How Bats Could Help Scientists Stop Ebola Outbreaks

May 21, 2018

The current Ebola outbreak in the Democratic Republic of Congo has spread to at least 58 people and has killed nearly half of them, according to an update from the World Health Organization (WHO) today (May 23). This is the ninth outbreak of Ebola in the country since the deadly virus was first discovered in 1976 in a village near the Ebola River.

But what if scientists could predict Ebola outbreaks and stop them before they start?

That’s the goal of a group of researchers who hope to predict Ebola outbreaks before they begin by tracking the migration patterns of bats, one of the disease’s main hosts. The researchers detailed their work in a new study published  in the journal Scientific Reports.

“Traditionally, scientists studying the [spread] of diseases such as Ebola have operated under the assumption that diseases move in unison,” said study co-author Paul Bocchini, a professor of civil and environmental engineering at Lehigh University of Pennsylvania.” In reality, diseases transmitted by animal hosts depend on how those hosts migrate.”

Using satellite information, as well as data on infection, birth and mortality rates in bats, Bocchini and his fellow researchers developed a model that tracks the migration patterns of African mammals in pursuit of resources on the continent.

By “feeding the model” with this information plus data on the availability of food and shelter for the bats, the model was able to “accurately predict outbreak hotspots corresponding to bat migration during the 2014 Ebola outbreak,” Bocchini told Live Science. (The 2014 outbreak occurred in West Africa and claimed the lives of more than 11,000 people.)

In other words, the researchers used their models to retroactively speculate where the 2014 Ebola outbreak in Africa should have occurred, based on a map of bat movements. And as it turns out, those models were correct. The epidemic did occur in the hotspots it predicted.

For example, the model retroactively predicted the 2014 peak of Ebola-infected bats in the remote Guinean village of Meliandou. In fact, when the researchers analyzed Ebola reports from the village over the same time period, they found that the peak they predicted coincided with the month the epidemic began.

Now, “our goal is to use this model to predict future Ebola outbreaks,” says Bocchini.” If you know where the risk is highest in a given time period, you can allocate resources specifically to those hotspots.”

Resources include vaccines, public health campaigns, and even doctors – but those resources are always limited, Bocchini adds.

Other experts agree that such predictive models could be helpful, especially when combined with other disease control methods.

“Contact tracing,” or modeling that identifies and potentially treats individuals who come into contact with infected individuals, has so far been the primary system for Ebola control, said Cameron Browne, an assistant professor of applied mathematics at the University of Louisiana-Lafayette who was not involved in the new study.Browne’s research focuses on mathematical modeling of infectious diseases.

“Identifying possible spillover effects from bats to humans is absolutely important,” Browne told Live Science. “Spillover” refers to the spread of a disease from one species to another; by tracking infected bats, the new study’s model helps predict areas where spillover is more likely to occur.

“However, once a hotspot is identified, there still needs to be a control strategy,” says Browne.” Ultimately, surveillance through modeling is the key to disease control – whether it involves contact tracing or identifying animals that could lead to an outbreak.”

Bocchini and his fellow researchers have received funding from the National Institutes of Health to continue their work. They hope to make their model available to all countries and plan to “apply the technique to more recent and potential future outbreaks,” he said.

“We think this modeling approach could even be applied to other diseases,” said Bocinni.” In the Americas, the model could even predict outbreaks of diseases like Zika.” More research is needed in this area, though.