New model predicts Ebola epidemic in Liberia could be ended by June

The Ebola epidemic in Liberia could likely 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 the University of Georgia and Pennsylvania State University. The model includes such factors as the location of infection and treatment, the development of hospital capacity, and the adoption of safe burial practices. The study appears in the open access journal PLOS Biology.

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 should be largely contained by June.

To build the model, the researchers started with information gleaned from earlier Ebola outbreaks. They included data about variables such as the numbers of patients hospitalized and healthcare workers infected, which allowed them to estimate the level of under-reporting; rates of transmission in hospitals, the community and from funerals; and the effectiveness of infection control measures.

Once they had a working model with plausible parameters, they fine-tuned it using data from the World Health Organization and the Liberia Ministry of Health that included information about new cases as well as changes in behavior and public health interventions during that time, such as the addition of roughly 300 hospital beds and the adoption of safer burial practices.

Plausible parameter sets use recorded data that falls within the range of possibilities generated by the model at least 500 times, meaning that the model “fits” the data closely. This keeps the model's projections in line with observed reality, making it particularly useful for investigating a wide range of realistic potential interventions and accounting for the impacts of human behavior on disease transmission.