[Editors] MIT: early action key to reducing flu death toll

[Elizabeth Thomson]

MIT News Office
Massachusetts Institute of Technology
Room 11-400
77 Massachusetts Avenue
Cambridge, MA  02139-4307
Phone: 617-253-2700
http://web.mit.edu/newsoffice/www

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MIT: early action key to reducing flu death toll

--Engineer who survived pandemic of '68 creates model to track outbreak
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For Immediate Release
THURSDAY, MAY 31, 2007
Contact: Elizabeth A. Thomson, MIT News Office
Phone: 617-258-5402
Email: thomson at mit.edu

PHOTO AVAILABLE

CAMBRIDGE, Mass.--Nearly 40 years ago, MIT Professor Richard Larson 
spent a week sick in bed with the worst illness he'd ever had-the 
particularly virulent strain of flu that swept the globe in 1968. 
"That was the sickest I'd ever been," Larson recalled. "I really 
thought that was the end." It took him two or three months to recover 
fully from the illness.

Known as the Hong Kong flu, the virus killed 750,000 people 
worldwide, the second worst influenza pandemic the world has seen 
since the infamous 1918-1919 epidemic of so-called Spanish flu.

Now, many experts fear the world is on the brink of another deadly 
flu pandemic. And Larson wants to be sure that people are ready to 
deal with it.

To that end, he and his colleagues have developed a mathematical 
model to track the progression of a flu outbreak. Their results show 
that the death toll of an epidemic could be greatly reduced by 
minimizing social contacts and practicing good hygiene, such as 
frequent handwashing, as early as possible.

The report, "Simple Models of Influenza Progression within a 
Heterogeneous Population," will be published in the May-June issue of 
Operations Research, which comes out June 4.

"We can't reduce to zero the chance that any of us will get the next 
bad flu. But there is compelling evidence that we can reduce the 
chances of our loved ones and ourselves getting the flu by a 
significant factor," said Larson, the Mitsui Professor of Engineering 
Systems and of civil and environmental engineering.

The H5N1 strain of flu, also known as avian flu, has infected birds 
throughout Asia and Europe, with a few known cases among humans. So 
far, the disease has not mutated to a form where it can jump easily 
between humans, but if that happens, the disease could spread around 
the world in days or weeks.

Larson's research team decided to model the progress of such an 
epidemic, taking a unique approach. Unlike most existing models, 
theirs takes into account people's different levels of social 
activity and susceptibility to the flu.

One of the report's key findings is that "social distancing"-reducing 
the frequency and intensity of person-to-person contact-could be an 
effective way to limit the spread of the disease.

Influenza is normally spread by person-to-person contact, so people 
who have more contact with others have a higher risk of catching the 
disease and then spreading it. However, most existing influenza 
models assume that all individuals within a population have the same 
degree of social contact. They also assume that social behavior does 
not change over the course of the epidemic.

Such models "didn't do justice to the complexity of the problem," Larson says.

He and his team developed a dynamic mathematical model that assumes a 
heterogeneous population with different levels of flu susceptibility 
and social contact. They then used the model to compare different 
scenarios: one where people maintained their social interactions as 
the flu spread, and others where they did not.

Their results showed that reducing the social contacts of people who 
normally have the most interactions could dramatically slow early 
growth of the disease. Most of the disease spread is due to a 
minority of the population-the people with the most daily human 
contacts.  Focusing on these individuals and reducing their daily 
contacts can change an exponentially exploding disease into one that 
dies out over time.

A key feature of the model deals with "R0," a popular parameter of 
most other models, which is defined as the average number of new 
infections caused by a recently infected person in a population of 
susceptible individuals.  An R0 greater than 1.0 leads to exponential 
increase in the number of cases.

However, because R0 is an average over the entire population, it does 
not reflect that fact that only a fraction of the population is 
responsible for the majority of new infections.  Averages can be 
misleading-for example, when a billionaire enters any establishment, 
on average everyone there instantly becomes at least a millionaire.

The researchers believe that splitting R0 into components, one for 
each level of activity or propensity to become infected, provides 
better policy guidance. In Larson's model, every population component 
is assigned different values for R0 , depending on factors such as 
that component's frequency of human contact and susceptibility to 
infection if exposed to the flu. Each of these factors can be at 
least partially controlled, suggesting that our individual and 
collective behaviors in response to the flu can greatly influence the 
numbers who become infected.

The researchers also found a striking difference in death toll 
depending on how early in the epidemic social distancing measures 
went into effect. For example, in a hypothetical population of 
100,000 susceptible individuals, 12,000 fewer people were infected if 
social distancing steps were taken on day 30 of an outbreak instead 
of day 33.  But intervention on Day 0 is best.

This finding is consistent with historical research reported in April 
by two research teams, one led by the National Institute of Allergy 
and Infectious Diseases and one from the United Kingdom, that 
demonstrated that those communities in 1918 that took aggressive 
social distancing actions early usually suffered less from the 
"Spanish Flu" than those who waited and debated.

The findings strongly suggest that influenza emergency plans should 
include measures to reduce social contact, such as encouraging people 
to work from home and avoid large gatherings, Larson said. This is 
especially important because it generally takes at least six months 
from the time of an outbreak to develop an effective vaccine. Those 
who must continue to work, such as doctors and other health care 
workers, should be the first to receive any available avian flu 
vaccine that might be developed, he said.

Larson says that large institutions like MIT, as well as state and 
local governments, should have emergency plans ready to put into 
action as soon as the first case of human-to-human H5N1 influenza is 
reported.

"We need to be aggressive. We need to be assertive. Don't 
dilly-dally, don't have a lot of political debate and foot-dragging," 
he said. "If people do take it seriously, the number of deaths could 
be greatly reduced.  A key is to start taking aggressive steps well 
before the flu is at your doorstep."

Larson became interested in modeling influenza after reading a book 
about the 1918 outbreak, which killed between 50 and 100 million 
people around the world. He had never heard much about the epidemic, 
which in the United States claimed more victims than World War I.

"Reading the history of it, I became fascinated," he said. "The 
wonderful thing about being in OR (operations research) is you can go 
into any problem you think is important and relevant and really 
contribute to it."

Larson said he hopes that other operations researchers will take up 
influenza research and develop more detailed models.

"Any mathematical model of the disease is bound to be incorrect," 
Larson wrote in the Operations Research paper. "But we are not 
seeking multidecimal accuracy, but rather insights on how to limit 
the spread of the disease. We firmly believe that fresh eyes from the 
OR community can play a significant role in this quest."

Other members of the MIT research team include undergraduate Kelley 
Bailey; Stan Finkelstein, senior research scientist in the 
Engineering Systems Division; Karima Robert Nigmatulina, graduate 
student in the Operations Research Center; Robert Rubin, faculty 
member at the Harvard-MIT Division of Health Sciences and Technology; 
and Katsunobu Sasanuma, a graduate student in the Engineering Systems 
Division and the Operations Research Center.

The research was funded in part by an IBM Faculty Research Award.

--MIT--

Written by Anne Trafton, MIT News Office