Mailman School of Public Health researchers are forecasting the spread of Ebola in West Africa in an effort to help medical professionals better treat patients and contain the disease.

Environmental health sciences professor Jeffrey Shaman developed a computer model last month to organize the limited amount of available data about the spread of Ebola in order to determine practical ways to deal with the virus.

Right now, the model is able to forecast the cumulative infection rate and mortality rate up to six weeks in the future. Computed forecasts are published online on a weekly basis to help medical personnel decide how many beds, nurses, and medical personnel will be needed in a given week.

"It's a very dangerous disease—it is very destabilizing. It has completely eroded whatever public health infrastructure, medical infrastructure there were in those countries," Shaman said last week. "Many of the nurses and doctors are dead—they died of Ebola. So you don't have people who are capable of taking care of anyone."

With medical personnel and other caregivers dying from Ebola in affected regions of West Africa, making sense of the data beforehand is key to containing the epidemic, according to Shaman.

Shaman noted that Ebola's high fatality rate—according to the World Health Organization's Nov. 21 situation report, there have been 5,459 reported Ebola deaths in eight countries—overwhelms the medical professionals in these countries. Considering the epidemiological history of West Africa, where diseases like meningitis and tuberculosis are not uncommon, the impact of Ebola on the medical system becomes even more important.

"All they are doing is Ebola, all Ebola all the time," Shaman said.

[Related: Columbia Med Center doctor tests positive for Ebola after returning from West Africa, city officials confirm]

Wan Yang, an associate research scientist who helped Shaman develop the model, said that the benefits of forecasting using models include being able to understand the dynamics of Ebola transmission and planning intervention measures.

Shaman's model is a form of the compartmental model, known in the field of epidemiology for its simplicity.

"It is parsimonious, which means it doesn't have a lot of bells and whistles on it," Shaman said. "We understand what's going on a lot more easily because of that."

The compartmental model divides the population into several sectors: individuals who are susceptible to the pathogen, others who are being affected by it, and others who have recovered from it.

With statistical algorithms, a simulation method uses data supplied by the World Health Organization to calculate factors such as the length of the incubation period.

Knowing the incubation period—the time during which an infected person does not show symptoms and is not infectious—is instrumental to understanding the transmission of Ebola, Shaman said.

According to Yang, the model has two main strengths: First, it emulates the variability inherent in the real world by accommodating for three varying potential scenarios, producing a separate forecast for "improved," "no change," and "degraded" cases.

[Related: Columbia Engineering, Mailman faculty and students team up to look for Ebola solutions]

"Second, we simultaneously use three data streams—incidence, mortality, and case-fatality rate—in the inference and forecasts," she said.

But these advantages do come at a cost. While the model's simplicity makes the data easier to understand, it is "a little dubious in terms of its veracity," Shaman said.

As global attention converges on the epidemic, the Centers for Disease Control and the World Health Organization have also generated their own models for forecasting the spread of Ebola. The complexity of these other models, however, also has its drawbacks.

"The trade-off is that if you go with a complex model to describe a system, generally people are making it up from whole cloth," Shaman said. "You are making up the processes. You are making up what [individuals] are doing all the time."

Instead of imposing unknown parameters onto the model, Shaman's model takes what he describes as an "agnostic" position, letting the data speak for itself.

Shaman said they focused on Ebola because of the urgency around the disease.

"It was here, it was timely, and it was a crisis," Shaman said. "But there is a limit that we can do, given how little we understand the disease and where we sit right now."

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