Understanding the movement and behaviour of crowds is essential to minimising health hazards at mass gatherings (MGs).
Computer
models that provide accurate simulations of how crowds behave can be
used to identify health and safety issues at MGs, and could be adapted
to simulate the spread of infections and to test the potential of public
health interventions to disrupt or prevent an outbreak, according to
the fourth paper in The Lancet Infectious Diseases Series on mass gatherings health.
In
the paper, Dr Anders Johansson from the University of Bristol’s
Department of Civil Engineering and colleagues review how crowd behavior
can be sensed, analyzed, and modelled, and explain how this knowledge
can be used to manage environments in which MGs take place to improve
safety and security and lessen the risks of injury or death.
Large
crowd disasters such as stampedes are major causes of death and injury
at MGs, the inevitable result of extreme crowding. In 2010, ineffective
crowd control and a poorly designed venue (the site was designed for 250
000 participants, but had 1.4 million) resulted in a stampede in a
narrow tunnel during the Love Parade music festival in Germany, in which
21 people were crushed to death and 500 injured.
The
authors observe that although the objective of mass gatherings is to
bring people together, crowd management strategies aim to keep people
separated (in time and space). To resolve this paradox requires
environmental management to guide the appropriate movement and emotion
of the crowd.
State-of-the-art
agent-based computer models use fine-scale data from actual movements
of individuals obtained by techniques such as detailed video recordings,
Global Positioning System (GPS), or mobile phone tracking to identify
points of congestion and overcrowding that are useful for crowd
management.
Such
models have already been used for the Notting Hill Carnival to simulate
the ways crowds interact and disperse under different conditions of
movement and congestion, and to assess alternative routes to reduce the
number of accidents, delays in treatment, and public order offences.
Johansson
and colleagues also describe how models of crowd movement can be
adapted to take into account other scenarios, for example, how
individuals in confined spaces might spread disease through their
proximity.
This
new modelling approach to the spread of epidemics incorporates
population-level features typically used in epidemiological models,
while also taking into account individual-level behaviour and features
that could prevent the spread of disease (eg, immunisation, screening,
and quarantine).
They
conclude: “Such models would allow us to test various interventions on a
virtual population with a computer and measure their success rates
before testing them on real populations, possibly saving both resources
and life.”
