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Pandemic
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Scientists simulate pandemic influenza outbreak in Chicago
Other topics:
Physician Information System
Virginia Tech
March 10, 2008
Blacksburg, VA -- By using computer simulations and modeling, an
international group of researchers including scientists from the
Virginia Bioinformatics Institute (VBI) at Virginia Tech’s
Network Dynamics and Simulation Science Laboratory (NDSSL) have
determined how a pandemic influenza outbreak might travel
through a city similar in size to Chicago, Ill. This information
helped them to determine the preferred intervention strategy to
contain a potential flu pandemic, including what people should
do to decrease the likelihood of disease transmission.
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The new results, based on three different computer simulation
models, are described in a paper published in the Proceedings of
the National Academy of Sciences by scientists involved in the
Models of Infectious Disease Agent Study (MIDAS).* MIDAS is a
collaboration of research and informatics groups supported by
the National Institutes of Health (NIH) to develop computational
models to examine interactions between infectious agents and
their hosts, disease spread, prediction systems, and response
strategies.
The global epidemic of avian
influenza in bird populations, as well as the risk of a virulent
form of the bird flu virus being transferred to humans, has made
influenza pandemic preparedness a top public health priority in
the United States, Europe, and other countries. The great
influenza pandemic of 1918 resulted in 40 to 50 million deaths
worldwide. If a pandemic were to occur today, it could cause
widespread social and economic disruptions.
In the paper, “Modeling Targeted Layered Containment of an
Influenza Pandemic in the USA,” members of the MIDAS Working
Group on Modeling Pandemic Influenza concluded that a timely
implementation of targeted household antiviral prevention
measures and a reduction in contact between individuals could
substantially lower the spread of the disease until a vaccine
was available.
The groups coordinated efforts to each create individual-based,
computer simulation models to examine the impact of the same set
of intervention strategies used during a pandemic outbreak in a
population similar in size to Chicago, which has about 8.6
million residents. Intervention methods used were antiviral
treatment and household isolation of identified cases, disease
prevention strategies and quarantine of household contacts,
school closings, and reducing workplace and community contacts.
Although using the same population, each model had its own
representation of the combinations of intervention. All of the
simulations suggest that the combination of providing preemptive
household antiviral treatments and minimizing contact could play
a major role in reducing the spread of illness, with timely
initiation and school closure serving as important factors.
“VBI’s computer simulation models are built on our detailed
estimates for social contacts in an urban environment,” said VBI
Professor and NDSSL Deputy Director Stephen Eubank, who leads
the VBI team in the working group. “They provide a realistic
picture of how social mixing patterns change under
non-pharmaceutical interventions such as closing schools or
workplaces. For example, when schools close, young students
require a caregiver’s attention. That can disrupt social mixing
patterns at work if a working parent stays home or make closing
schools pointless if the children are placed in large day-care
settings. We can use our model to suggest the best mix of
intervention strategies in a variety of scenarios, taking
factors like these into account.”
Bruno Sobral, Executive and Scientific Director of VBI,
remarked: “Transdisciplinary science, which is the foundation of
the way we do research at VBI, requires a special type of
collaborative framework at the very outset of a project. The
highly detailed social-network models that underpin this
international research project arise from transdisciplinary
science that removes disciplinary boundaries and promotes
innovation. The impact of this approach to science is
highlighted by the success of this research undertaking which
benefits from a very clear interface between diverse experts in
high-performance computing, disease modeling and public health
practice.”
While the three different models used in the study show that
timely intervention significantly impedes the spread of
influenza through a population, the authors caution against
over-interpretation of the modeling results. The researchers
emphasize that the models are tools that provide guidance rather
than being fully predictive. In the case of a future outbreak of
pandemic influenza, capabilities such as real-time surveillance
and other analyses will hopefully be available for the public
health community and policy makers.
“These models, which are built from the best available data and
with the best tools, contribute greatly to our understanding of
how a pandemic could spread and what measures might protect the
public’s health,” said Jeremy M. Berg, Ph.D., director of NIH’s
National Institute of General Medical Sciences, which supports
the MIDAS program. “But they are not our only resource—field
work and experimental studies remain critical and will enhance
the quality and reliability of these and other models.”
Along with Eubank, Professor and NDSSL Director Chris Barrett,
Professor and NDSSL Deputy Director Madhav Marathe, Simulation
Science Statistician Richard Beckman, graduate student Bryan
Lewis, Assistant Professor and Senior Research Associate Anil
Vullikanti and other members of the NDSSL group contributed to
the study. The teams contributing to the MIDAS working group
include researchers from VBI, the University of Washington, Fred
Hutchinson Cancer Research Center in Seattle, Los Alamos
National Laboratories, Imperial College London, and the
University of Pittsburgh. The paper’s lead author, M. Elizabeth
Halloran, is affiliated to the University of Washington,
Seattle, WA, and the Fred Hutchinson Cancer Research Center,
Seattle, WA.
This work was partially supported by the National Institute of
General Medical Sciences MIDAS network grants U01-GM070749,
U01-GM070694, U01-GM070698, and U01-GM070708.
* Modeling targeted layered containment of an influenza pandemic
in the USA. Proceedings of the National Academy of Sciences
(2008). In press. The paper will be available on-line the week
of March 10-14.
About MIDAS
Models of Infectious Disease Agent Study (MIDAS) is a
collaboration of research and informatics groups established to
develop computational models of the interactions between
infectious agents and their hosts, disease spread, prediction
systems, and response strategies. The models will be useful to
policymakers, public health workers, and other researchers who
want to better understand and respond to emerging infectious
diseases. If a disease outbreak occurs, the MIDAS network may be
called upon to develop specific models to aid public officials
in their decision-making processes. More information about MIDAS
is available at http://www.nigms.nih.gov/Initiatives/MIDAS/
About the Virginia Bioinformatics Institute
The Virginia Bioinformatics Institute (VBI) at Virginia Tech has
a research platform centered on understanding the "disease
triangle" of host-pathogen-environment interactions in plants,
humans and other animals. By successfully channeling innovation
into transdisciplinary approaches that combine information
technology and biology, researchers at VBI are addressing some
of today's key challenges in the biomedical, environmental and
plant sciences. https://www.vbi.vt.edu/ |
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