Health officials try to stay a step ahead of viruses and bacteria in order to prevent their spread and the possibility of a pandemic. Scientists at Virginia Tech are now taking things further. They’ve created a computer model that realistically simulates the spread of disease and may be an impo rtant tool when real pandemics happen. From IEEE Spectrum’s “Responding to Disasters: From Prediction to Recovery” special, Prachi Patel has our report.
GELLERMAN: Scientists now have a new weapon in the on-going war between evolving bacteria and antibiotic drugs - it’s virtual reality. In computer games, the simulated environments put you in the midst of imaginary worlds. At Virginia Tech, researchers have created a computer model to help health officials tackle pandemics in the real world.
Reporter Prachi Patel toured the place where virtual reality and real threat meet, and has our story as part of the the IEEE Spectrum program “Responding to Disasters: From Prediction to Recovery”.
PATEL: In 1976, a swine flu virus infected military recruits in Fort Dix, New Jersey. One man died, but the bug never spread beyond the base. It just disappeared on its own. Fast-forward 33 years.
[CLIP FROM RADIO/TV ON SWINE FLU: The World Health Organization has declared a swine flu pandemic as the disease continues to spread around the world.]
PATEL: The 2009 H1N1 virus was completely different. It tore around the globe, infecting 61 million people, and killing 12,000. Viruses and bacteria are notoriously hard to predict. So how do you tackle a pandemic? Well, one way to understand how a disease could spread is to computerize the situation, and see what happens with an artificial population. During the 2009 H1N1 outbreak, U.S. health officials used a computer model built by researchers at Virginia Tech. The model is called EpiSimdemics. Think of it as an artificial America, built inside a supercomputer. Christopher Barrett is one of the project’s leaders.
BARRETT: EpiSimdemics provides a way to generate a real-world social network from detailed modeling of individual activities and you can spread a disease over those individuals that are interacting in that network.
PATEL: First, the researchers built a synthetic population. They used census data to mimic the real population of, say, Chicago, or all of America. Next, they assigned daily activities to each artificial person, again, based on actual social surveys. Then they added models of people’s movements.
BARRETT: So if they were going to drive, for example, you might need models of traffic - detailed traffic - with every individual in a vehicle or in a bus or something so that you can figure out where they are, who they’re next to.
PATEL: What they end up with is a very large real-world social network that changes with time. Finally, they incorporated models of various diseases such as the common flu, swine flu and HIV, based on how they spread and how infectious they are. Now, you can introduce a few infected individuals and see how a disease spreads.
MARATHE: The part that is also innovative in these class of models is a representation of behaviors and how individuals react in face of diseases.
PATEL: Computer scientist Madhav Marathe helped create EpiSimdemics. He points out a key innovation: its adaptiveness to human behavior.
MARATHE: The individual behaviors, the disease and the social contact network all change in response to each other. For instance, I decide not to go to work, or I decide to get antivirals, or decide not to send my child to work. This in effect changes how the disease continues to move on the fabric of the social contact network that has just been changed.
PATEL: Of course, the simulation can’t tell exactly what’s going to happen in a pandemic. But public health officials can tweak the model, say, introduce a school closure or make a vaccine available, and see how it affects things. That gives them a good overall understanding of what can happen and what can go wrong. In 2009, for instance, EpiSimdemics helped government agencies plan a counter-attack.
MARATHE: The question that we were posed, along with other groups, was - if you’re given this small quota of vaccines, how do you decide whom to vaccinate? And remember that the decision has to be done under the following different criteria: how many people do you save, how much control you can achieve from the disease, what’s the potential economic impact, how can you save critical workers so the society can keep functioning and so on, and so forth.
PATEL: Different decisions, of course, might have led to different outcomes. In the end, computer models like these might not stop a disease in its tracks, but they can certainly help save lives. For Living on Earth, this is Prachi Patel.
GELLERMAN: Our story is part of the IEEE Spectrum, National Science Foundation program “Responding to Disasters, from Prediction to Recovery.” For more information, go to our website - LOE dot org.
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