Biology subjects
Publications and source records attributed to Crowley, D. E..
We explore the role of Australian black flying foxes (Pteropus alecto) as reservoir hosts of potentially zoonotic Borrelia bacteria. Across six sites, 2% of 840 bats were infected with one of two novel Borrelia haplotypes. Phylogenetic reconstruction indicated these infections are distinct from Lyme or relapsing fever clades.
Sampling reservoir hosts over time and space is critical to detect epizootics, predict spillover, and design interventions. However, because sampling is logistically difficult and expensive, researchers rarely perform spatiotemporal sampling of many reservoir hosts. Bats are reservoirs of many virulent zoonotic pathogens such as filoviruses and henipaviruses, yet the highly mobile nature of these animals has limited optimal sampling of bat populations. To quantify the frequency of temporal sampling and to characterize the geographic scope of bat virus research, we here collated data on filovirus and henipavirus prevalence and seroprevalence in wild bats. We used a phylogenetically controlled meta-analysis to next assess temporal and spatial variation in bat virus detection estimates. Our analysis shows that only one in four bat virus studies report data longitudinally, that sampling efforts cluster geographically (e.g., filovirus data are available across much of Africa and Asia but are absent from Latin America and Oceania), and that sampling designs and reporting practices may affect some viral detection estimates (e.g., filovirus seroprevalence). Within the limited number of longitudinal bat virus studies, we observed high heterogeneity in viral detection estimates that in turn reflected both spatial and temporal variation. This suggests that spatiotemporal sampling designs are essential to understand how zoonotic viruses are maintained and spread within and across wild bat populations, which in turn could help predict and preempt risks of zoonotic viral spillover.
A series of logical events must occur for a pathogen to spill over from animals to people. The pathogen must be present in an animal reservoir, it must be shed from the reservoir into the environment or be transferred from the reservoir to a vector, it must persist in the environment or vector until contact with a human or amplifier host, and it must successfully enter, colonize, and reproduce within the human. These events each represent a barrier the pathogen must cross to successfully infect a human. Percolation models of pathogens completing the series of barriers or logical events can connect models of spillover risk with standard tools for statistical inference.\n\nHere, we develop percolation-based models of spillover risk and a theoretical framework for managing spillover as an inextricably multilevel process. Through analysis and simulation, we show that estimated associations between level-specific covariates and spillover events will err towards associations from dominant pathway to spillover, a potential problem if there are alternative pathways to spillover with different associations with covariates. Furthermore, estimated associations between covariates and spillover will better reflect associations between covariates and success probabilities of bottleneck events with the highest pathogen attrition rates in the data observed. If one agrees with a percolation model for spillover, then GLMs should not be used to estimate relative importance of various levels. We recommend always using nonlinear models for predicting spillover risk with quantitative covariates and discuss why switching regression models may be well suited for avoiding some obvious pitfalls in predicting spillover from alternative pathways or wildlife reservoirs. Finally, we demonstrate how percolation models formalize an intuitive management paradigm for mitigating risk in the inherently multilevel process of pathogen spillover.
Predicting which novel microorganisms may spill over from animals to humans has become a major priority in infectious disease biology. However, there are few tools to help assess the zoonotic potential of the enormous number of potential pathogens, the majority of which are undiscovered or unclassified and may be unlikely to infect or cause disease in humans. We adapt a new biological machine learning technique - phylofactorization - to partition viruses into clades based on their non-human host range and whether or not there exist evidence they have infected humans. Our cladistic analyses identify clades of viruses with common within-clade patterns - unusually high or low propensity for spillover. Phylofactorization by spillover yields many clades of viruses containing few to no representatives that have spilled over to humans, including the families Papillomaviridae and Herpesviridae, and the genus Parvovirus. Removal of these non-zoonotic clades from previous trait-based analyses changed the relative significance of traits determining spillover due to strong associations of traits with non-zoonotic clades. Phylofactorization by host breadth yielded clades with unusually high host breadth, including the family Togaviridae. We identify putative life-history traits differentiating clades host breadth and propensities for zoonosis, and discuss how these results can prioritize sequencing-based surveillance of emerging infectious diseases.