Modelling to contain pandemics
Agent-based computational models can capture irrational behaviour, complex social networks
and global scale — all essential in confronting H1N1, says Joshua M. Epstein.
modelling is playing a central part in mapping
the disease’s possible spread, and designing
policies for its mitigation.
Classical epidemic modelling, which began
in the 1920s, was built on differential equa-
tions. These models assume that the popula-
tion is perfectly mixed, with people moving
from the susceptible pool,
to the infected one, to the
recovered (or dead) one.
Within these pools, every-
one is identical, and no one
adapts their behaviour. A tri-
umph of parsimony, this approach
revealed the threshold nature of epidem-
ics and explained ‘herd immunity’, where
the immunity of a subpopulation can stifle
outbreaks, protecting the entire herd.
But such models are ill-suited to captur-
ing complex social networks and the direct
contacts between individuals, who adapt their
behaviours — perhaps irrationally — based on
Agent-based models (ABMs) embrace this
complexity. ABMs are artificial societies: every
single person (or ‘agent’) is represented as a dis-
tinct software individual. The computer model
tracks each agent, ‘her’ contacts and her health
status as she moves about virtual space — travel-
ling to and from work, for instance. The models
can be run thousands of times to build a robust
statistical portrait comparable to epidemic data.
ABMs can record exact chains of transmission
from one individual to another. Perhaps most
importantly, agents can be
made to behave something like
real people: prone to error, bias,
fear and other foibles.
Such behaviours can have a
huge effect on disease progres-
sion. What if significant num-
bers of Americans refuse H1N1 vaccine out of
fear? Surveys and historical experience indicate
that this is entirely possible, as is substantial
absenteeism among health-care workers. Fear
itself can be contagious. In 1994, hundreds of
thousands of people fled the Indian city of Surat
to escape pneumonic plague, although by World
Health Organization criteria no cases were con-
firmed. The principal challenge for agent mod-
elling is to represent such behavioural factors Models, however, are not crystal balls
s the world braces for an autumn wave
of swine flu (H1N1), the relatively new
technique of agent-based computational
appropriately; the capacity to do so is improv-
ing through survey research, cognitive science,
and quantitative historical study.
Robert Axtell and I published a full agent-
based epidemic model1 in 1996. Agents with
diverse digital immune systems roamed a land-
scape, spreading disease. The model tracked
dynamic epidemic networks, simple mecha-
nisms of immune learning, and behavioural
changes resulting from disease progression, all
of which fed back to affect epidemic dynamics.
However, the model was small (a few thousand
agents) and behaviourally primitive.
Now, the cutting edge in performance is the
Global-Scale Agent Model (GSAM)2, developed
by Jon Parker at the Brookings Institution’s
Center on Social and Economic Dynamics in
Washington DC, which I direct. This includes
6.5 billion distinct agents, with movement
and day-to-day local interactions modelled as
available data allow. The epidemic plays out
on a planetary map, colour-coded for the dis-
ease state of people in different
regions — black for suscepti-
ble, red for infected, and blue
for dead or recovered. The
map pictured shows the state of
affairs 4.5 months into a simu-
lated pandemic beginning in
Tokyo, based on a plausible H1N1 variant.
For the United States, the GSAM contains 300
million cyber-people and every hospital and
staffed bed in the country. The National Center
for the Study of Preparedness and Catastrophic
Event Response at Johns Hopkins University in
Baltimore is using the model to optimize emer-
gency surge capacity in a pandemic, supported
by the Department of Homeland Security.
and the simulation shown here is not a pre-
diction. It is a ‘base case’ which by design is
highly unrealistic, ignoring pharmaceuticals,
quarantines, school closures and behavioural
adaptations. It is nonetheless essential because,
base case in hand, we can rerun the model to
investigate the questions that health agencies
face. What is the best way to allocate limited
supplies of vaccine or antiviral drugs? How
effective are school or work closures?
Agent-based models helped to
shape avian flu (H5N1) policy,
through the efforts of the National
Institutes of Health’s Models of
Infectious Disease Agent Study
(MIDAS) — a research network
to which the Brookings
Institution belongs. The
GSAM was recently
presented to officials
from the Centers for
Disease Control and
Prevention in Atlanta,
Georgia, and other agencies, and will be inte-
gral to MIDAS consulting on H1N1 and other
emerging infectious diseases. In the wake of
the 11 September terrorist attacks and anthrax
attacks in 2001, ABMs played a similar part in
designing containment strategies for smallpox.
These policy exercises highlight another
important feature of agent models. Because
they are rule-based, user-friendly and highly
visual, they are natural tools for participatory
modelling by teams — clinicians, public-health
experts and modellers. The GSAM executes
an entire US run in around ten minutes, fast
enough for epidemic ‘war games’, giving deci-
sion-makers quick feedback on how interven-
tions may play out. This speed may even permit
the real-time streaming of surveillance data for
disease tracking, akin to hurricane tracking. As
H1N1 progresses, and new health challenges
emerge, such agent-based modelling efforts
will become increasingly important.
Joshua M. Epstein is director of the Center on
Social and Economic Dynamics at the Brookings
Institution, 1775 Massachusetts Avenue,
Washington DC 20036, USA.
1. Epstein, J. M. & Axtell, R. L. Growing Artificial Societies:
Social Science from the Bottom Up Ch. V. (MIT Press, 1996).
2. Parker, J. A. ACM Trans Model. Comput. S. (in the press).
See Opinion, page 685, and Editorial, page 667.
Further reading accompanies this article online.
“Agents can be made
to behave something
like real people: prone
to error, bias, fear.”
Simulation of a pandemic beginning in Tokyo.
NATURE|Vol 460|6 August 2009
© 2009 Macmillan Publishers Limited. All rights reserved