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The Mumbai Suburban Railways, \emph{locals}, are a key transit infrastructureof the city and is crucial for resuming normal economic activity. To reducedisease transmission, policymakers can enforce reduced crowding and mandatewearing of masks. \emph{Cohorting} -- forming groups of travelers that alwaystravel together, is an additional policy to reduce disease transmission on\textit{locals} without severe restrictions. Cohorting allows us to: ($i$) formtraveler bubbles, thereby decreasing the number of distinct interactions overtime; ($ii$) potentially quarantine an entire cohort if a single case isdetected, making contact tracing more efficient, and ($iii$) target cohorts fortesting and early detection of symptomatic as well as asymptomatic cases.Studying impact of cohorts using compartmental models is challenging because ofthe ensuing representational complexity. Agent-based models provide a naturalway to represent cohorts along with the representation of the cohort memberswith the larger social network. This paper describes a novel multi-scaleagent-based model to study the impact of cohorting strategies on COVID-19dynamics in Mumbai. We achieve this by modeling the Mumbai urban region using adetailed agent-based model comprising of 12.4 million agents. Individualcohorts and their inter-cohort interactions as they travel on locals aremodeled using local mean field approximations. The resulting multi-scale modelin conjunction with a detailed disease transmission and intervention simulatoris used to assess various cohorting strategies. The results provide aquantitative trade-off between cohort size and its impact on disease dynamicsand well being. The results show that cohorts can provide significant benefitin terms of reduced transmission without significantly impacting ridership andor economic \& social activity.

Cohorting to isolate asymptomatic spreaders: An agent-based simulation study on the Mumbai Suburban Railway