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Taking the gamble out of Ebola outbreaks


Left to right: Javier Buceta and Paolo Bocchini, Lehigh University

 

Lehigh research pair awarded NIH grant to assess spread of disease

Roll a typical 6-sided die and the chance that it will land on any given side is one in six. With a pair of dice, the probability of rolling an ordered pair is one in 36.

When it comes to predicting Ebola outbreaks, we are not so lucky.

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 highly contagious virus first appeared in 1976, when it infected 600 people in two separate outbreaks in the tropical regions of sub-Saharan Africa. One of the outbreaks was near the Ebola River, after which the virus is named.

Fast forward to 2014, and the disease had spread to Guinea, Liberia and Sierra Leone where it was responsible for the deaths of more than 11,000 people over a 2-year period.

The carriers of the Ebola virus are believed to be fruit bats, which are not affected by the disease. In that respect, the bats are known as reservoirs, meaning they naturally harbor disease-causing organisms and serve as potential sources of disease outbreaks.

Bats, our only flying mammal, are reservoirs for more than 60 zoonoses -- including rabies and SARS as well as Ebola. Though bats are essential members of the global ecosystem, where they assist in seed dispersal, pollination and nocturnal predation of an enormous number of insects, 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 were awarded a grant in April 2018 from the National Institutes of Health (NIH) to develop a forecasting tool to estimate the risk.

The grant, "Risk Assessment of Ebola Outbreaks through Probabilistic Modeling of Chiroptera Zoonotic Dynamics and Socioeconomic Factors," will apply computational analysis to project 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, associate professor of bioengineering and chemical and biomolecular engineering, together with Paolo Bocchini, assistant professor of civil and environmental engineering, are the principal investigators of the project.

"Bat migration patterns are affected by complex factors, including temperatures and weather patterns," Bocchini said. "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 continued. "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.

"We are studying the bat's migratory pattern due to environmental pressures, and we also plan to consider socioeconomic, cultural and demographic factors of the population to better understand what determines the risk of an outbreak," Buceta said.

Their methodology encompasses tools from computational epidemiology, engineering, data science and uncertainty quantification.

"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 continued. "Our model will be calibrated with factual satellite data by means of different regression models."

Nigeria will be used as a case study. "We have leveraged as much as possible the fact that Lehigh has UN-NGO status to obtained support from the president of Nigeria’s Center for Public Health," Buceta said.

Non-Governmental Organizations (NGOs) partner with the United Nations by contributing valuable information and ideas, advocating for positive change and providing essential operational capacity during emergencies.

The end result of the research will be a tool that predicts the probability of Ebola outbreaks and the dynamics of the zoonotic niche. The team has named the tool PAREO (Predictive Analysis of the Risk of Ebola Outbreaks), at http://probabilisticmodeling.org/pareo.

"Our ultimate goal is to shift the current research paradigm in the context of Ebola to evaluate, probabilistically, the risk of hemorrhagic fever spreading in humans," Bocchini said.

Buceta added, "This project will be the culmination of an interdisciplinary collaboration among our teams, spurred from the Probabilistic Modeling Group we coordinate at Lehigh."

The three-year NIH grant runs through March 2021.

 

- Mary Anne Lynch '16G

May 1, 2018

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