基于网络的主动联系人追踪:为保护隐私的COVID-19应用程序提供先发制人的,基于程度的警报框架
Diaoulé Diallo1, Tobias Hecking1
1German Aerospace Center, Institute of Software Technology, Sankt Augustin, Germany.
基于网络的主动联系追踪 (NPCT) 为COVID-19应用程序提供了对二进制联系追踪 (BCT) 的隐私保护升级. 与目前的方法相比,NPCT可以减少40%的流行病峰值,并减少社会干扰.
科学领域:
- 流行病学 流行病学
- 网络科学 网络科学
- 公共卫生技术 公共卫生技术
背景情况:
- 目前的COVID-19暴露通知应用程序使用二进制接触追踪 (BCT),根据固定的风险值发布统一的隔离命令.
- BCT的局限性包括延迟的警报和广泛的隔离,可能会错过早期传播,同时造成不必要的干扰.
研究的目的:
- 引入基于网络的主动联系追踪 (NPCT),这是一个新的,保护隐私和分散的干预计划.
- 与传统BCT相比,评估NPCT在减轻流行病方面的有效性.
主要方法:
- NPCT利用用户的蓝牙联系历史生成个别风险评分.
- 一个动态的,疫情意识的门调整干预水平,触发分级的"减少X%的接触"提示,而不是所有或没有的隔离.
- 在四个合成和实证时间网络上进行了模拟.
主要成果:
- NPCT证明了减少大约40%的流行病峰值的能力.
- 干预只抑制了20%的联系,这表明了更有针对性的方法.
- 干预负担集中在高风险个人身上,在不同类型的网络和合规水平上表现稳定.
结论:
- 对于现有的BCT应用,NPCT提供了一个实用且有效的升级.
- 该计划平衡了疫情控制,加强了隐私保护和降低了社会成本.
- 通过数字联系人追踪,NPCT提供了一种更细微和更有效的方法来管理传染病爆发.
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