Suchergebnisse

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.

This work quantifies the impact of interventions to curtail mobility and social interactions in order to control the COVID-19 pandemic. We analyze the change in world-wide mobility at multiple spatio-temporal resolutions -- county, state, country -- using an anonymized aggregate mobility map that captures population flows between geographic cells of size 5 km (2) . We show that human mobility underwent an abrupt and significant change, partly in response to the interventions, resulting in 87% reduction of international travel and up to 75% reduction of domestic travel. Taking two very different countries sampled from the global spectrum, we observe a maximum reduction of 42% in mobility across different states of the United States (US), and a 68% reduction across the states of India between late March and late April. Since then, there has been an uptick in flows, with the US seeing 53% increase and India up to 38% increase with respect to flows seen during the lockdown. As we overlay this global map with epidemic incidence curves and dates of government interventions, we observe that as case counts rose, mobility fell -- often before stay-at-home orders were issued. Further, in order to understand mixing within a region, we propose a new metric to quantify the effect of social distancing on the basis of mobility. We find that population mixing has decreased considerably as the pandemic has progressed and are able to measure this effect across the world. Finally, we carry out a counterfactual analysis of delaying the lockdown and show that a one week delay would have doubled the reported number of cases in the US and India. To our knowledge, this work is the first to model in near real-time, the interplay of human mobility, epidemic dynamics and public policies across multiple spatial resolutions and at a global scale.