Harvard Public Health The technology was in development long before COVID-19, but the pandemic spurred a surge of interest.
or a motley collection of artificial intelligence researchers and startups, the end of the COVID-19 pandemic is just the beginning. The pandemic provided would-be entrepreneurs an opportunity to prove that artificial intelligence technology can help flag emerging illnesses before they turn into full-fledged epidemics.
While researchers have been experimenting with the technology for some time, COVID-19 created a surge of interest. BlueDot, an outbreak intelligence startup based in Toronto, says its customer base has grown by 475 percent surge since 2020 when it sent an alert to clients about an emerging illness in Wuhan five days before the World Health Organization announced it was tracking a “cluster of pneumonia cases” in the area. The company’s founder and CEO, Kamran Khan, says it has added vaccine manufacturers, as well as government and private security companies, to its customer list.
At Boston Children’s Hospital, officials started using AI to support capacity planning after the pandemic started, says John Brownstein, the hospital’s innovation officer. And Epiwatch, an AI epidemic surveillance service based at UNSW Australia, recently received a more than $5 million grant from Vitalik Buterin, the co-founder of the cryptocurrency Ethereum.
“Traditional disease surveillance is done by health departments and will collect reports or notifications from hospitals, primary health care, and from laboratories,” explains Epiwatch’s founder, Raina MacIntyre. MacIntyre is also a public health physician and epidemiologist at UNSW Sydney. “By the time things land on the desk of a health department, usually the epidemic has already spread.”
Epidemic surveillance AI usually involves algorithms that mine through sources of data—news reports, social media posts, and flight data—that aren’t traditionally used in disease surveillance. AI models can automatically scour this data, looking for patterns and abnormalities that public health authorities might not otherwise be aware of. For example, Epiwatch uses an AI technology called natural language processing that scrapes the web for terminology that could be associated with the emergence of a new illness, like “syndrome” and “pneumonia.”
McIntyre says that while AI can’t entirely replace health department epidemic monitoring based on lab tests and reports from healthcare institutions, companies like Epiwatch can still provide critical early warning data to officials— and give them a hint of where to look for a potential outbreak.
Critically, these AI systems weren’t just built to track COVID-19. At Boston Children’s Hospital, researchers have run a “Health Map” for more than a decade, says Brownstein. An earlier version of the software was able to flag one of the first signs of H1N1 in Mexico back in 2009. Epiwatch, meanwhile, was inspired by the World Health Organization’s slow reaction to the emergence of Ebola in West Africa in 2014.