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Scientists are creating their own virtual worlds where people live and work—and get sick. Here, researchers can mimic viruses and predict the spread of contagious diseases through a community. Successful simulations can help us better prepare for real-life outbreaks.

»  Preparing for a Pandemic
»  The Rise & Fall of Deadly Dengue

Preparing for a Pandemic

In 2001, malicious mail containing anthrax endangered hundreds of people. In 2003, the SARS virus traveled the globe, infecting about 8,000 people. More than 700 of them died. Health officials say that a fatal flu is among our future threats.

Right now, researchers participating in an international project called MIDAS are simulating the potential spread of such a pandemic influenza. While a flu virus capable of infecting millions of people worldwide hasn't emerged recently, many health officials fear that it soon could if the avian flu spreading among birds in parts of Asia, Europe, and Africa becomes easily transmissible between people.

Flu and You

To create the pandemic flu simulations, the MIDAS researchers use computer models to build virtual cities, countries, and even continents. Here, thousands of pretend people go to school, work, stores, and other places. The researchers base the residents' activities on information about actual people like you.

Stephen Eubank, a physicist at Virginia Tech University in Blacksburg and part of the MIDAS team, has modeled virtual versions of major U.S. metropolitan areas using local transportation and census data. In Eubank's cities, there really are six (or fewer) degrees of separation between any two people—making it easy for germs to spread.

How would your simulated life be different from your best friend's?

"Viruses don't care much about geography," says Eubank. "They care about social networks and how people come into contact with each other."

Virtual Viruses

Another key part of studying the spread of infection with computers involves developing a virtual version of the germ. To model its spread as realistically as possible, the researchers track down everything known about the infectious agent. Eubank, who has studied plague, smallpox, and anthrax, has gathered information on how each agent spreads between people, how contagious it is, and how long it takes for an infected person to show symptoms.

Not knowing the actual characteristics of such a virus, the MIDAS researchers use health reports and scientific data collected during earlier flu pandemics to estimate what a future one might be like.

Christina Mills, now a medical student at Harvard Medical School in Cambridge, Massachusetts, did a lot of her research in the library. She scoured the shelves for scientific articles that discussed the 1918 Spanish flu, a pandemic that killed between 20 and 40 million people. Most of the people who died were young.

"It was very old-fashioned," says Mills, who's studying international health. "I couldn't just type a search word into Google™ and get the necessary information." The hunt eventually led her to the 1918 transmission rates.

Asking Questions

Create a timeline
of what you did yesterday. List all the people (even if you don't know their names) you came into contact with. If you were contagious with the seasonal flu, how many of these people do you think you would have infected? The answer is surprisingly low. Estimates suggest that you'd pass the virus to no more than three people. But if more than one other person catches it from you, the bug will continue to spread.

With these pieces in place, the MIDAS researchers invite policymakers to ask questions that can be answered using the models. Questions range from What happens if we don't do anything? to How many people could be protected if we intervene?

The researchers create different simulations that change the variables, like the contagiousness of the virus or the number of people taking "snow days"—Eubank's term for people who voluntarily hang out at home to avoid infection.

"What's so great about the computer simulations is that you can try out different situations that you can't create in real societies," says Eubank.

With more than 250 possible combinations to simulate, Eubank says he relies on statisticians to help him determine which arrangements will produce the most informative results.

"It's easy to come up with questions," says Mills. "The hard part is figuring out which ones we should—and could—answer."

Because of the amount of data and calculations involved, the simulations run on high-performance computers that can simulate a 180-day outbreak in a matter of hours. Eubank uses software programs to take snapshots of the pretend pandemic as it occurs.

"I know exactly when a virtual person gets infected, shows symptoms, and recovers," says Eubank, explaining that the computer records every change in disease state.

Flu Forecast

These maps of the United States display the potential spread of pandemic flu. Each dot changes from green to red as more people in that area get sick. The top map shows what could happen if we don't do anything. The bottom map shows the effect of giving people a less effective vaccine while a better one is being developed.
Proceedings of the National Academy of Sciences

Watch the flu spread.

Eubank and other researchers modeling pandemic flu have simulated outbreak scenarios in virtual versions of Southeast Asia, the United States, and the United Kingdom. Even though these countries have different populations and transportation patterns, the researchers found similar results: The early implementation of a combination of intervention measures, such as vaccinating certain people and giving medicine to those who do get sick, was the most effective at either stopping or slowing the spread of infection.

While the results generated by the simulations are useful, Eubank stresses that they're not a guarantee of what actually will happen. He and others often will ask different models the same questions and, when the models agree, they'll have more confidence in the predictions.

Meet the Simulators

Stephen Eubank started out studying high-energy physics but then got into modeling the dynamics of nonlinear systems, which are systems that can't be solved by adding up all of the parts. He has developed computational models to study natural languages, traffic patterns, and financial markets. He plans to use the infectious disease models to study how behaviors, like smoking, spread through society.

Christina Mills has a Sc.D. (like a Ph.D.) and is now working toward an M.D. For her, modeling infectious diseases is a dream job because it combines her interests in math, biology, and human health. While most of her classmates getting double degrees will go on to practice "bench to bedside" research in which they translate lab findings into patient care, Mills says she'll stick with the "computers to clinics" approach.—EC

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The Rise & Fall of Deadly Dengue
By Alison Davis

This map from 2007 shows areas infested with the mosquito that carries the dengue virus (orange) and areas with both the mosquito and dengue epidemic activity (red).
Credit: Centers for Disease Control and Prevention

If you live in the United States and don't travel abroad, chances are you'll never come down with dengue fever. That's not the case for people living in tropical and subtropical climates, like South America, Africa, and the Caribbean.

Between 50 and 100 million of these people catch the mosquito-transmitted dengue virus every year. Most of them will bounce back after 2 weeks of rest and extra fluids. A small percentage, however, won't be so lucky. After contracting dengue a second time, some people may develop a potentially fatal dengue hemorrhagic fever.

Scientists suspected that the human immune system might be to blame for making the second infection more dangerous, but until recently they weren't sure how.

Using computer simulations, epidemiologists Derek Cummings and Donald Burke at the Johns Hopkins Bloomberg School of Public Health in Baltimore, Maryland, learned that the infected person's antibodies—proteins that should fight off dengue—actually help the virus copy itself. More copies make the virus a better predator, allowing it to spread faster and infect more people.

But the researchers also learned that the virus actually causes its own demise. Like a hungry wolf pack that clears out the local deer population, the virus eventually starves itself. Infecting too many people reduces its "food" supply.

This work is just one example of how researchers can develop models to answer questions about outbreaks of dengue or other diseases. With a mathematically based model, ecologist Pejman Rohani at the University of Georgia in Atlanta examined 30 years of epidemiological data from Thailand, a hot spot for dengue. He learned that environmental factors, like warmer temperatures, can re-route mosquito flyways and in turn change dengue infection rates.

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