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ABSTRACT Zoonotic pathogens pose a significant risk to human health, with spillover into human populations contributing to chronic disease, sporadic epidemics, and occasional pandemics. Despite the widely recognized burden of zoonotic spillover, our ability to identify which animal populations serve as primary reservoirs for these pathogens remains incomplete. This challenge is compounded when prevalence reaches detectable levels only at specific times of year. In these cases, statistical models designed to predict the timing of peak prevalence could guide field sampling for active infections. Here we develop a general model that leverages routinely collected serosurveillance data to optimize sampling for elusive pathogens. Using simulated data sets we show that our methodology reliably identifies times when pathogen prevalence is expected to peak. We then apply our method to two putativeEbolavirusreservoirs, straw-colored fruit bats (Eidolon helvum) and hammer-headed bats (Hypsignathus monstrosus) to predict when these species should be sampled to maximize the probability of detecting active infections. In addition to guiding future sampling of these species, our method yields predictions for the times of year that are most likely to produce future spillover events. The generality and simplicity of our methodology make it broadly applicable to a wide range of putative reservoir species where seasonal patterns of birth lead to predictable, but potentially short-lived, pulses of pathogen prevalence. AUTHOR SUMMARYMany deadly pathogens, such as Ebola, Lassa, and Nipah viruses, originate in wildlife and jump to human populations. When this occurs, human health is at risk. At the extreme, this can lead to pandemics such as the West African Ebola epidemic and the COVID-19 pandemic. Despite the widely recognized risk wildlife pathogens pose to humans, identifying host species that serve as primary reservoirs for many pathogens remains challenging. Ebola is a notable example of a pathogen with an unconfirmed wildlife reservoir. A key obstacle to confirming reservoir hosts is sampling animals with active infections. Often, disease prevalence fluctuates seasonally in wildlife populations and only reaches detectable levels at certain times of year. In these cases, statistical models designed to predict the timing of peak prevalence could guide efficient field sampling for active infections. Therefore, we have developed a general model that uses serological data to predict times of year when pathogen prevalence is likely to peak. We demonstrate with simulated data that our method produces reliable predictions, and then apply our method to two hypothesized reservoirs for Ebola virus, straw-colored fruit bats and hammer-headed bats. Our method can be broadly applied to a range of potential reservoir species where seasonal patterns of birth can lead to predictable pulses of peak pathogen prevalence. Overall, our method can guide future sampling of reservoir populations and can also be used to make predictions for times of year that future outbreaks in human populations are most likely to occur.
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This content will become publicly available on January 31, 2026
Wildlife disease surveillance from village to peak: Trypanosome infections of mammals on Sulawesi revealed higher prevalence in intact montane forests
Zoonotic diseases, including those carried by mammalian hosts, pose a significant threat to human health worldwide and substantial investment in wildlife disease surveillance is aimed at identifying the risk of spillover from wildlife to human populations where they interact. However, host species diversity is highest in the most intact habitats away from human habitation and most of the potential host species within these habitats are unsampled for infections. This is particularly true in biodiverse tropical ecosystems where the prevalence and identity of infections are the least known. We screened for presence of trypanosomes in 2,335 specimens from 66 species of rodents and shrews sampled from 11 mountain areas on the tropical island of Sulawesi, Indonesia. Our sampling spanned from the edge of human occupation into the most intact forests available on the island with sampling elevations ranging from 220 to 2,700 m. The two most common Trypanosoma species we detected were a native species from the Theileri clade (19.0 % of samples) and an introduced species from the Lewisi clade (5.1 % of murid rodent samples). Both species were detected at all elevations, extending from village edges to mountain peaks, but both reached their highest prevalence above 2,000 m elevation in the most intact forest away from human habitation. If these patterns with trypanosome infections are typical of other zoonotic diseases, wildlife disease surveillance would need to shift resources to study host-pathogen dynamics in more remote ecosystems. Sampling focused on the breadth of biodiversity, such as collected by and housed in natural history collections, is needed to further our understanding of zoonotic diseases and their prevalence.
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- PAR ID:
- 10596956
- Publisher / Repository:
- Asociación Mexicana de Mastozoología
- Date Published:
- Journal Name:
- Therya
- Volume:
- 16
- Issue:
- 1
- ISSN:
- 2007-3364
- Page Range / eLocation ID:
- 125 to 141
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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