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Готові списки джерел за темами / Pathogen spillover

Добірка наукової літератури з теми "Pathogen spillover"

Автор: Grafiati

Опубліковано: 19 червня 2021

Оновлено: 4 лютого 2022

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Статті в журналах
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Статті в журналах з теми "Pathogen spillover":

1

Washburne, Alex D., Daniel E. Crowley, Daniel J. Becker, Kezia R. Manlove, Marissa L. Childs, and Raina K. Plowright. "Percolation models of pathogen spillover." Philosophical Transactions of the Royal Society B: Biological Sciences 374, no. 1782 (August 12, 2019): 20180331. http://dx.doi.org/10.1098/rstb.2018.0331.

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Predicting pathogen spillover requires counting spillover events and aligning such counts with process-related covariates for each spillover event. How can we connect our analysis of spillover counts to simple, mechanistic models of pathogens jumping from reservoir hosts to recipient hosts? We illustrate how the pathways to pathogen spillover can be represented as a directed graph connecting reservoir hosts and recipient hosts and the number of spillover events modelled as a percolation of infectious units along that graph. Percolation models of pathogen spillover formalize popular intuition and management concepts for pathogen spillover, such as the inextricably multilevel nature of cross-species transmission, the impact of covariance between processes such as pathogen shedding and human susceptibility on spillover risk, and the assumptions under which the effect of a management intervention targeting one process, such as persistence of vectors, will translate to an equal effect on the overall spillover risk. Percolation models also link statistical analysis of spillover event datasets with a mechanistic model of spillover. Linear models, one might construct for process-specific parameters, such as the log-rate of shedding from one of several alternative reservoirs, yield a nonlinear model of the log-rate of spillover. The resulting nonlinearity is approximately piecewise linear with major impacts on statistical inferences of the importance of process-specific covariates such as vector density. We recommend that statistical analysis of spillover datasets use piecewise linear models, such as generalized additive models, regression clustering or ensembles of linear models, to capture the piecewise linearity expected from percolation models. We discuss the implications of our findings for predictions of spillover risk beyond the range of observed covariates, a major challenge of forecasting spillover risk in the Anthropocene. This article is part of the theme issue ‘Dynamic and integrative approaches to understanding pathogen spillover’.

2

Power, Alison G., and Charles E. Mitchell. "Pathogen Spillover in Disease Epidemics." American Naturalist 164, S5 (November 2004): S79—S89. http://dx.doi.org/10.1086/424610.

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3

Faust, Christina L., Hamish I. McCallum, Laura S. P. Bloomfield, Nicole L. Gottdenker, Thomas R. Gillespie, Colin J. Torney, Andrew P. Dobson, and Raina K. Plowright. "Pathogen spillover during land conversion." Ecology Letters 21, no. 4 (February 21, 2018): 471–83. http://dx.doi.org/10.1111/ele.12904.

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4

Borremans, Benny, Christina Faust, Kezia R. Manlove, Susanne H. Sokolow, and James O. Lloyd-Smith. "Cross-species pathogen spillover across ecosystem boundaries: mechanisms and theory." Philosophical Transactions of the Royal Society B: Biological Sciences 374, no. 1782 (August 12, 2019): 20180344. http://dx.doi.org/10.1098/rstb.2018.0344.

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Pathogen spillover between different host species is the trigger for many infectious disease outbreaks and emergence events, and ecosystem boundary areas have been suggested as spatial hotspots of spillover. This hypothesis is largely based on suspected higher rates of zoonotic disease spillover and emergence in fragmented landscapes and other areas where humans live in close vicinity to wildlife. For example, Ebola virus outbreaks have been linked to contacts between humans and infected wildlife at the rural-forest border, and spillover of yellow fever via mosquito vectors happens at the interface between forest and human settlements. Because spillover involves complex interactions between multiple species and is difficult to observe directly, empirical studies are scarce, particularly those that quantify underlying mechanisms. In this review, we identify and explore potential ecological mechanisms affecting spillover of pathogens (and parasites in general) at ecosystem boundaries. We borrow the concept of ‘permeability’ from animal movement ecology as a measure of the likelihood that hosts and parasites are present in an ecosystem boundary region. We then discuss how different mechanisms operating at the levels of organisms and ecosystems might affect permeability and spillover. This review is a step towards developing a general theory of cross-species parasite spillover across ecosystem boundaries with the eventual aim of improving predictions of spillover risk in heterogeneous landscapes. This article is part of the theme issue ‘Dynamic and integrative approaches to understanding pathogen spillover’.

