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[Preprint]. 2020 Dec 23:arXiv:2012.12839v2.

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

Alok Talekar  1 , Sharad Shriram  2 , Nidhin Vaidhiyan  2 , Gaurav Aggarwal  1 , Jiangzhuo Chen  3 , Srini Venkatramanan  3 , Lijing Wang  3 , Aniruddha Adiga  3 , Adam Sadilek  1 , Ashish Tendulkar  1 , Madhav Marathe  3 , Rajesh Sundaresan  2   4 , Milind Tambe  1
Affiliations

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

Alok Talekar et al. ArXiv. .

Abstract

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.

Keywords: Cohorts; Covid-19; Public Transportation; Social Simulation.

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Figures

Figure 1:
Figure 1:
Schematic representation of our agent-based modeling of local trains.
Figure 2:
Figure 2:
An example of different cohort-to-coach strategies and their impact on network structure. There are four cohorts (colored by black, green, blue, and red) and two coaches (colored by orange and yellow). Two days are shown in the figure. (a) No cohort-to-coach strategy. Passengers from all cohorts are randomly mixing between coaches on different days. (b) Static cohort-to-coach strategy. Members of a cohort travel together and coach assigned to the cohort is fixed across days. (c) Dynamic cohort-to-coach strategy. Members of a cohort travel together and coach assigned to the cohort is arbitrary across days.
Figure 3:
Figure 3:
Y-axis represents daily new detected positive cases. Parameters which vary are labeled in the legend, where as parameters which don’t are noted above the plot. The shaded region represents 1 standard deviation away from the mean, estimated over 5 simulation runs with the selected configuration. Beta represents βcoach, the transmission rate parameter of the interaction in train coaches. Crowding represents the crowding factor of train coaches, which impacts occupancy limit of a train coach. Isolation value of 0 or 1 represents lack or presence of cohort isolation policy respectively. Coach_strategy of 0 or 1 represents static or dynamic coach assignment respectively. One_off_ratio represents proportion of travelers that travel one off, and the remainder (1.0 − one_off_ratio) travel in cohorts of the selected cohort_size. Station detection represents the proportion of symptomatic infected commuters detected at the station, via thermal screening or other testing mechanisms.
Figure 4:
Figure 4:
Effect of cohort size on peak daily case load and total case load at saturation.
Figure 5:
Figure 5:
As cohort size increases, there is a greater contribution of quarantined cases due to cohorting, but the increase in total quarantined cases in the city is marginal.
Figure 6:
Figure 6:
Effect of one-off travelers: As fraction of one-off travelers increase, the daily positive cases increase but partial cohorting does far better than business as usual (cohort size of 1).
See this image and copyright information in PMC

References

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