Campus News

UGA, Penn State model predicts end to Ebola epidemic in Liberia

The Ebola epidemic in Liberia likely could be eliminated by June if the current high rate of hospitalization and vigilance can be maintained, according to a new model developed by ecologists at UGA and Pennsylvania State University.

The model includes factors such as the location of infection and treatment, the development of hospital capacity and the adoption of safe burial practices and is “probably the first to include all those elements,” said John Drake, an associate professor in the UGA Odum School of Ecology who led the project. The study appeared in the open access journal PLOS Biology Jan. 13.

Drake said that the UGA model should be useful to public health officials as they continue to combat the Ebola epidemic because it offers both general insights and realistic forecasts, something few models are able to do.

During fall 2014, the authors ran the model for five different hospital capacity scenarios. For the worst case, with no further increase in hospital beds, the median projection was for 130,000 total cases through the end of 2014; for the best case-an increase of 1,400 more beds, for roughly 1,700 total or an 85 percent hospitalization rate-the median projection was 50,000 cases. After the authors updated it with more recent information collected through Dec. 1, the model projected that, if an 85 percent hospitalization rate can be achieved, the epidemic largely should be contained by June.

“That’s a realistic possibility but not a foregone conclusion,” Drake said. “What’s needed is to maintain the current level of vigilance and keep pressing forward as hard as we can.”

Epidemic modeling is an important tool that helps public health officials design, target and implement policies and procedures to control disease transmission, and several models of the 2014 Ebola epidemic already have been published. Many of these models, according to Drake, seek to estimate the disease’s reproductive number-the number of new cases that one infected individual can generate.

“This is useful because it says how far transmission must be reduced to contain the epidemic,” he said. “Our model does this too, but it does other stuff as well. It aims to be intermediate in complexity-it captures all the things we think to be most important and ignores the rest.”