Can big data help with global pandemics?
Last month, billionaire philanthropist Bill Gates spoke at a security conference in Munich, Germany, on a topic that he and his foundation have been hard at work addressing for years — infectious diseases. And while Gates’ overall tone was one of optimism concerning humanity’s ability to combat the spread of viruses such as Zika and Ebola, Gates also did not mince words about the growing threat of a major pandemic.
“Whether it occurs by a quirk of nature or at the hand of a terrorist, epidemiologists say a fast-moving airborne pathogen could kill more than 30 million people in less than a year,” Gates said. “And they say there is a reasonable probability the world will experience such an outbreak in the next 10 to 15 years.”
One could be forgiven for thinking such talk about global health calamities to be mere speculation, or that the seemingly constant stream of news reports on viral outbreaks — from the Asian bird flu to killer tick-borne viruses to the Nipah virus of Malaysia — are no more than cheap fear-mongering.
But the truth is that more infectious diseases have been emerging in recent decades. Research has shown that the number of new diseases over the past century has almost quadrupled, all thanks to the expansion of human development into previously wild regions around the globe. Speaking to NPR, disease ecologist Barbara Han of the Cary Institute of Ecosystem Studies in New York likens the deforestation occurring in places like the Amazon and Malaysia’s Borneo rain forest to puncturing a balloon filled with viruses. “Whatever survives, spills out. Deforestation is closely tied to disease emergence,” says Han.
The US global surveillance for pathogens project called PREDICT, led by researchers at UC Davis School of Veterinary Medicine, has so far reportedly identified more than 1,000 new viruses from over 20 countries, pathogens that have in many cases been around for thousands of years in other animals but have yet to make the jump to humans.
The challenge is not just in predicting where and when the next viral pandemic may emerge but in determining the best method for controlling its further spread. That’s where computing science professor at Simon Fraser University in Burnaby, BC, Leonid Chindelevitch sees value in mathematical modelling, which he says can help predict how pathogens go through genetic mutations that lead to outbreaks, how viruses and bacteria develop resistance to drugs and what might be the best methods to stop them.
Calling it an “arms race” with infectious diseases, Chindelevitch says that modelling different approaches to dealing with newly mutated pathogens may be our best hope. “It’s very hard to predict which of the many viruses and bacteria that we have today will be the next big threat,” says Chindelevitch in an SFU news release. “We didn’t predict SARS, avian flu, Ebola and now Zika; they’re all surprises. That’s one of the big challenges in the field—how do we know when the next big threat is going to come?”
Chindelevitch believes that the best strategy may turn out to be one where instead of trying to eradicate a particular pathogen, scientists try to direct the organism’s genetic evolution so as to turn it into something more benign.
“I think we live at a time where the positive development is that we have more and more data on these genomes and better and better ability to make sense of this data using the types of methods that my colleagues and I develop,” he says.