预的实证网络
Vedant Das Swain1, Jiajia Xie1,2, Maanit Madan1
1College of Computing, Georgia Institute of Technology, Atlanta, GA, United States.
Frontiers in digital health
|June 1, 2023
概括
大学WiFi数据可以创建移动模型,以告知本地关闭政策. 这些局部关闭比远程指导更有效地减少传染病传播和峰值感染,负面影响更少.
科学领域:
- 流行病学 流行病学
- 网络科学 网络科学
- 公共卫生政策 公共卫生政策
背景情况:
- 像COVID-19这样的传染病对大学校园构成重大挑战.
- 全校校园关闭和远程教学 (RI) 是常见的干预措施,但有相当大的负面副作用.
研究的目的:
- 引入WiFi流动性模型 (WiMob) 以了解校园社区的流动性.
- 使决策者能够探索和设计更细致的干预策略,例如局部关闭 (LC).
主要方法:
- 利用匿名的WiFi数据来构建社区联系网络.
- 开发WiMob以分析各种校园空间的行为,并确定传播途径.
- 使用WiMob生成的联系网络模拟疾病传播,以将LC与RI进行比较.
主要成果:
- 局部关闭 (LC) 实现了与远程指令 (RI) 相似的累积感染减少.
- 与RI相比,LC在峰值感染和内部传播方面表现出优异的降低.
- 通过关闭更少的地点和减少广泛的远程学习的需求,LC将校园中断最小化.
结论:
- 无线网络移动模型 (WiMob) 为大学提供了一种数据驱动的方法来管理传染病爆发.
- 局部关闭 (LC) 是一种更有效,更少破坏性的替代方案,而不是广泛的远程指导政策.
- WiMob使大学能够设计和评估针对未来疫情预防的有针对性的干预措施.
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