Browsing by Author "Peel, Alison J."

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Conditions affecting the timing and magnitude of Hendra virus shedding across pteropodid bat populations in Australia
(2017-09) Paez, David J.; Giles, John R.; McCallum, Hamish I.; Field, H.; Jordan, D.; Peel, Alison J.; Plowright, Raina K.
Understanding infection dynamics in animal hosts is fundamental to managing spillover and emergence of zoonotic infections. Hendra virus is endemic in Australian pteropodid bat populations and can be lethal to horses and humans. However, we know little about the factors driving Hendra virus prevalence in resevoir bat populations, making spillover difficult to predict. We use Hendra virus prevalence data collected from 13 000 pooled bat urine samples across space and time to determine if pulses of prevalence are periodic and synchronized across sites. We also test whether site-specific precipitation and temperature affect the amplitude of the largest annual prevalence pulses. We found little evidence for a periodic signal in Hendra virus prevalence. Although the largest amplitude pulses tended to occur over winter, pulses could also occur in other seasons. We found that Hendra virus prevalence was weakly synchronized across sites over short distances, suggesting that prevalence is driven by local-scale effects. Finally, we found that drier conditions in previous seasons and the abundance of Pteropus alecto were positively correlated with the peak annual values of Hendra virus prevalence. Our results suggest that in addition to seasonal effects, bat density and local climatic conditions interact to drive Hendra virus infection dynamics.
Ecology, evolution and spillover of coronaviruses from bats
(Springer Science and Business Media LLC, 2021-11) Ruiz-Aravena, Manuel; McKee, Clifton; Gamble, Amandine; Lunn, Tamika; Morris, Aaron; Snedden, Celine E.; Yinda, Claude Kwe; Port, Julia R.; Buchholz, David W.; Yeo, Yao Yu; Faust, Christina; Jax, Elinor; Dee, Lauren; Jones, Devin N.; Kessler, Maureen K.; Falvo, Caylee A.; Crowley, Daniel; Bharti, Nita; Brook, Cara E.; Aguilar, Hector C.; Peel, Alison J.; Restif, Olivier; Schountz, Tony; Parrish, Colin R.; Gurley, Emily S.; Lloyd-Smith, James O.; Hudson, Peter J.; Munster, Vincent J.; Plowright, Raina K.
In the past two decades, three coronaviruses with ancestral origins in bats have emerged and caused widespread outbreaks in humans, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since the first SARS epidemic in 2002–2003, the appreciation of bats as key hosts of zoonotic coronaviruses has advanced rapidly. More than 4,000 coronavirus sequences from 14 bat families have been identified, yet the true diversity of bat coronaviruses is probably much greater. Given that bats are the likely evolutionary source for several human coronaviruses, including strains that cause mild upper respiratory tract disease, their role in historic and future pandemics requires ongoing investigation. We review and integrate information on bat–coronavirus interactions at the molecular, tissue, host and population levels. We identify critical gaps in knowledge of bat coronaviruses, which relate to spillover and pandemic risk, including the pathways to zoonotic spillover, the infection dynamics within bat reservoir hosts, the role of prior adaptation in intermediate hosts for zoonotic transmission and the viral genotypes or traits that predict zoonotic capacity and pandemic potential. Filling these knowledge gaps may help prevent the next pandemic.
Estimating viral prevalence with data fusion for adaptive two‐phase pooled sampling
(Wiley, 2021-10) Hoegh, Andrew; Peel, Alison J.; Madden, Wyatt; Ruiz-Aravena, Manuel; Morris, Aaron; Washburne, Alex D.; Plowright, Raina K.
The COVID-19 pandemic has highlighted the importance of efficient sampling strategies and statistical methods for monitoring infection prevalence, both in humans and in reservoir hosts. Pooled testing can be an efficient tool for learning pathogen prevalence in a population. Typically, pooled testing requires a second- phase retesting procedure to identify infected individuals, but when the goal is solely to learn prevalence in a population, such as a reservoir host, there are more efficient methods for allocating the second- phase samples.2. To estimate pathogen prevalence in a population, this manuscript presents an approach for data fusion with two- phased testing of pooled samples that allows more efficient estimation of prevalence with less samples than traditional methods. The first phase uses pooled samples to estimate the population prevalence and inform efficient strategies for the second phase. To combine information from both phases, we introduce a Bayesian data fusion procedure that combines pooled samples with individual samples for joint inferences about the population prevalence.3. Data fusion procedures result in more efficient estimation of prevalence than traditional procedures that only use individual samples or a single phase of pooled sampling.4. The manuscript presents guidance on implementing the first- phase and second- phase sampling plans using data fusion. Such methods can be used to assess the risk of pathogen spillover from reservoir hosts to humans, or to track pathogens such as SARS-CoV-2 in populations.
