PULLMAN, Wash. - A new predictive model developed at Washington State University could help scientists more efficiently identify the reservoirs of emerging zoonotic viruses and dangerous pathogens like Ebola that can spill over from animals into humans.
Confirming a reservoir species is critical to understanding and preventing those spillovers, but it requires detecting live virus in an actively infected animal. That can be a significant challenge, as infections are often rare, short-lived and fluctuate seasonally, reaching detectable levels only during brief windows each year.
The model, created by researchers in the WSU College of Veterinary Medicine's Paul G. Allen School for Global Health, relies on detailed information collected on suspected reservoir species - including serological data that can indicate previous infection and seasonal biological patterns such as birth cycles - to identify those times. The model was detailed in a study published in the journal EcoHealth.
"Identifying reservoir hosts is a major challenge across a lot of zoonotic diseases," said Erin Clancey, a quantitative biologist at WSU who led the creation of the model. "This approach gives us a way to narrow in on when we're most likely to find the virus in wildlife."
While the model can be applied to any zoonotic virus, Ebola was of specific interest to Clancey's collaborator, assistant professor Stephanie Seifert, who leads the Molecular Ecology of Zoonotic and Animal Pathogens lab in the Allen School where she studies the factors contributing to viral emergence and cross-species transmission.
Since the first known outbreak in people in 1976 in the Democratic Republic of Congo, the virus has periodically resurfaced, including during a 2014-16 epidemic in West Africa that infected more than 28,000 and killed more than 11,000. Central Africa is currently experiencing an outbreak of the Bundibugyo strain of Ebola that has been officially declared a Public Health Emergency of International Concern by the World Health Organization.
Despite decades of research, the virus' natural reservoir has yet to be confirmed, although several bat species are considered strong candidates.
"This is a virus that likely exists at very low levels in wildlife populations, and it's happening in one of the most biodiverse regions on Earth," Seifert said. "It's really like looking for a needle in a haystack."
The model was initially tested using simulated data, where the timing of infections was already known, and was shown to accurately predict those patterns. The researchers then applied it to previously published data from bat species suspected of harboring Ebola, including straw-colored fruit bats and hammer-headed bats, to identify specific windows when infections were most likely occurring.
By helping researchers focus on those narrow windows, the model could make fieldwork more efficient. Sampling wildlife is often expensive and logistically complex, particularly in remote regions where researchers must contend with limited access, challenging terrain and seasonal weather conditions.
"A major challenge is not just finding the right species, but knowing when to sample," Seifert said. "If you miss that window, you're unlikely to detect the virus."
In addition to guiding when to sample, the model could also help researchers better understand when spillover events are most likely to occur. By comparing predicted peaks in infection in wildlife populations with the timing of outbreaks in humans, scientists may be able to identify patterns that improve surveillance and response efforts.
"You can't spend an entire year camped out in a remote region waiting for the right moment - it's impractical and expensive," Clancey said. "With limited resources, this gives researchers a way to plan field seasons more strategically."
