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Bats host a number of pathogens that cause severe disease and onward transmission in humans and domestic animals. Some of these pathogens, including henipaviruses and filoviruses, are considered a concern for future pandemics. There has been substantial effort to identify these viruses in bats. However, the reservoir hosts for Ebola virus are still unknown and henipaviruses are largely uncharacterized across their distribution. Identifying reservoir species is critical in understanding the viral ecology within these hosts and the conditions that lead to spillover. We collated surveillance data to identify taxonomic patterns in prevalence and seroprevalence and to assess sampling efforts across species. We systematically collected data on filovirus and henipavirus detections and used a machine-learning algorithm, phylofactorization, in order to search the bat phylogeny for cladistic patterns in filovirus and henipavirus infection, accounting for sampling efforts. Across sampled bat species, evidence for filovirus infection was widely dispersed across the sampled phylogeny. We found major gaps in filovirus sampling in bats, especially in Western Hemisphere species. Evidence for henipavirus infection was clustered within the Pteropodidae; however, no other clades have been as intensely sampled. The major predictor of filovirus and henipavirus exposure or infection was sampling effort. Based on these results, we recommend expanding surveillance for these pathogens across the bat phylogenetic tree.
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Using serosurveys to optimize surveillance for zoonotic pathogens
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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- Award ID(s):
- 2200140
- PAR ID:
- 10519196
- Publisher / Repository:
- bioRxiv
- Date Published:
- Format(s):
- Medium: X
- Institution:
- bioRxiv
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Posted Content:
- https://doi.org/10.1101/2024.02.22.581274
