texas magazine

It’s been nearly a year since the H1N1 virus first appeared, and it’s still spreading. While health care workers focus on vaccines and treatment, McCombs statistician Paul Damien attacks the pandemic with a different tool: math.

The H1N1 influenza virus first appeared in its current form in Mexico in March 2009. In April, cases cropped up in California, Texas and New York. By the end of the month, as the World Health Organization declared a public health emergency, the virus had spread to Canada, Europe and the Middle East and had claimed lives in Mexico and Texas.

Panic and confusion began to set in as people tried to cope with the new disease. Schools, hospitals, governments and public transportation authorities were forced to make decisions about closures, quarantines and restricted services without fully understanding the disease or its potential for harm.

And they weren’t easy decisions to make. Take schools, for example. Consider the complexities and ramifications of the decision to close one school, even for a few days. How many sick children warrant shutting down a school? What if a child has only a suspected case of H1N1? How long should the school remain closed? How are working parents supposed to arrange last-minute child care?

Individuals may find themselves in equally murky waters. How do I know if I have H1N1 or just the seasonal flu? Should I stay home from work? Is it necessary to go to the doctor?

Paul Damien isn’t a health care worker or a virologist, but he’s trying to help answer those questions and stop the spread of H1N1. Damien is a professor in the Department of Information, Risk, and Operations Management at McCombs, and he’s using math and statistics to fight the disease and make sense of all the behavioral and medical variables.

Using Math to Map

A pandemic is often portrayed as a menacing virus uncontrollably creeping across the map, swallowing up cities, then states, then entire nations. But that methodical depiction ignores the human element of infection. Our individual behaviors affect how a disease spreads just as much as the nature of the virus itself. Discovering the impact and consequences of those behaviors is crucial to understanding and ultimately ending the pandemic.

By gathering mountains of data—how many people you came in contact with yesterday, how many stories your newspaper ran about the flu, your intention to get vaccinated—Damien is helping create mathematical models that can predict infection rates and transmission patterns. Those models can then be used by organizations like the Centers for Disease Control (CDC) and the National Institutes of Health (NIH) to help inform their public outreach efforts and make recommendations on decisions such as when to close a school.

“Only by using mathematical methods can we best quantify these uncertainties,” Damien says. “Just as a thermometer measures temperature, or a ruler, length, probability is the best way to measure uncertainty, leading to useful ways of assessing the various risks associated with H1N1. And this is what we are attempting to do.”

Flu fighter Paul Damien.

Funded by a $3 million, five-year grant from the NIH, Damien is partnering with Lauren Ancel Meyers, a mathematical biologist in UT’s College of Natural Sciences, and Yale mathematical epidemiologist Alison Galvani, along with graduate students at both universities. The grant is part of the Models of Infectious Disease Agent Study program, a national network of researchers using mathematical models to help public health officials better predict, intervene and contain contagious diseases.

Damien and the research team are surveying families, tracking media coverage of the disease and monitoring reports from physicians and school districts to create a deep reservoir of data that can illustrate the spread of H1N1 and news and opinions about the disease.

“Think of all the people in Texas as a network, where everyone is coming in contact with someone,” Damien says. “The more contact points you have with people that could become infected, the more susceptible the population is to the infection.

“This is particularly true at UT, for example,” he adds. “You have 50,000 students on campus, many of whom may have just come back from different parts of the world. Some of them already have a flu virus. Who did they last contact? Which part of the world did they go to? In that part of the world, was H1N1 already rampant? Those are parts of the model that could help determine infection rate.”

Damien and his research partners are in regular contact with health organizations such as the CDC, providing critical updates as more is learned about the disease.

For instance, the researchers noticed an unusually large number of pregnant women were becoming infected, indicating a unique vulnerability to H1N1 in that population. They shared that data with the CDC, which, in turn, produced an education campaign targeted at pregnant women.

H1N1 Fact and Fiction

Public understanding of the disease is a critical component of the study. Despite constant updates from the CDC and the news media, many people still don’t understand H1N1, Damien says. The biggest misunderstanding is the difference between seasonal flu and H1N1.

“The symptoms of H1N1 are quite similar to the regular flu virus, so there is confusion about when to go to the doctor,” Damien says. “It’s not very clear to a lot of people what are the exact symptoms they should be worried about. We see this every year with people trying to distinguish between the flu and the common cold.”

Damien says H1N1 patients experience the same symptoms as seasonal flu, but often also suffer from a severe cough and nausea. But their survey results indicate many people aren’t aware of those differentiating symptoms.

So while some may scoff at H1N1’s continued prominence in the news, most people could still benefit from paying close attention to media coverage of the disease, Damien says. In addition to increasing understanding about the disease itself, Damien’s research shows that the frequency of news reports about H1N1 is positively correlated with people’s interest in seeing a doctor and getting a vaccine.

The models are also helping establish a basic portrait of the H1N1 virus.

“These models will help us understand the life cycle of the virus,” Damien says. “When does it peak? When does it actually leave your system, and how long are you contagious? That’s a very critical aspect. The contagion aspect of H1N1 is a lot more vicious than the regular flu virus, because it tends to remain latent in the body in such a way that even though you’re improving, you could still pass it on to someone else. Whereas with the regular flu virus, you’re most contagious in the early stages.”

Another component of understanding the virus is tracking how it responds to vaccines and treatment. Damien says that until recently scientists believed two doses of the vaccine administered over three weeks were needed in order to be effective. But new research indicates that one dose may be sufficient for a certain segment of the population. That finding alone has massive implications for how doctors treat patients and also for how pharmaceutical companies manufacture and distribute the vaccines.

Damien and the other researchers are only halfway through their study. This time next year, when they have a full flu season’s worth of data on record, their picture of the H1N1 virus will become even clearer.

By Tracy Mueller

Additional reporting by Lee Clippard