Using Serosurveys to Optimize Surveillance for Zoonotic Pathogens

Researchers have developed a new mathematical approach that uses serological surveys, or antibody testing, to improve surveillance of zoonotic diseases in wildlife populations. The method helps identify when pathogen prevalence is most likely to peak in animal reservoirs, allowing scientists to focus field sampling efforts during periods when active infections are most likely to be detected. This is particularly valuable for diseases that occur seasonally and are difficult to find because infections may only be present at certain times of the year.

Using simulated data, the researchers demonstrated that the model can reliably predict periods of heightened infection risk. They also applied the method to surveillance data from straw-colored fruit bats in Cameroon, a species suspected of harboring filoviruses such as Ebola-related viruses, and identified a likely seasonal peak in infection prevalence.

The approach could improve wildlife disease monitoring, reduce surveillance costs, enhance detection of emerging pathogens, and help scientists better understand and prevent zoonotic spillover events into human populations.