Cohorting to isolate asymptomatic spreaders: An agent-based simulation study on the Mumbai Suburban Railway
Cohorting to isolate asymptomatic spreaders: An agent-based simulation study on the Mumbai Suburban Railway
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DOI：
10.5555/3463952.3464199
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发表时间：
2020-12
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通讯作者：
A. Talekar;S. Shriram;N. Vaidhiyan;G. Aggarwal;Jiangzhuo Chen;S. Venkatramanan;Lijing Wang;A. Adiga;A. Sadilek;A. Tendulkar;M. Marathe;R. Sundaresan;M. Tambe
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文献类型：
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作者：
A. Talekar;S. Shriram;N. Vaidhiyan;G. Aggarwal;Jiangzhuo Chen;S. Venkatramanan;Lijing Wang;A. Adiga;A. Sadilek;A. Tendulkar;M. Marathe;R. Sundaresan;M. Tambe
当地人说，孟买郊区铁路是孟买的关键交通基础设施，对恢复正常的经济活动至关重要。由于运输过程中密度较高，疾病传播的潜在风险很高，政府已采取观望方式恢复正常运营。为了减少疾病传播，政策制定者可以强制减少拥挤，并强制戴口罩。结成旅行团，总是一起旅行，这是一项额外的政策，可以在没有严格限制的情况下减少疾病在当地人的传播。队列管理使我们能够：(I)形成旅行者气泡，从而随着时间的推移减少不同互动的数量；(Ii)如果检测到单个病例，可能隔离整个队列，从而提高接触者追踪的效率；以及(Iii)针对队列进行检测和早期发现有症状和无症状的病例。由于随之而来的表征复杂性，使用区隔模型研究队列的影响是具有挑战性的。基于代理的模型提供了一种自然的方式来表示队列以及具有更大社会网络的队列成员的表示。本文描述了一个新的基于多尺度智能体的模型来研究队列策略对孟买新冠肺炎动态的影响。我们通过使用一个由1240万个代理组成的基于代理的详细模型对孟买市区进行建模来实现这一点。使用本地平均场近似来模拟个体队列及其队列间的相互作用，因为他们在本地行驶。由此产生的多尺度模型与详细的疾病传播和干预模拟器一起用于评估各种队列策略。这些结果提供了队列大小与其对疾病动态和福祉的影响之间的定量权衡。结果表明，队列可以在不显著影响乘客和/或经济和社会活动的情况下，在减少传播方面提供显著的好处。
The Mumbai Suburban Railways, locals, 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. Cohorting - forming groups of travelers that always travel together, is an additional policy to reduce disease transmission on locals without severe restrictions. Cohorting allows us to: (i) form traveler bubbles, thereby decreasing the number of distinct interactions over time; (ii) potentially quarantine an entire cohort if a single case is detected, making contact tracing more efficient, and (iii) 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.