Collective Response of Human Populations to Large-Scale Emergencies

By James P. Bagrow, Dashun Wang, Albert-László Barabási. PLoS ONE, 2011

356 citations recorded by the source corpus.

Despite recent advances in uncovering the quantitative features of stationary human activity patterns, many applications, from pandemic prediction to emergency response, require an understanding of how these patterns change when the population encounters unfamiliar conditions. To explore societal response to external perturbations we identified real-time changes in communication and mobility patterns in the vicinity of eight emergencies, such as bomb attacks and earthquakes, comparing these with eight non-emergencies, like concerts and sporting events. We find that communication spikes accompanying emergencies are both spatially and temporally localized, but information about emergencies spreads globally, resulting in communication avalanches that engage in a significant manner the social network of eyewitnesses. These results offer a quantitative view of behavioral changes in human activity under extreme conditions, with potential long-term impact on emergency detection and response.

Subfields: Epidemiology, Statistical and Nonlinear Physics, Transportation

View Paper: https://www.semanticscholar.org/paper/bfc23938b16cf2cf24a46357434d183b0303d096

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In Epidemiology

Martin Hilbert

In Statistical and Nonlinear Physics

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Citations in the corpus

Applications of artificial intelligence for disaster management (2020, background): “Therefore, telecommunications volume sharply increases, usually following the jump-delay pattern (Bagrow et al. 2011).”
Dynamic population mapping using mobile phone data (2014, background): “In the specific application presented here, spatially and temporally detailed population distribution datasets potentially may provide the essential denominator required in many fields, such as studying collective human responses to disease outbreaks (61, 62), emergencies (63, 64), or any applicatio”
Predictability of population displacement after the 2010 Haiti earthquake (2012, background): “Data from mobile phones have the advantage of high resolution in time and space, being instantaneously available with no interview bias, and they provide longitudinal data for very large numbers of persons (12, 17-23).”
Measuring Large-Scale Social Networks with High Resolution (2014, background): “The ability to efficiently map communication networks and mobility patterns (using cell towers) for large populations has made it possible to quantify human mobility patterns, including investigations of social structure evolution [166], economic development [67], human mobility [37,38], spreading p”
Urban Sensing Using Mobile Phone Network Data: A Survey of Research (2014, background): “In particular, several works tried to infer the human patterns of mobility during emergencies and special events [Bagrow et al. 2011; Calabrese et al. 2010; Ferrari et al. 2012; Lu et al. 2012; Traag et al. 2011].”