U of G Prof Modelling Spread of H1N1 Flu Virus

May 05, 2009 - News Release

A University of Guelph mathematics professor is developing a model to predict the spread of the H1N1 flu virus and guide public health strategies to combat a potential pandemic.

Prof. Chris Bauch is one of a handful of researchers in Ontario who were approached last fall by the Ontario Agency for Health Protection and Promotion (OAHPP) to create mathematical models to help with the province's pandemic preparedness planning. In recent weeks, they've stepped up the collaborative effort to focus on the transmission of H1N1 in order to predict the spread of the disease.

“These models play an important role in deciding which mitigation strategies to adopt and which ones not to,” said Bauch. “For example, when do public health agencies decide it’s time to tap into the stockpile of anti-virals? Are more drastic measures required, like closing schools?”

In previous work, Bauch has used game theory to predict the spread of infectious diseases such as smallpox. Game theory is a mathematical analysis of how individuals make decisions in groups when the impact of their decision depends on the decisions reached by others in the group.

A working model for the transmission of the new H1N1 flu virus is still a couple of weeks away, he said, but there are important lessons to be learned from modelling and previous pandemics.

“All our previous models have emphasized the importance of acting early and aggressively to mitigate the spread of infection. What might seem like overkill isn’t necessarily a bad thing.”

A critical piece of the H1N1 flu puzzle is calculating the “R-zero” of the virus, to predict how quickly the pathogen can spread through the population. R-zero is the number of people who can be infected by a single infected person.

For the seasonal flu, the R-zero is about 1.5. Measles and chicken pox have an R-zero of 9 or 10 and even higher, while a pandemic typically has an R-zero of 2. That might not sound very high, but it’s at that point that the number of infections begins to grow exponentially.

“I think there is a general lack of intuition for how incredibly rapid exponential growth can be,” said Bauch.

He illustrates by referring to a classic mathematical problem involving grains of rice (or in some versions of the fable, wheat) on a chessboard. If you place grains of rice on each square of the chessboard, starting with a single grain on the first square, two on the second and four on the third, then doubling the number on each subsequent square, how many would be on the chessboard at the finish?

The answer: many times more than the total from one harvest if all the Earth’s arable land was devoted to growing rice.

That’s why in an outbreak situation, it’s essential to implement effective strategies to mitigate the R-zero.

While there are still major gaps in understanding how the current crisis developed, Bauch said it is possible to extrapolate from data from communities where the infection has already run its course: communities such as La Gloria, Mexico, where up to 60 per cent of the population may have been infected.

The good news is that so far in Ontario, there have been relatively few confirmed cases and the rate of secondary infection with H1N1 flu virus appears to be quite low. Conditions in the spring and summer months, when people spend more time outdoors, also tend to make it more difficult for infections to spread.

But that could change.

“Right now, we’ve got a small sample size issue that makes it difficult to predict what will happen,” he said.

“Also, we’ve seen with pandemics in the past that a relatively mild wave of infection can emerge in the spring, then in the fall we get hammered with a more virulent strain of the disease.”

Chris Bauch
Department of Mathematics and Statistics
519-823-4120, Ext. 53079

For media questions, contact Communications and Public Affairs: Lori Bona Hunt, Ext. 53338, lhunt@uoguelph.ca, or Barry Gunn, Ext. 56982, bagunn@uoguelph.ca.

University of Guelph
50 Stone Road East
Guelph, Ontario, N1G 2W1