[论文解读] Progmosis: Evaluating Risky Individual Behavior During Epidemics Using Mobile Network Data

Antonio Lima, Veljko Pejović+3 more|arXiv (Cornell University)|Apr 6, 2015

Human Mobility and Location-Based Analysis参考文献 18被引用 5

一句话总结

Progmosis 提出了一种基于移动网络数据的隐私保护个体风险评估模型，用于流行病防控，通过用户移动行为和区域感染率量化其传播疾病的可能性。模拟结果显示，在埃博拉样疫情中，针对高风险个体实施移动限制措施，30天内可将二次感染减少24%。

ABSTRACT

The possibility to analyze, quantify and forecast epidemic outbreaks is fundamental when devising effective disease containment strategies. Policy makers are faced with the intricate task of drafting realistically implementable policies that strike a balance between risk management and cost. Two major techniques policy makers have at their disposal are: epidemic modeling and contact tracing. Models are used to forecast the evolution of the epidemic both globally and regionally, while contact tracing is used to reconstruct the chain of people who have been potentially infected, so that they can be tested, isolated and treated immediately. However, both techniques might provide limited information, especially during an already advanced crisis when the need for action is urgent. In this paper we propose an alternative approach that goes beyond epidemic modeling and contact tracing, and leverages behavioral data generated by mobile carrier networks to evaluate contagion risk on a per-user basis. The individual risk represents the loss incurred by not isolating or treating a specific person, both in terms of how likely it is for this person to spread the disease as well as how many secondary infections it will cause. To this aim, we develop a model, named Progmosis, which quantifies this risk based on movement and regional aggregated statistics about infection rates. We develop and release an open-source tool that calculates this risk based on cellular network events. We simulate a realistic epidemic scenarios, based on an Ebola virus outbreak; we find that gradually restricting the mobility of a subset of individuals reduces the number of infected people after 30 days by 24%.

研究动机与目标

• 开发一种利用 readily available 移动网络数据评估流行病期间个体传染风险的方法。
• 通过识别最有可能传播疾病的人群，实现有针对性的公共卫生干预，而无需知晓感染者身份。
• 通过基于移动行为的干预优先级排序（如检测、隔离或健康信息传播）减少流行病传播。
• 通过促进去中心化部署（即移动数据永不离开用户设备）确保用户隐私。
• 评估基于真实世界移动轨迹推导出的风险评分的移动限制策略的有效性。

提出的方法

• 该模型使用蜂窝网络通话详单记录（CDRs）推断个体移动模式和区域感染率。
• 基于传播感染的可能性和预期二次感染人数，计算每个用户的个体风险评分。
• 风险模型结合移动数据与聚合的区域感染统计数据，估算个体传播潜力。
• 实现了一个开源工具，以去中心化方式从 CDR 事件计算风险评分。
• 使用来自一个当前无埃博拉病毒国家的真实移动轨迹数据进行模拟，模拟埃博拉样疫情并控制移动限制。
• 评估选择性限制高风险个体与随机选择相比的影响。

实验结果

研究问题

• RQ1 能否在不识别感染者身份的前提下，利用移动网络数据估算流行病期间个体的疾病传播风险？
• RQ2 基于风险评分的有针对性的移动限制在减少二次感染方面的有效性如何？
• RQ3 去中心化部署对隐私保护和此类系统实际可行性的影晌如何？
• RQ4 公众对疫情认知引发的行为改变如何影响基于移动行为的风险评估的可靠性？
• RQ5 个体风险异质性在多大程度上影响有针对性防控策略的整体有效性？

主要发现

• 通过 Progmosis 选择的高风险个体子集实施移动限制，30天后感染人数相比随机选择减少了24%。
• 该模型即使在不知晓感染者身份的情况下，也能成功基于移动模式和区域感染率识别出高传播潜力个体。
• 模拟使用了当前无埃博拉病毒国家的真实 CDR 数据，证明了该模型在真实世界移动模式下的适用性。
• 该方法在不确定性下依然有效，因其无需事先知晓感染者身份。
• 去中心化部署在可行性和隐私保护方面优于集中式系统，同时保持了风险评估的准确性。
• 该模型凸显了个体传播异质性在疾病传播中的重要性，支持有针对性干预而非全面措施。