5

Cross, Paul C., Diann J. Prosser, Andrew M. Ramey, Ephraim M. Hanks, and Kim M. Pepin. "Confronting models with data: the challenges of estimating disease spillover." Philosophical Transactions of the Royal Society B: Biological Sciences 374, no. 1782 (August 12, 2019): 20180435. http://dx.doi.org/10.1098/rstb.2018.0435.

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For pathogens known to transmit across host species, strategic investment in disease control requires knowledge about where and when spillover transmission is likely. One approach to estimating spillover is to directly correlate observed spillover events with covariates. An alternative is to mechanistically combine information on host density, distribution and pathogen prevalence to predict where and when spillover events are expected to occur. We use several case studies at the wildlife–livestock disease interface to highlight the challenges, and potential solutions, to estimating spatio-temporal variation in spillover risk. Datasets on multiple host species often do not align in space, time or resolution, and may have no estimates of observation error. Linking these datasets requires they be related to a common spatial and temporal resolution and appropriately propagating errors in predictions can be difficult. Hierarchical models are one potential solution, but for fine-resolution predictions at broad spatial scales, many models become computationally challenging. Despite these limitations, the confrontation of mechanistic predictions with observed events is an important avenue for developing a better understanding of pathogen spillover. Systems where data have been collected at all levels in the spillover process are rare, or non-existent, and require investment and sustained effort across disciplines. This article is part of the theme issue ‘Dynamic and integrative approaches to understanding pathogen spillover’.

6

Sokolow, Susanne H., Nicole Nova, Kim M. Pepin, Alison J. Peel, Juliet R. C. Pulliam, Kezia Manlove, Paul C. Cross, et al. "Ecological interventions to prevent and manage zoonotic pathogen spillover." Philosophical Transactions of the Royal Society B: Biological Sciences 374, no. 1782 (August 12, 2019): 20180342. http://dx.doi.org/10.1098/rstb.2018.0342.

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Spillover of a pathogen from a wildlife reservoir into a human or livestock host requires the pathogen to overcome a hierarchical series of barriers. Interventions aimed at one or more of these barriers may be able to prevent the occurrence of spillover. Here, we demonstrate how interventions that target the ecological context in which spillover occurs (i.e. ecological interventions) can complement conventional approaches like vaccination, treatment, disinfection and chemical control. Accelerating spillover owing to environmental change requires effective, affordable, durable and scalable solutions that fully harness the complex processes involved in cross-species pathogen spillover. This article is part of the theme issue ‘Dynamic and integrative approaches to understanding pathogen spillover’.

7

Childs, Marissa L., Nicole Nova, Justine Colvin, and Erin A. Mordecai. "Mosquito and primate ecology predict human risk of yellow fever virus spillover in Brazil." Philosophical Transactions of the Royal Society B: Biological Sciences 374, no. 1782 (August 12, 2019): 20180335. http://dx.doi.org/10.1098/rstb.2018.0335.

