Talekar Alok, Shriram Sharad, Vaidhiyan Nidhin, Aggarwal Gaurav, Chen Jiangzhuo, Venkatramanan Srini, Wang Lijing, Adiga Aniruddha, Sadilek Adam, Tendulkar Ashish, Marathe Madhav, Sundaresan Rajesh, Tambe Milind
Google Inc.
Indian Institute of Science, Bangalore.
ArXiv. 2020 Dec 23:arXiv:2012.12839v2.
The Mumbai Suburban Railways, , 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. - forming groups of travelers that always travel together, is an additional policy to reduce disease transmission on without severe restrictions. Cohorting allows us to: () form traveler bubbles, thereby decreasing the number of distinct interactions over time; () potentially quarantine an entire cohort if a single case is detected, making contact tracing more efficient, and () 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.
孟买市郊铁路是该市重要的交通基础设施，对于恢复正常经济活动至关重要。由于通勤期间人口密度高，疾病传播的潜在风险很大，政府采取了观望态度以恢复正常运营。为了减少疾病传播，政策制定者可以强制减少拥挤并要求佩戴口罩。——将旅客组成始终一起出行的群体，是在没有严格限制的情况下减少市郊铁路疾病传播的一项额外政策。群体划分使我们能够：（）形成旅客气泡，从而随着时间的推移减少不同互动的数量；（）如果检测到单个病例，有可能对整个群体进行隔离，使接触者追踪更有效，以及（）针对群体进行检测并早期发现有症状和无症状病例。由于随之而来的表示复杂性，使用 compartmental 模型研究群体的影响具有挑战性。基于代理的模型提供了一种自然的方式来表示群体以及在更大的社会网络中表示群体成员。本文描述了一种新颖的多尺度基于代理的模型，用于研究群体划分策略对孟买 COVID-19 动态的影响。我们通过使用由 1240 万个代理组成的详细基于代理的模型对孟买市区进行建模来实现这一点。个体群体及其在乘坐市郊铁路时的群体间互动使用局部平均场近似进行建模。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群体划分策略。结果提供了群体规模与其对疾病动态和福祉影响之间的定量权衡。结果表明，群体划分在减少传播方面可以带来显著益处，而不会对乘客量以及经济和社会活动产生重大影响。
