USA TODAY International Edition
A weapon of war on disease: Math
Algorithms may help stop epidemics in their tracks
As the biologist prepared to enter a cave in Uganda, a village leader stopped her. Before they began searching for bats — a host for dozens of diseases, including severe acute respiratory syndrome (SARS) — they would need to talk to the dead, the leader said .
So, on a day in early 2013, researcher Amy Gilbert sat on the dirt floor of a thatched hut, surrounded by villagers, and together they asked the ancestors for permission to enter the cave.
Gilbert, who was working for the U.S. Centers for Disease Control and Prevention, found herself wondering about eye contact. Should she look up at the villagers, or down at the dirt? More important: What would happen if the spirits said no? Fortunately, they didn’t. While sitting in a séance may seem extreme, the data gathered by Gilbert and other researchers — everything from the size and frequency of bat litters to the levels of virus in their blood serum — is being used to build mathematical tools scientists hope will achieve a landmark in human health.
They want to predict an infectious outbreak before it happens.
“I think we can do it, though I don’t think we can do it now,” said John Drake, a professor of ecology at the University of Georgia.
“With every new outbreak, we get better and better because we learn lessons. But I think this is a very achievable goal.”
In a 2015 paper, “The Algorithm That’s Hunting Ebola,” disease ecologist Barbara Han wrote: “One day, I hope that biologists will forecast disease outbreaks in the same way meteorologists forecast the weather. With one major difference: A meteorologist can’t stop a storm front, but we may be able to prevent outbreaks.”
Biologists now monitor possible outbreak signs, including weather
patterns that could boost mosquito numbers in certain regions, or land-use changes that bring people into closer contact with animals carrying diseases capable of jumping to humans.
Already, mathematical models have predicted additional rodent species capable of passing diseases to humans. They also have suggested that a much larger swath of the U.S. than previously thought, as far north as Wisconsin, may be vulnerable to the Zika virus, a disease linked to severe birth defects.
Driving the effort to translate early signals in nature into accurate disease warnings are the powerful, problemsolving operations called algorithms, building blocks of the computer age.
But developing mathematical tools that actually predict an outbreak before it happens is no easy task, and some experts doubt it can be done.
Even confirming an epidemic has begun is challenging in the early stages. Scientists track hundreds of real-time disease reports and emerging health threats on the webpage HealthMap.
“But which is the one of the many thousands that we have to pay attention to and try to mitigate?” asked Marc Lipsitch, a professor of epidemiology at Harvard University. “There’s just a lot of chance and a lot of factors we don’t understand.”
When modelers do know the disease, they still face challenges trying to predict how it will spread.
“With Zika, 80% of people are completely asymptomatic. … We only see the tip of the iceberg with the people who have symptoms and go to the doctor,” said Alessandro Vespignani, director of the Network Science Institute at Northeastern University in Boston.
But Vespignani believes that is precisely why models are needed.
Those tip-of-the-iceberg observations begin to make more sense when mathematical tools include information drawn from past outbreaks. Such tools can help nations decide where to put money and staff, and even whether it makes sense to close their borders.
“We are at war against diseases,” Vespignani said. “The soldiers on the field are the doctors, nurses, health care workers. What we do is provide intelligence so they can anticipate the movements of the enemy.”
At least three multi-million dollar federal programs are fueling the development of predictive tools for diseases. A fourth, the Global Virome Project, seeks to prepare for the next pandemic by identifying 99% of the animal viruses that have zoonotic potential, or the possibility to spread to humans.
The task is massive. Organizers say it will take about $3.4 billion over 10 years.
To date, scientists have discovered about 4,400 viruses, but the actual figure is believed to be much larger; by one estimate the number of mammalian viruses alone is around 320,000.
Nowhere is the threat of animal diseases jumping to humans more acute — and the promise of modeling to help us prepare, more crucial — than with influenza. The 1918 Spanish Flu killed 20 million to 50 million people, many of them otherwise healthy young adults.
Swine flu caused the most recent pandemic in 2009, killing up to 203,000 people worldwide.
Bird flu could be the next. So far, human cases of avian influenza have proven relatively rare and difficult to transmit, but the mortality rate is 60%.
Jeffrey Shaman, director of the climate and health program at Columbia University, has developed methods of predicting seasonal influenza that can be applied to a pandemic. During the 2012-2013 season, he generated weekly flu predictions for 108 U.S. cities. Each week for each city, his team ran an ensemble of 300 simulations depicting different ways the flu season might progress. The simulations were compared with field estimates of the actual number of people with flu in each city.
Based on this comparison, an algorithm adjusted or optimized all 300 simulations. The optimized versions then generated forecasts for the rest of the flu season. As the season progressed, the simulations got closer to agreement with one another and produced more accurate forecasts.
Shaman compared the process to aiming a cannon and using the results of each shot to draw closer to the target.
The next year, 2013-’14, the CDC launched its first “Predict the Influenza Season Challenge.” Eleven groups entered; Shaman’s group won. The CDC has continued the contest and used some of the forecasts to aid decisionmaking at the agency.
Though all the work and the research spending may sound impressive, it reflects a less impressive truth: We spend far less trying to predict outbreaks than we do responding to them. In a single year, 2009, Congress approved more than $7.6 billion to fight the H1N1 pandemic.
“We’ve been plagued by diseases for as long as humans have been around,” Han said, “but we’ve never been able to truly forecast disease… We spend a lot of money fighting fires.”