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Many (re)emerging infectious diseases in humans arise from pathogen spillover from wildlife or livestock, and accurately predicting pathogen spillover is an important public health goal. In the Americas, yellow fever in humans primarily occurs following spillover from non-human primates via mosquitoes. Predicting yellow fever spillover can improve public health responses through vector control and mass vaccination. Here, we develop and test a mechanistic model of pathogen spillover to predict human risk for yellow fever in Brazil. This environmental risk model, based on the ecology of mosquito vectors and non-human primate hosts, distinguished municipality-months with yellow fever spillover from 2001 to 2016 with high accuracy (AUC = 0.72). Incorporating hypothesized cyclical dynamics of infected primates improved accuracy (AUC = 0.79). Using boosted regression trees to identify gaps in the mechanistic model, we found that important predictors include current and one-month lagged environmental risk, vaccine coverage, population density, temperature and precipitation. More broadly, we show that for a widespread human viral pathogen, the ecological interactions between environment, vectors, reservoir hosts and humans can predict spillover with surprising accuracy, suggesting the potential to improve preventive action to reduce yellow fever spillover and avert onward epidemics in humans. This article is part of the theme issue ‘Dynamic and integrative approaches to understanding pathogen spillover’.

8

Manlove, Kezia R., Laura M. Sampson, Benny Borremans, E. Frances Cassirer, Ryan S. Miller, Kim M. Pepin, Thomas E. Besser, and Paul C. Cross. "Epidemic growth rates and host movement patterns shape management performance for pathogen spillover at the wildlife–livestock interface." Philosophical Transactions of the Royal Society B: Biological Sciences 374, no. 1782 (August 12, 2019): 20180343. http://dx.doi.org/10.1098/rstb.2018.0343.

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Managing pathogen spillover at the wildlife–livestock interface is a key step towards improving global animal health, food security and wildlife conservation. However, predicting the effectiveness of management actions across host–pathogen systems with different life histories is an on-going challenge since data on intervention effectiveness are expensive to collect and results are system-specific. We developed a simulation model to explore how the efficacies of different management strategies vary according to host movement patterns and epidemic growth rates. The model suggested that fast-growing, fast-moving epidemics like avian influenza were best-managed with actions like biosecurity or containment, which limited and localized overall spillover risk. For fast-growing, slower-moving diseases like foot-and-mouth disease, depopulation or prophylactic vaccination were competitive management options. Many actions performed competitively when epidemics grew slowly and host movements were limited, and how management efficacy related to epidemic growth rate or host movement propensity depended on what objective was used to evaluate management performance. This framework offers one means of classifying and prioritizing responses to novel pathogen spillover threats, and evaluating current management actions for pathogens emerging at the wildlife–livestock interface. This article is part of the theme issue ‘Dynamic and integrative approaches to understanding pathogen spillover’.

9

Becker, Daniel J., Alex D. Washburne, Christina L. Faust, Erin A. Mordecai, and Raina K. Plowright. "The problem of scale in the prediction and management of pathogen spillover." Philosophical Transactions of the Royal Society B: Biological Sciences 374, no. 1782 (August 12, 2019): 20190224. http://dx.doi.org/10.1098/rstb.2019.0224.

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Disease emergence events, epidemics and pandemics all underscore the need to predict zoonotic pathogen spillover. Because cross-species transmission is inherently hierarchical, involving processes that occur at varying levels of biological organization, such predictive efforts can be complicated by the many scales and vastness of data potentially required for forecasting. A wide range of approaches are currently used to forecast spillover risk (e.g. macroecology, pathogen discovery, surveillance of human populations, among others), each of which is bound within particular phylogenetic, spatial and temporal scales of prediction. Here, we contextualize these diverse approaches within their forecasting goals and resulting scales of prediction to illustrate critical areas of conceptual and pragmatic overlap. Specifically, we focus on an ecological perspective to envision a research pipeline that connects these different scales of data and predictions from the aims of discovery to intervention. Pathogen discovery and predictions focused at the phylogenetic scale can first provide coarse and pattern-based guidance for which reservoirs, vectors and pathogens are likely to be involved in spillover, thereby narrowing surveillance targets and where such efforts should be conducted. Next, these predictions can be followed with ecologically driven spatio-temporal studies of reservoirs and vectors to quantify spatio-temporal fluctuations in infection and to mechanistically understand how pathogens circulate and are transmitted to humans. This approach can also help identify general regions and periods for which spillover is most likely. We illustrate this point by highlighting several case studies where long-term, ecologically focused studies (e.g. Lyme disease in the northeast USA, Hendra virus in eastern Australia, Plasmodium knowlesi in Southeast Asia) have facilitated predicting spillover in space and time and facilitated the design of possible intervention strategies. Such studies can in turn help narrow human surveillance efforts and help refine and improve future large-scale, phylogenetic predictions. We conclude by discussing how greater integration and exchange between data and predictions generated across these varying scales could ultimately help generate more actionable forecasts and interventions. This article is part of the theme issue ‘Dynamic and integrative approaches to understanding pathogen spillover’.

