A big-picture look at the world’s worst Ebola epidemic: West Africa, 2013-2016

areas where public health resources and even basic infrastructure are limited, real-time genome sequencing and analysis can speed the response by telling health officials where to initiate contact tracing, add hospital beds, quarantine those infected and implement other infection controls.

It can also provide information unavailable by other methods. In earlier studies, for example, genome sequencing had confirmed that Ebola deaths in Sierra Leone and Liberia came from Guinea and were not a new introduction from the virus’ natural reservoir.

“Genome sequencing can tell you epidemiologically relevant things that are unobtainable by traditional methods,” Bedford said.

And synthesizing that information with data on population size, travel distances, geography, language and other factors can provide context for which factors influenced the epidemic’s spread and duration — and where to target treatment and interventions.

Technology — and data-sharing — advances
Virus genome analysis has played a bigger role in understanding the West African Ebola epidemic than for any other infectious disease outbreak for two reasons: modern advances in sequencing technologies and scientists who were unusually willing to share data.

So-called “next-generation” sequencing equipment have dramatically lowered both the costs and time to prepare samples and do sequencing, making it much easier to sequence an entire viral genome.

And scientists relatively early on in the epidemic decided to share viral genome sequences they were collecting from patients rather than waiting until they published a research paper. Publications are considered the currency of science, but the admonition to “publish or perish” took on a new meaning in the midst of such a devastating outbreak.

“The old model where you hold onto the sequences until the publication comes out, which as any academic knows is going to be months, is morally wrong if those sequences can be used to affect a response on the ground,” Dudas said.

Early posting of data on the public database GenBank led to a surge of collaboration from experts in diverse fields. It was when one of those early researchers, Harvard University’s Dr. Pardis C. Sabeti (another author on the new paper) and her team sequenced 99 Ebola genomes from patients in Sierra Leone and uploaded their data that Rambaut became involved. Bedford had been a postdoc in Rambaut’s lab before coming to the Hutch in 2013, six months before the Ebola epidemic started. Dudas, who is now a postdoc in Bedford’s lab, did his doctorate research under Rambaut.

Analysts like Dudas, Rambaut and Bedford bring to data