The recent Ebola epidemic in West Africa was the most deadly outbreak of the disease ever recorded, infecting 28,000 people and killing 11,000.
Now a new collaborative study has tracked how that epidemic spread by looking at genetic data of the virus collected in real time. The study was led by computational biologist and Fred Hutchinson Cancer Research Center fellow Dr. Gytis Dudas and published today in the journal Nature.
The researchers say sequencing the genome of viruses like Ebola in real time could prevent epidemics on the scale of the one in West Africa, possibly saving thousands of lives during an outbreak of disease.
The study was authored by 96 scientists from 60 institutions in 18 countries, many of whom worked as clinicians and geneticists in the field during the Ebola outbreak. The data they collected on the virus’s genome showed how it was evolving and spreading, helping health organizations manage the epidemic.
“There are still some people who think that genome sequencing is effectively stamp collecting,” Dudas said in a press release. “You might collect samples, and you might sequence them and look in retrospect at the outbreak. But all the sequencing that’s been done leading up to this publication was essentially being done in real time. And each analysis was then used to go back to the field and make decisions. It’s a way to understand what’s driving an epidemic.”
The video below shows a time-lapse of how the Ebola virus spread across West Africa, based on the data collected during the study.
By collecting data from a huge number of on-the-ground scientists, Dudas and his fellow researchers were able to see how that real-time data shaped the response to the epidemic, and what else could have been done.
“We calculated that 3.6 percent of cases traveled, basically meaning that if you were able to focus on those mobile cases and reduce their mobility, you might have had a disproportionate effect on the epidemic,” Dudas said.
So if scientists were able to share their data across regions and organizations, and if health authorities could respond to it quickly enough, the epidemic could have been contained even more than it was.
The study and its results speak to the benefits of sharing data, particularly during public health crises. That goes against traditional practice in academic and other research institutions, where the emphasis on publishing original work means researchers are often dissuaded from sharing their data.
But if real-time data sharing and collaboration was embraced, it could have a huge impact on managing diseases like Ebola and preventing future epidemics.