10

Rush, Elizabeth R., Erin Dale, and A. Alonso Aguirre. "Illegal Wildlife Trade and Emerging Infectious Diseases: Pervasive Impacts to Species, Ecosystems and Human Health." Animals 11, no. 6 (June 18, 2021): 1821. http://dx.doi.org/10.3390/ani11061821.

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Emerging infectious disease (EID) events can be traced to anthropogenic factors, including the movement of wildlife through legal and illegal trade. This paper focuses on the link between illegal wildlife trade (IWT) and infectious disease pathogens. A literature review through Web of Science and relevant conference proceedings from 1990 to 2020 resulted in documenting 82 papers and 240 identified pathogen cases. Over 60% of the findings referred to pathogens with known zoonotic potential and five cases directly referenced zoonotic spillover events. The diversity of pathogens by taxa included 44 different pathogens in birds, 47 in mammals, 16 in reptiles, two in amphibians, two in fish, and one in invertebrates. This is the highest diversity of pathogen types in reported literature related to IWT. However, it is likely not a fully representative sample due to needed augmentation of surveillance and monitoring of IWT and more frequent pathogen testing on recovered shipments. The emergence of infectious disease through human globalization has resulted in several pandemics in the last decade including SARS, MERS, avian influenza H1N1,and Ebola. We detailed the growing body of literature on this topic since 2008 and highlight the need to detect, document, and prevent spillovers from high-risk human activities, such as IWT.

Більше джерел

Дисертації з теми "Pathogen spillover":

1

Sundblad, Frida. "The relationship between the prevalence of ten known pathogens in wild swedish bees and the presence of a nearby apiary." Thesis, Uppsala universitet, Institutionen för medicinsk cellbiologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-445973.

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Pollination by insects is of great importance for the global food production. There is a specific need for pollination by bees in greenhouses and tunnel cultivations to increase the quantity, quality and market value of the crops. Imported bee colonies from central Europe are used for pollination of Swedish crops and have a great economic importance but are also a threat to wild Swedish bees by posing a risk of pathogen transmission between the bee species. The aim of this study was to investigate how imported bees affect the prevalence of pathogens amongst wild bees. Analysis was performed on 236 wild bees collected in near proximity to tunnel cultivation, greenhouse cultivation and collected from two control landscapes. The abdomen of the bees was used to extract RNA/DNA for further detection and quantification of ten pathogens using qPCR. The proportion of infected bees within each group was calculated based on the results from the qPCR analysis. A two-proportion z-test was used to determine whether the difference in pathogen prevalence between the four groups was of statistically significant at α = 0.05. The results show that there was no significant difference when comparing the presence of all pathogens between bees in the test groups and the bees in the control groups (p= 0,29- 0,33). However, the prevalence of three viruses was significantly higher among bees collected in the near proximity of a greenhouse compared with bees collected from the near proximity of a tunnel cultivation (p< 0,003). For Slow bee paralysis virus the prevalence was 2,5 times higher and for Deformed wing virus and Black queen cell virus the prevalence was 3,5 and 1,3 times higher among bees collected near a greenhouses compared to near a tunnel cultivation.

2

Blaisdell, Gretchen Kai 1974. "Introduced plant species, herbivores and pathogens, and the host-enemy relationships that accompany invasions." Thesis, University of Oregon, 2011. http://hdl.handle.net/1794/11227.