Models of Eucalypt phenology predict bat population flux
(2016-10) Giles, John R; Plowright, Raina K.; Eby, Peggy; Peel, Alison J.; McCallum, Hamish I.
Fruit bats (Pteropodidae) have received increased attention after the recent emergence of notable viral pathogens of bat origin. Their vagility hinders data collection on abundance and distribution, which constrains modeling efforts and our understanding of bat ecology, viral dynamics, and spillover. We addressed this knowledge gap with models and data on the occurrence and abundance of nectarivorous fruit bat populations at 3 day roosts in southeast Queensland. We used environmental drivers of nectar production as predictors and explored relationships between bat abundance and virus spillover. Specifically, we developed several novel modeling tools motivated by complexities of fruit bat foraging ecology, including: (1) a dataset of spatial variables comprising Eucalypt-focused vegetation indices, cumulative precipitation, and temperature anomaly; (2) an algorithm that associated bat population response with spatial covariates in a spatially and temporally relevant way given our current understanding of bat foraging behavior; and (3) a thorough statistical learning approach to finding optimal covariate combinations. We identified covariates that classify fruit bat occupancy at each of our three study roosts with 86-93% accuracy. Negative binomial models explained 43-53% of the variation in observed abundance across roosts. Our models suggest that spatiotemporal heterogeneity in Eucalypt-based food resources could drive at least 50% of bat population behavior at the landscape scale. We found that 13 spillover events were observed within the foraging range of our study roosts, and they occurred during times when models predicted low population abundance. Our results suggest that, in southeast Queensland, spillover may not be driven by large aggregations of fruit bats attracted by nectar-based resources, but rather by behavior of smaller resident subpopulations. Our models and data integrated remote sensing and statistical learning to make inferences on bat ecology and disease dynamics. This work provides a foundation for further studies on landscape-scale population movement and spatiotemporal disease dynamics.
Optimizing noninvasive sampling of a zoonotic bat virus
(Wiley, 2021-08) Giles, John R.; Peel, Alison J.; Wells, Konstans; Plowright, Raina K.; McCallum, Hamish; Restif, Olivier
Outbreaks of infectious viruses resulting from spillover events from bats have brought much attention to bat- borne zoonoses, which has motivated increased ecological and epidemiological studies on bat populations. Field sampling methods often collect pooled samples of bat excreta from plastic sheets placed under-roosts. However, positive bias is introduced because multiple individuals may contribute to pooled samples, making studies of viral dynamics difficult. Here, we explore the general issue of bias in spatial sample pooling using Hendra virus in Australian bats as a case study. We assessed the accuracy of different under- roost sampling designs using generalized additive models and field data from individually captured bats and pooled urine samples. We then used theoretical simulation models of bat density and under- roost sampling to understand the mechanistic drivers of bias. The most commonly used sampling design estimated viral prevalence 3.2 times higher than individual- level data, with positive bias 5–7 times higher than other designs due to spatial autocorrelation among sampling sheets and clustering of bats in roosts. Simulation results indicate using a stratified random design to collect 30–40 pooled urine samples from 80 to 100 sheets, each with an area of 0.75–1 m2, and would allow estimation of true prevalence with minimum sampling bias and false negatives. These results show that widely used under- roost sampling techniques are highly sensitive to viral presence, but lack specificity, providing limited information regarding viral dynamics. Improved estimation of true prevalence can be attained with minor changes to existing designs such as reducing sheet size, increasing sheet number, and spreading sheets out within the roost area. Our findings provide insight into how spatial sample pooling is vulnerable to bias for a wide range of systems in disease ecology, where optimal sampling design is influenced by pathogen prevalence, host population density, and patterns of aggregation.
Pathogen spillover driven by rapid changes in bat ecology
(Springer Science and Business Media LLC, 2023-01) Eby, Peggy; Peel, Alison J.; Hoegh, Andrew; Madden, Wyatt; Giles, John R.; Hudson, Peter J.; Plowright, Raina K.
During recent decades, pathogens that originated in bats have become an increasing public health concern. A major challenge is to identify how those pathogens spill over into human populations to generate a pandemic threat1. Many correlational studies associate spillover with changes in land use or other anthropogenic stressors2,3, although the mechanisms underlying the observed correlations have not been identified4. One limitation is the lack of spatially and temporally explicit data on multiple spillovers, and on the connections among spillovers, reservoir host ecology and behaviour and viral dynamics. We present 25 years of data on land-use change, bat behaviour and spillover of Hendra virus from Pteropodid bats to horses in subtropical Australia. These data show that bats are responding to environmental change by persistently adopting behaviours that were previously transient responses to nutritional stress. Interactions between land-use change and climate now lead to persistent bat residency in agricultural areas, where periodic food shortages drive clusters of spillovers. Pulses of winter flowering of trees in remnant forests appeared to prevent spillover. We developed integrative Bayesian network models based on these phenomena that accurately predicted the presence or absence of clusters of spillovers in each of the 25 years. Our long-term study identifies the mechanistic connections between habitat loss, climate and increased spillover risk. It provides a framework for examining causes of bat virus spillover and for developing ecological countermeasures to prevent pandemics.
