[论文解读] Cohorting to isolate asymptomatic spreaders: An agent-based simulation study on the Mumbai Suburban Railway

Alok Talekar, Sharad Shriram+11 more|PubMed|Dec 23, 2020

COVID-19 epidemiological studies参考文献 18被引用 5

一句话总结

该基于代理的模拟研究评估了分组策略——将孟买本地列车通勤者划分为固定出行群体——以减少无症状SARS-CoV-2传播。通过限制组间互动并实现在检测到感染后迅速隔离整个群体，分组策略显著降低了疾病传播，同时对乘客出行影响较小，最优效果出现在每组12至20名乘客时。

ABSTRACT

The Mumbai Suburban Railways, <i>locals</i>, are a key transit infrastructure of the city and is crucial for resuming normal economic activity. Due to high density during transit, the potential risk of disease transmission is high, and the government has taken a wait and see approach to resume normal operations. To reduce disease transmission, policymakers can enforce reduced crowding and mandate wearing of masks. <i>Cohorting</i> - forming groups of travelers that always travel together, is an additional policy to reduce disease transmission on <i>locals</i> without severe restrictions. Cohorting allows us to: (<i>i</i>) form traveler bubbles, thereby decreasing the number of distinct interactions over time; (<i>ii</i>) potentially quarantine an entire cohort if a single case is detected, making contact tracing more efficient, and (<i>iii</i>) target cohorts for testing and early detection of symptomatic as well as asymptomatic cases. Studying impact of cohorts using compartmental models is challenging because of the ensuing representational complexity. Agent-based models provide a natural way to represent cohorts along with the representation of the cohort members with the larger social network. This paper describes a novel multi-scale agent-based model to study the impact of cohorting strategies on COVID-19 dynamics in Mumbai. We achieve this by modeling the Mumbai urban region using a detailed agent-based model comprising of 12.4 million agents. Individual cohorts and their inter-cohort interactions as they travel on locals are modeled using local mean field approximations. The resulting multi-scale model in conjunction with a detailed disease transmission and intervention simulator is used to assess various cohorting strategies. The results provide a quantitative trade-off between cohort size and its impact on disease dynamics and well being. The results show that cohorts can provide significant benefit in terms of reduced transmission without significantly impacting ridership and or economic & social activity.

研究动机与目标

• 评估分组作为公共卫生策略在减少高密度城市铁路系统（如孟买郊区铁路）中无症状传播的效果。
• 量化在分组策略下，疾病传播减少与对乘客出行及经济活动影响之间的权衡。
• 评估在不同一次性出行比例和不同分组规模政策下的分组策略有效性。
• 探究在分组策略中，固定车厢分配是否优于动态分配以改善疾病传播结果。

提出的方法

• 开发了多尺度基于代理的模型，模拟孟买城市网络中1240万名个体，包括详细的列车通勤动态。
• 使用本地平均场近似方法对组间互动及通勤期间的社会网络结构进行建模。
• 整合了具有干预措施（如佩戴口罩、体温筛查和隔离协议）的分层疾病传播模型。
• 模拟了不同分组规模（1至20人）和一次性出行者比例（0%至40%）的多种分组策略。
• 使用开源模拟代码对不同政策下的列车拥挤、车站拥堵及接触追踪效率进行建模。
• 通过每日和累计确诊病例数、被隔离人数以及峰值传播负荷等指标评估结果。

实验结果

研究问题

• RQ1 分组规模如何影响孟买郊区铁路系统中每日和累计的SARS-CoV-2感染病例数？
• RQ2 在部分实施分组策略时，一次性出行者对疾病传播有何影响？
• RQ3 与动态分配相比，固定车厢分配是否能更有效地改善疾病传播结果？
• RQ4 分组策略如何影响因接触追踪而被隔离的个体数量，以及全市范围总隔离人数的边际增加程度如何？
• RQ5 在维持经济和社会流动性的同时，分组策略在多大程度上可减少疾病传播？

主要发现

• 较大的分组规模（如12至20人）显著降低了峰值每日和累计病例数，且在分组规模超过10人时观察到最显著的传播减少效果。
• 即使一次性出行者占40%，采用12至20人的分组规模仍能显著降低疾病传播，相较于常规情况（分组规模=1），显示出对部分实施的强适应性。
• 因分组策略而被隔离的个体数量随分组规模增加而上升，但全市范围总隔离人数的边际增加较小，表明隔离效率较高。
• 与动态分配相比，固定车厢分配在减少疾病传播方面无显著优势，表明在实际操作中具有较高的灵活性。
• 分组策略使在单名成员检测阳性时能够实现对整个群体的早期发现与隔离，从而提升接触追踪效率并减少二次传播。
• 疾病传播对列车拥挤程度最为敏感，凸显了在列车和车站层面实施有效人群管理的必要性。