Strategizing COVID-19 Lockdowns Using Mobility Patterns

TL;DR

This paper proposes a flexible, multi-level quarantine strategy based on analyzing mobility patterns and community detection to control COVID-19 spread while minimizing societal disruption.

Contribution

It introduces a novel approach using social mobility networks and community detection to inform targeted quarantine policies during the COVID-19 pandemic.

Findings

Identification of natural social boundaries via community detection.

Effective targeting of quarantine measures based on community structure.

Potential to reduce economic and social disruption through mobility-based policies.

Abstract

During the COVID-19 pandemic, governments have tried to keep their territories safe by isolating themselves from others, limiting non-essential travel and imposing mandatory quarantines for travelers. While large-scale quarantine has been the most successful short-term policy, it is unsustainable over long periods as it exerts enormous costs on societies. As a result, governments which have been able to partially control the spread of the disease have been deciding to reopen businesses. However, the WHO has warned about the risks of re-opening prematurely, as is playing out in some countries such as Spain, France and various states in the US such as California, Florida, Arizona, and Texas. Thus, it is urgent to consider a flexible policy that limits transmission without requiring large scale and damaging quarantines. Here, we have designed a multi-level quarantine process based on the mobility patterns of individuals and the severity of COVID-19 contagion in the US. By identifying the natural boundaries of social mobility, policymakers can impose travel restrictions that are minimally disruptive to social and economic activity. The dynamics of social fragmentation during the COVID-19 outbreak are analyzed by applying the Louvain method with modularity optimization to weekly mobility networks. In a multi-scale community detection process, using the locations of confirmed cases, natural break points as well as high risk areas for contagion are identified. At the smaller scales, for communities with a higher number of confirmed cases, contact tracing and associated quarantine policies is increasingly important and can be informed by the community structure.