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We Could've Stopped Ebola If We Listened to the Data

Part of an interactive graphic depicting Ebola infections over time.

Computational epidemiologists remoteness from the levers of power and those that pull them is one reason their warnings earlier this year about the spread of Ebola were not heeded.

Credit: Zach Wener-Fligner

Computational epidemiologists were some of the first to sound alarm bells about the potential for a dangerous Ebola outbreak in West Africa earlier this year, writes Caitlin Rivers, a Ph.D. candidate at the Virginia Polytechnic Institute and State University's (Virginia Tech) Network Dynamics and Simulation Science Laboratory.

Rivers says multiple computational models showed the potential for explosive growth, yet their warnings were not heeded. She says this is in part due to computational epidemiologists' remoteness from the levers of power and those that pull them.

Rivers also notes most computational epidemiologists work at universities and are more familiar with theoretical research than practical research. However, Rivers notes this is starting to change.

One team of computational epidemiologists at Virginia Tech partnered with the U.S. Department of Defense to plot out potential outbreaks and their trajectories. The researchers do this by overlaying existent outbreak data with other data such as census records, clinical data, and other contextual information, which yields information about how the outbreak may spread.

Other computational epidemiologists are focusing more on prediction, using similar techniques to determine which areas are likely to be vulnerable so the state can react and find ways to protect them. One example is Healthmap, which was founded in 2005 and draws on information from publicly available sources to detect potential outbreaks.

From Quartz
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