With the Ebola outbreak spreading and no end to the epidemic in sight, Jeffrey Shaman, associate professor of environmental health sciences at Columbia University's Mailman School of Public Health, has been extremely busy in recent weeks.

Shaman, whose influenza prediction tool won the Centers for Disease Control and Prevention's Predict the Influenza Season Challenge in June, is not only gearing up for the upcoming flu season but has also been called into action in trying to model the worsening Ebola virus crisis in West Africa—a task Shaman says may have gone beyond the capabilities of tried and true surveillance methods.

"I'm spending a tremendous amount of my time on it," Shaman told Health Data Management. "I'm part of a group of modelers who are funded through the National Institutes of Health and in times of emergency we are asked to help out with things and provide model support for understanding. And, in the case of this disease, because there is such limited information about how it has behaved in the past, and because observations of what is going on right now are not great necessarily, models are really needed to try to fill in those gaps and make sense of it all.

"It was a rare disease that took place in central Africa in very isolated settings, and the process of isolation, containment, and contact tracing was sufficient to snuff out these outbreaks in the past," Shaman continued, "but in the current situation, all bets are off. Contact tracing may not even work at this point because they are too overwhelmed. It's in the cities, it's in places where people are too interconnected, and given that, will you be actually able to snuff it out?"

The only way to combat the Ebola crisis, Shaman said, is through a massive investment of resources in the affected areas, and perhaps scientists will be able to draw some empirical observations in retrospect. Given that the epidemic has outstripped the ability of healthcare providers to put sick patients in hospital beds, developing any sort of predictive model on the fly will be a long shot.

"It is a highly virulent disease with a tremendous mortality rate, and within the cities there are too many people who may have the disease with whom others come in contact, and too much disarray in government services and healthcare services to actually keep tabs on everybody who is infectious," he said. "There haven't been enough beds to handle them, so they've been in the broader community, not isolated, and it's a very, very dire situation.

"Given the fact this is in a territory we haven't experienced and have no observational records--and we don't know what the end game of this is--it's very open-ended and very disconcerting. How we respond to it has to be with a massive influx of resources and effort, but the optimal way isn't going to be known until it's over, unfortunately. Maybe we'll be able to see it retrospectively, but it's very difficult to assess."

The latest Shaman group Ebola forecasts run through Nov. 30, and predict a steepening curve of both infection and mortality rates; Shaman said the group's U.S. flu forecast for the 2014-15 season has yet to begin, though his researchers are starting to gear up for it. And, in contrast to the paucity of data and the uncertainty of how to use it in the Ebola crisis, he said the conversation about using the ever-increasing number of flu data vectors is becoming more detailed.

Whereas the early days of crowdsourcing data for flu were marked by skepticism by epidemiologists, Shaman said scientists and policy makers alike now accept that supplemental data can offer valuable additional insights to traditional surveillance activities. His group's website, he said, makes use of many sources of data, including sanctioned surveillance data, nowcasted feeds derived from surveillance, crowdsourcing, Twitter, and Google Flu Trends.

"We can do it off mixes and matches, or new measures of flu, which we in fact do," he said. "In fact, that's what's on the website, a combination of Google Flu Trends and WHO/NRVESS data on positive rates. In that sense, we can run it off of anything and generate long lead predictions."

Shaman said his group held a workshop in May to brainstorm with CDC officials as well as epidemiologists from numerous jurisdictional levels to begin building a sense of community around what data might work best to help predict flu season vectors, and how granular to go in data feeds.

"I think it’s the same kind of thing weather forecasts went through 30, 40, 50 years ago," he said. "They have a long track record of people developing a familiarity and understanding of what it can and can't do. So what we're doing with infectious disease forecasts is the new kid on the block. How that information is actually being integrated in decision-making is an open question. We've started working on that and we hope to have some pilot projects where we work with social scientists, decision scientists and even social anthropologists who look at how people make decisions in the face of uncertainty.”

Register or login for access to this item and much more

All Health Data Management content is archived after seven days.

Community members receive:
  • All recent and archived articles
  • Conference offers and updates
  • A full menu of enewsletter options
  • Web seminars, white papers, ebooks

Don't have an account? Register for Free Unlimited Access