Bostonians’ collective memory of the year 1918 is inextricably tied to the Curse of the Bambino and the last Red Sox World Series victory of the 20th century.
But for MIT’s Richard Larson, Sox fan though he is, 1918 calls to mind a more significant event. What would become one of the worst plagues in modern history reached its East Coast peak that fall. Boston was the first urban center of the great influenza pandemic, and those infected in the city would spread the disease to the rest of the country and to Europe. In September alone, about 1,000 Bostonians died of the disease.
The influenza pandemic lasted through the next year. About 500 million people (nearly a quarter of the world’s population) became infected, resulting in as many as 700,000 deaths in the United States and at least 50 million worldwide. Influenza is normally much more lethal in the very young and very old, but for reasons that remain unclear, the 1918 strain claimed a disproportionate number of healthy young men and women. More Americans died in the epidemic than in all the wars of the 20th century combined.
A few milder influenza epidemics have occurred since then, including the 1968 Hong Kong flu, which kept Larson in bed for a full week. But many researchers fear that we are in for another big one. The U.S. Department of Health and Human Services estimates that an epidemic similar to the 1918 outbreak would kill almost two million Americans.
If such an epidemic strikes in the foreseeable future, we won’t be able to stop it. As far as modern medicine has come since 1918, it still takes longer to develop a vaccine than it would for a new strain of the flu to sweep the globe. If the virus were as bad as 1918’s, Larson says, the death toll could surpass 200 million worldwide.
But Larson also has a hopeful message. The course of a pandemic is not inevitable, he says, and individuals, families, employers, and governments can all do things to contain the spread of the disease. Larson believes that traditional epidemiological models have painted an overly bleak picture, leading to feelings of helplessness among the public and a lack of good planning by the government. “The science that informs the policy treats the flu’s spread as a fait accompli,” he says. But he begs to differ.
Larson, a professor of engineering systems, is in the midst of a two-year project to mathematically model how behavioral changes might contain a future pandemic. As part of that work, he is evaluating state governments’ preparedness plans and recommending ways to improve them. In January, his research team will host an on-campus event where it will provide feedback to representatives from about half the states and several universities. The researchers will also present a “master plan” consolidating ideas gleaned from the states’ plans and from their modeling research.
A New Model
Influenza epidemics happen when a strain of the virus adapted to other animals, such as chickens, mutates to become easily transmissible between humans. Public-health officials are nervously tracking H5N1, the virulent bird flu that has killed 200 people (mostly in Indonesia and Vietnam). Although it has passed between humans only rarely, no one can predict when or whether a particular virus will acquire the ability to do so efficiently. And because a virus that acquires that ability is by definition new to humans, it’s much harder to fight off than the seasonal flu, which at least resembles something our immune systems have seen before. Lack of established immunity to a new strain leaves us naked against its assault.
Quarantines and border controls won’t do any good: people newly infected with any flu are contagious for a full 24 hours before they show symptoms. As epidemiologists put it, these people are “shedding” the virus with every breath. The science may change in the coming years, says Larson, but today it takes six months to develop and test a vaccine once a virus emerges. Six months is plenty of time for the virus to engulf the world. Although the secondary bacterial infections associated with influenza can now be treated with antibiotics, we’re nearly as vulnerable to the disease itself as we were in 1918. With a denser and more mobile population, we may be even more vulnerable.
Epidemiologists debate how often an epidemic as bad as the one that struck in 1918 is likely to occur. Some say it happens only once a millennium, others once a century–in which case our number is up. But epidemics are such complex systems that until one is under way, no one can predict much. It’s impossible to gauge the lethality of a virus before it emerges, and viruses are always mutating in unforeseeable ways. They tend to strike in waves, but the duration, timing, and location of those waves cannot be forecast. “Any serious researcher is humbled by the pre-flu limitations on what we know,” says Larson.
More important for Larson, human behavior is unpredictable. Most epidemiological models take into account neither the heterogeneity of the population nor the fact that people’s behaviors will change over the course of an epidemic. Larson calls this a “fundamental error.” His contribution to influenza modeling is to feed human variability into the equations. Because the flu has no inevitable course, encouraging people to change their behavior can slow its spread and reduce its toll.
Models don’t predict how many people a virus will kill–there are too many unknowns for that. Instead, they predict the speed at which an epidemic will spread through the population before burning out. One of the most important parameters in these models is Ro, the average number of other people who will be infected by each infected person in the early days of the pandemic. If Ro is greater than one, the disease initially spreads exponentially and then subsides, as most of the population considered in the model has been exposed. That is, according to most traditional theories, there’s no containing the spread of the disease: it must simply run its course.
“The idea in good currency,” Larson says, “is that Ro is a fixed constant.” Fixed-Ro equations, which epidemiologists initially adopted in the 1960s and ’70s, are based on concepts developed in Germany in the 19th century to predict population growth. (In the original equations, Ro was the reproductive constant, the average number of female babies a newborn female baby would have in her lifetime.) They provide “an accurate description of something that moves very slowly,” like human population growth, says Larson. But they do a poor job of predicting fast-moving phenomena such as the spread of a highly contagious respiratory virus. That’s why Larson treats Ro as a variable with separate components.