Forecasting Flu Pandemics Hinges on Insights into the Virus
Scientists have made strides in predicting how influenza outbreaks will spread, but now the pressure is on for a breakthrough way to model how deadly new strains form.
Influenza is a crafty opponent. Just when researchers think they might know where it’s headed next, it mutates. New strains form constantly, allowing the virus to evade detection by the human immune system, and these new strains can turn into pandemics with little to no warning. In 1918, the H1N1 subtype caused the most serious pandemic to date, killing 50 million people worldwide. That disaster was followed by the H2N2 pandemic in 1957, the H3N2 in 1968, and the resurgence last year of H1N1, now also known as swine flu.
To predict what the next dangerous strain will look like, researchers are trying to model the biology and evolution of the virus in more sophisticated ways. “If I was to make a prediction, I’d say that the H2 strain of the virus could cause the next pandemic,” says Klaus Stohr, director of influenza vaccine franchises at Novartis and former head of the World Health Organization’s global influenza program. But he admits that predictions like his are just educated guesses. Above all, “we need better models that could genetically predict which subtype will cause the next pandemic,” he says. “That would be a real breakthrough. That, in my view, could be a Nobel Prize-winning discovery.”
In contrast to pandemic flu, seasonal flu occurs with predictable regularity and is largely controllable. Six months before the beginning of the flu season in each hemisphere, the World Health Organization examines circulating strains from the previous year and determines which six are mostly likely to be a problem. Those are the strains that appear in the flu vaccines distributed to physicians’ offices each year, and they’re usually spot on. But when the virus mutates in ways scientists don’t anticipate—when it picks up a gene from one of the strains that infect birds or pigs, or when it manages to hop straight from livestock to humans—the result is a type of influenza against which humans have limited immunity. That has the potential to attack hard and spread fast.
A severe flu pandemic could result in as much as a 5.5 percent drop in the U.S. gross domestic product, amounting to a $683 billion loss to the country’s economy, according to a report by the Trust for America’s Health. And despite the unceasing work of labs around the world—the World Health Organization has more than 100 laboratories participating in its influenza network—there is not yet much that researchers or public health officials can do to predict a pandemic, let alone prevent one. “People are studying the virus and its genome all the time and want to get to those answers,” says Martin Meltzer, a health economist with the Centers for Disease Control and Prevention. “We don’t know enough about how the genome of the flu virus interacts with the human genome that it infects.”
While developing a model that predicts what the next pandemic virus will look like is proving difficult, “there’s been more progress in predicting how it will spread once it has started circulating in the human population,” says Cecile Viboud, an epidemiologist at the National Institutes of Health. “Modeling suggested very early on, for pandemic influenza, that it was not useful to close borders and air traffic, because by the time you detected the virus somewhere it had already spread to other places.” Modeling has also proved important for understanding who should be vaccinated, against both seasonal and pandemic strains. “If you vaccinate school kids, you reduce disease transmission in the entire community,” Viboud says.
So some researchers have been focusing their efforts on faster overall detection: If they can identify a new strain of the virus early enough, and if enough antiviral medication can be produced in time, there’s at least a chance of stemming a pandemic’s tide. One particularly promising area is search-engine and social-network data. This information flows in a constant torrent, and researchers can examine it for indications of disease hot spots. “We’ve developed algorithms that crawl the Web, looking for specific words of disease or warnings of disease,” says John Brownstein, an epidemiologist at Children’s Hospital in Boston. “It’s low-cost and highly geographically specific.”
In fact, a recent study by Brownstein and his colleagues showed that as Internet-based reporting of all diseases has increased, the time between a disease’s first occurrence and its discovery by public health officials has decreased at a rate of 7.3 percent a year. Early reporting systems can help pharmaceutical companies get the right vaccines to the right places in time to prevent widespread disaster.
But for influenza pandemics, early detection is sometimes not conclusive enough. Brownstein says that his system picked up a signal of unusual respiratory activity in Mexico when the H1N1 swine flu first erupted in 2009, but that it was just one of a number of unusual events occurring at the same time. “How do you differentiate [the strain in] Mexico from the others, and how do you know that’s the one that will turn into a global pandemic?”
To answer that, scientists are developing model after model of viral evolution and mutation, and they’re increasing their surveillance of the virus in wildlife and livestock, trying to keep up with the changes—or possibly get a step ahead. “We have an extraordinary amount of information on the evolution of influenza, with a large sample of genetic data. We know what the viruses look like going all the way back to 1918,” says Oliver Pybus, an evolutionary biologist at the University of Oxford. “If it were obvious and simple, we’d have found it by now.”