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xvi, 109 p. : ill.
Invasions by introduced plant species cost billions of dollars each year in the United States and threaten native habitat. The primary goal of my dissertation research was to examine the role that natural enemies (pathogens and herbivores) play in these invasions in both unmanaged and restored plant communities. In two related studies in seasonal wetland prairies in the Willamette Valley, Oregon, USA, I surveyed natural enemy attack on common native and introduced plant species in a restoration experiment designed to test the effects of site preparation techniques on plant community composition. Restoration treatments had little influence on enemy attack rates. Attack rates depended on idiosyncratic differences in the relationships between host species and plant community characteristics, suggesting that existing theories concerning these relationships have limited predictive power. Another field experiment tested the potential for enemy spillover from introduced to native species and dilution of natural enemy attack on introduced species by native species. I examined natural enemy attack on three native and three perennial grasses that commonly co-occur in the Willamette Valley. The native species are commonly used in restoration. The introduced species are common throughout North America and potentially harbor enemies that could affect both crops and natural communities. There was no compelling evidence of enemy spillover from the introduced to the native species, but dilution of enemies on the introduced species by the native species was evident in year 2 and even stronger in year 3 for two of the three introduced species. Using the same three introduced species from the spillover/dilution study, I tested the enemy release hypothesis, which proposes that introduced species lose natural enemies upon introduction and are thus "released" from population control. I surveyed populations of the three grass species across a wide geographic area in their native and naturalized ranges in Europe and the United States, respectively. I also compared my results to those of a previously published literature survey. My field survey supported release from herbivores but not from fungal pathogens. In contrast, the literature survey found evidence of release from fungal pathogens. This dissertation includes unpublished co-authored material.
Committee in charge: Brendan Bohannan, Chairperson; Bitty Roy, Co-Advisor; Scott Bridgham, Co-Advisor; Eric Seabloom, Member; Robert Mauro, Outside Member

Частини книг з теми "Pathogen spillover":

1

Daniels, P. W., K. Halpin, A. Hyatt, and D. Middleton. "Infection and Disease in Reservoir and Spillover Hosts: Determinants of Pathogen Emergence." In Current Topics in Microbiology and Immunology, 113–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-70962-6_6.

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2

Alexander, Kathleen A., Colin J. Carlson, Bryan L. Lewis, Wayne M. Getz, Madhav V. Marathe, Stephen G. Eubank, Claire E. Sanderson, and Jason K. Blackburn. "The Ecology of Pathogen Spillover and Disease Emergence at the Human-Wildlife-Environment Interface." In Advances in Environmental Microbiology, 267–98. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92373-4_8.

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3

Hamer, Sarah, and Gabriel Hamer. "Pathogen Transmission at the Expanding Bird–Human Interface." In Infectious Disease Ecology of Wild Birds, 229–44. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198746249.003.0012.

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The interface between wild bird populations and human populations is expanding, with the emergence of pathogen transmission between birds and humans as one consequence. In this chapter, several case studies of the spillover of avian pathogens into humans, and to a lesser extent human pathogens into birds, are reviewed in the context of the ecological and evolutionary factors that are important for disease emergence. Transmission and disease emergence may be complex, in some cases sculpted by the interaction of multiple parasite species with birds or their vectors. Additionally, avian migration allows opportunities for bird-associated pathogens and vectors to be transported over great distances, sometimes initiating new foci of zoonotic disease emergence. While several management strategies are being used to detect and respond to the emergence of avian zoonoses in birds and people, an increased understanding of the biology and circumstances that support transmission and spillover will further direct such efforts.

4

Franklin, Alan B., Sarah N. Bevins, and Susan A. Shriner. "Pathogens from Wild Birds at the Wildlife–Agriculture Interface." In Infectious Disease Ecology of Wild Birds, 207–28. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198746249.003.0011.

