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NIH Research Matters

March 14, 2008

“Virtual” Flu Outbreak Points to Options for Preventing Spread

By simulating an outbreak of pandemic flu as it spreads throughout a large city, researchers have identified public responses—like closing schools and giving anti-viral treatments—that might significantly slow the spread of infection. These tactics could give researchers more time to develop targeted vaccines, the scientists say.

Computer generated model of a virtual city.

Public health officials have long been tracking the highly infectious H5N1 avian flu virus, which primarily infects birds but can also infect humans. Already more than 370 people have contracted the H5N1 strain, and nearly two-thirds have died, according to the World Health Organization. H5N1 cannot easily spread from one person to another. But if the virus mutates and gains that ability, it could quickly spread worldwide, just as the “Spanish Flu” virus did in 1918.

Because a well-matched vaccine for pandemic flu will take time to produce, health officials want to identify other strategies that could reduce infections while a vaccine is being manufactured. Three research teams involved in the Models of Infectious Disease Agent Study, supported by NIH's National Institute of General Medical Sciences (NIGMS), responded to this need.

As reported in the early online edition of the Proceedings of the National Academy of Sciences on March 10, 2008, each team developed a different computational model representing a virtual city of about 8.6 million people—a size similar to Chicago. They designed their models to simulate an outbreak of pandemic flu and the efforts to contain its spread. People in the virtual communities interacted at school, work, home and other settings.

The researchers created several scenarios to test different combinations of interventions. Responses included medical approaches, like antiviral treatment and prevention, and non-medicinal strategies, like school closures and travel restrictions.

The models, though designed differently, generated similar results. They projected that, with no interventions, between 47% and 60% of the population would have flu symptoms. But when quick steps were taken—like shutting down schools and giving antiviral medicines to about half of those infected, along with their close contacts—the number of flu cases dropped between 83% and 94%. These reductions were achieved when even just a small fraction of people adhered to quarantine and social-distancing measures.

“The good news was that all three of the disease-modeling groups involved in the study found that an outbreak of pandemic flu similar to the pandemic of 1918 could be mitigated if these measures were implemented quickly,” says lead author Dr. M. Elizabeth Halloran of the Fred Hutchinson Cancer Research Center and University of Washington.

While computer models can't predict the real-world effectiveness of public health measures with certainty, these simulations can help guide planning and preparation for a deadly flu outbreak in the future.

—by Emily Carlson

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Editor: Harrison Wein, Ph.D.
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NIH Research Matters is a weekly update of NIH research highlights from the Office of Communications and Public Liaison, Office of the Director, National Institutes of Health.

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This page last reviewed on December 3, 2012

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