Persistent infections support maintenance of a coronavirus in a population of Australian bats (Myotis macropus)
(2017-07) Jeong, J.; Smith, C. S.; Peel, Alison J.; Plowright, Raina K.; Kerlin, D. H.; McBroom, J.; McCallum, Hamish I.
Understanding viral transmission dynamics within populations of reservoir hosts can facilitate greater knowledge of the spillover of emerging infectious diseases. While bat-borne viruses are of concern to public health, investigations into their dynamics have been limited by a lack of longitudinal data from individual bats. Here, we examine capture-mark-recapture (CMR) data from a species of Australian bat (Myotis macropus) infected with a putative novel Alphacoronavirus within a Bayesian framework. Then, we developed epidemic models to estimate the effect of persistently infectious individuals (which shed viruses for extensive periods) on the probability of viral maintenance within the study population. We found that the CMR data analysis supported grouping of infectious bats into persistently and transiently infectious bats. Maintenance of coronavirus within the study population was more likely in an epidemic model that included both persistently and transiently infectious bats, compared with the epidemic model with non-grouping of bats. These findings, using rare CMR data from longitudinal samples of individual bats, increase our understanding of transmission dynamics of bat viral infectious diseases.
Transmission or Within-Host Dynamics Driving Pulses of Zoonotic Viruses in Reservoir-Host Populations
(2016-08) Plowright, Raina K.; Peel, Alison J.; Streicker, Daniel G.; Gilbert, Amy T.; McCallum, Hamish I.; Wood, James; Baker, Michelle L.; Restif, Olivier
Progress in combatting zoonoses that emerge from wildlife is often constrained by limited knowledge of the biology of pathogens within reservoir hosts. We focus on the host-pathogen dynamics of four emerging viruses associated with bats: Hendra, Nipah, Ebola, and Marburg viruses. Spillover of bat infections to humans and domestic animals often coincides with pulses of viral excretion within bat populations, but the mechanisms driving such pulses are unclear. Three hypotheses dominate current research on these emerging bat infections. First, pulses of viral excretion could reflect seasonal epidemic cycles driven by natural variations in population densities and contact rates among hosts. If lifelong immunity follows recovery, viruses may disappear locally but persist globally through migration; in either case, new outbreaks occur once births replenish the susceptible pool. Second, epidemic cycles could be the result of waning immunity within bats, allowing local circulation of viruses through oscillating herd immunity. Third, pulses could be generated by episodic shedding from persistently infected bats through a combination of physiological and ecological factors. The three scenarios can yield similar patterns in epidemiological surveys, but strategies to predict or manage spillover risk resulting from each scenario will be different. We outline an agenda for research on viruses emerging from bats that would allow for differentiation among the scenarios and inform development of evidence-based interventions to limit threats to human and animal health. These concepts and methods are applicable to a wide range of pathogens that affect humans, domestic animals, and wildlife
Transmission or within-host dynamics driving pulses of zoonotic viruses in reservoir-host populations
(2016-08) Plowright, Raina K.; Peel, Alison J.; Streicker, Daniel G.; Gilbert, Amy T.; McCallum, Hamish I.; Wood, James; Baker, Michelle L.; Restif, Olivier
Progress in combatting zoonoses that emerge from wildlife is often constrained by limited knowledge of the biology of pathogens within reservoir hosts. We focus on the host–pathogen dynamics of four emerging viruses associated with bats: Hendra, Nipah, Ebola, and Marburg viruses. Spillover of bat infections to humans and domestic animals often coincides with pulses of viral excretion within bat populations, but the mechanisms driving such pulses are unclear. Three hypotheses dominate current research on these emerging bat infections. First, pulses of viral excretion could reflect seasonal epidemic cycles driven by natural variations in population densities and contact rates among hosts. If lifelong immunity follows recovery, viruses may disappear locally but persist globally through migration; in either case, new outbreaks occur once births replenish the susceptible pool. Second, epidemic cycles could be the result of waning immunity within bats, allowing local circulation of viruses through oscillating herd immunity. Third, pulses could be generated by episodic shedding from persistently infected bats through a combination of physiological and ecological factors. The three scenarios can yield similar patterns in epidemiological surveys, but strategies to predict or manage spillover risk resulting from each scenario will be different. We outline an agenda for research on viruses emerging from bats that would allow for differentiation among the scenarios and inform development of evidence-based interventions to limit threats to human and animal health. These concepts and methods are applicable to a wide range of pathogens that affect humans, domestic animals, and wildlife.