Various intervention methods have been introduced worldwide to slow down the spread of the SARS-CoV-2 virus , by limiting human mobility in different ways. While large scale lockdown strategies are effective in reducing the spread rate, they come at a cost of significantly limited societal functions. We show that natural human mobility has high diversity and heterogeneity such that a small group of  individuals and gathering venues play an important role in the spread of the disease. We discover that interventions that focus on protecting the most active individuals and most popular venues can significantly reduce the peak infection rate and the total number of infected people while retaining high levels of social activity overall. This trend is observed universally in multi-agent simulations using three mobility data sets of different scales, resolutions, and modalities (check-ins at seven different cities , WiFi connection events at a university, and GPS traces of electric bikes), and suggests that strategies that exploit the network effect in human mobility provide a better balance between disease control and normal social activities.
Major interventions have been introduced worldwide to slow down the spread of the SARS-CoV-2 virus . Large scale lockdown of human movements are effective in reducing the spread , but they come at a cost of significantly limited societal functions. We show that natural human movements are statistically diverse, and the spread of the disease is significantly influenced by a small group of active individuals and gathering venues . We find that interventions focused on these most mobile individuals and popular venues reduce both the peak infection rate and the total infected population while retaining high levels of social activity . These trends are seen consistently in simulations with real human mobility data of different scales, resolutions, and modalities from multiple cities across the world. The observation implies that compared to broad sweeping interventions, more targeted strategies based on the network effects in human mobility provide a better balance between pandemic control and normal social activities.
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Various intervention methods
Major interventions
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, by limiting human mobility in different ways. While large scale lockdown strategies
. Large scale lockdown of human movements
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rate,
, but
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mobility has high diversity and heterogeneity such that
movements are statistically diverse, and the spread of the disease is significantly influenced by
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active
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play an important role in the spread of the disease. We discover that interventions that focus on protecting the most active individuals and most popular venues can significantly reduce
. We find that interventions focused on these most mobile individuals and popular venues reduce both
R
number of infected people
infected population
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overall. This trend is observed universally in multi-agent simulations using three mobility data sets
. These trends are seen consistently in simulations with real human mobility data
R
(check-ins at seven different cities , WiFi connection events at a university, and GPS traces of electric bikes), and suggests that strategies that exploit the network effect
from multiple cities across the world. The observation implies that compared to broad sweeping interventions, more targeted strategies based on the network effects
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disease
pandemic