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Birds are known to carry pathogens affecting human and agricultural health. Conversely, agricultural operations can serve as sources of pathogens that affect wild bird populations. This chapter provides guidelines to identify focal avian species that frequently use agricultural operations. These guidelines are coupled with identifying host types, such as maintenance and bridge hosts, and potential direct and indirect pathways for pathogen contamination from wild birds to agricultural operations, including patterns of spillover and spillback. The chapter also identifies major bacterial and viral pathogens of concern that are prevalent in birds and that affect human and agricultural health. These pathogens are then used to illustrate disease ecology concepts important at the wildlife–agriculture interface. These microorganisms include food-borne bacteria, influenza A viruses, and Newcastle disease virus. The chapter introduces the concept of contamination potential for categorizing avian species in terms of the risk they pose to contamination of agricultural operations with pathogens of concern. Finally, the chapter examines long-distance movements of wild birds in relation to pathogen introduction and illustrates this with global movement of influenza A viruses by wild birds.

5

Diuk-Wasser, Maria A., Maria del Pilar Fernandez, and Stephen Davis. "Ecological Interactions Influencing the Emergence, Abundance, and Human Exposure to Tick-Borne Pathogens." In Population Biology of Vector-Borne Diseases, 135–54. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198853244.003.0008.

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Tick-borne pathogens pose the greatest vector-borne disease burden in temperate areas of Europe and North America. We synthesize key aspects of tick life history that enable ticks to persist, spread and impact human health, including a two-year life cycle, multiple transmission pathways and dependence on hosts for tick feeding, movement and pathogen transmission. We discuss modeling advances that incorporate these traits in the context of climate-driven variation in tick feeding phenology. For established pathogens, such as the Lyme disease agent in the United States, we disentangle the linkages between land use change, habitat fragmentation and host diversity influencing human risk of infection along an urbanization gradient. We propose a coupled natural-human system framework for tick-borne pathogens that accounts for nonlinear effects and feedbacks between the enzootic cycle and human spillover. A deeper understanding of the eco-bio-social determinants of these diseases is required to develop more effective public health interventions.

6

Machalaba, Catherine, Cristina Romanelli, and Peter Stoett. "Global Environmental Change and Emerging Infectious Diseases." In Healthcare Policy and Reform, 38–71. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-6915-2.ch003.

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The prediction of emerging infectious diseases (EIDs) and the avoidance of their tremendous social and economic costs is contingent on the identification of their most likely drivers. It is argued that the drivers of global environmental change (and climate change as both a driver and an impact) are often the drivers of EIDs; and that the two overlap to such a strong degree that targeting these drivers is sound epidemiological policy. Several drivers overlap with the leading causes of biodiversity loss, providing opportunities for health and biodiversity sectors to generate synergies at local and global levels. This chapter provides a primer on EID ecology, reviews underlying drivers and mechanisms that facilitate pathogen spillover and spread, provides suggested policy and practice-based actions toward the prevention of EIDs in the context of environmental change, and identifies knowledge gaps for the purpose of further research.

7

Machalaba, Catherine, Cristina Romanelli, and Peter Stoett. "Global Environmental Change and Emerging Infectious Diseases." In Examining the Role of Environmental Change on Emerging Infectious Diseases and Pandemics, 24–67. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0553-2.ch002.

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The prediction of emerging infectious diseases (EIDs) and the avoidance of their tremendous social and economic costs is contingent on the identification of their most likely drivers. It is argued that the drivers of global environmental change (and climate change as both a driver and an impact) are often the drivers of EIDs; and that the two overlap to such a strong degree that targeting these drivers is sound epidemiological policy. Several drivers overlap with the leading causes of biodiversity loss, providing opportunities for health and biodiversity sectors to generate synergies at local and global levels. This chapter provides a primer on EID ecology, reviews underlying drivers and mechanisms that facilitate pathogen spillover and spread, provides suggested policy and practice-based actions toward the prevention of EIDs in the context of environmental change, and identifies knowledge gaps for the purpose of further research.