NIH grant supports statistical assessment of the deadly disease’s spread

Ebola is a zoonosis—a viral disease transmitted from animals to humans. Once symptoms appear after direct contact with infected body fluids, the risk of death is between 25 and 90 percent. Patients typically succumb to low blood pressure due to internal and external bleeding.

The carriers of the Ebola virus are believed to be fruit bats, which are not affected by the disease. The bats are known as reservoirs, meaning they naturally harbor disease-causing organisms and serve as potential sources of disease outbreaks, for more than 60 zoonoses, including rabies, SARS, and Ebola.

Though bats are essential members of the global ecosystem, they are also especially adept at harboring and spreading disease. To make matters worse, Ebola outbreaks are intermittent, and little is known about when, where or how the next one will occur.

To better predict Ebola outbreaks and contain them before they spread, two Lehigh University researchers are developing a forecasting tool to estimate the risk.

Javier Buceta and Paolo BocchiniThe project applies computational analysis to predict the spread of disease, which in turn will facilitate the preemptive deployment of resources as well as the application of focused mitigation plans.

Javier Buceta (pictured, left), associate professor of bioengineering and of chemical and biomolecular engineering, together with Paolo Bocchini (right), associate professor of civil and environmental engineering, are the principal investigators of the project, which is supported by the National Institute of Health.

"Bat migration patterns are affected by complex factors, including temperatures and weather patterns," Bocchini says. "Knowing the probabilities of how, and in what direction, an outbreak will occur can allow officials to rapidly direct doctors and supplies, as well as apply proper prevention and mitigation strategies."

The risk of an Ebola outbreak goes well beyond humans contracting the disease.

"The Ebola virus decimates the great ape population, which poses a conservation hazard," Bocchini continues. "Ebola represents a major threat worldwide through the potential global spread of infections, so an outbreak can have dramatic humanitarian as well as economic consequences."

The study aims to manage the uncertainty associated with a prediction by integrating a broad set of factors, encompassing tools from computational epidemiology, engineering, data science and uncertainty quantification. The team is studying bat migratory patterns due to environmental pressures, and also plan to consider socioeconomic, cultural and demographic factors to better understand the risk of an outbreak.

"To understand the ecology of the zoonotic niche, we are developing compartmental epidemiology models that include resource dynamics, variability, climate change and bat mobility," Buceta says. "Our model will be calibrated with factual satellite data by means of different regression techniques. Lehigh’s NGO (Non-Governmental Organization) status through the United Nations has played a major role in helping us secure an agreement with Nigeria and its Center for Public Health as a case study partner."