Geology topics

Daniel P. Walsh

Publications and source records attributed to Daniel P. Walsh.

At least 55 records · Page 3Linked to original sources

USGS2020

Assessing the risks posed by SARS-CoV-2 in and via North American bats — Decision framing and rapid risk assessment

The novel β-coronavirus, SARS-CoV-2, may pose a threat to North American bat populations if bats are exposed to the virus through interaction with humans, if the virus can subsequently infect bats and be transmitted among them, and if the virus causes morbidity or mortality in bats. Further, if SARS-CoV-2 became established in bat populations, it could possibly serve as a source for new infection in humans, domesticated animals, or other wild animals. Wildlife management agencies in the United States are concerned about these potential risks and have begun to issue guidance regarding work that brings humans into contact with bats, but decision making is difficult because of the high degree of uncertainty about many of the relevant processes that could lead to virus transmission and establishment. The risk assessment described in this report was undertaken to provide management agencies with an understanding of the likelihood that the various steps in the causal pathways would lead to SARS-CoV-2 infection of North American bats from people. This assessment focused on the active season for bats in the temperate zone of North America (April 15 through November 15), and used Myotis lucifugus (little brown bats) as a surrogate species. At the time of this work (April 2020), no empirical data about the effects of SARS-CoV-2 on North American bats were available, so a formal process of expert judgment was used to elicit estimates of the underlying parameters. Twelve experts in bat ecology, epidemiology, virology, and wildlife disease from the United States, United Kingdom, and Australia participated in the elicitation. A Monte Carlo simulation model was used to integrate the parameter estimates elicited from the experts and to predict the likelihood of exposure and infection in bats through a series of transmission pathways, with particular attention to capturing uncertainty in the predictions. Given the current state of knowledge as expressed by the expert panel, the results of this assessment indicate that there is a non-negligible risk of transmission of SARS-CoV-2 from humans to bats. For example, if a research scientist were shedding SARS-CoV-2 virus while handling bats under the field protocols used in North America prior to the COVID-19 pandemic, the risk model indicates that 50 percent (uncertainty, 15–84 percent) of those bats could be exposed to virus, and 17 percent (uncertainty, 3–51 percent) could become infected. Use of personal protective equipment, especially a respirator, is expected to reduce the exposure risk. The expert panel estimated that exposure risk from research scientists could be reduced 94–96 percent (uncertainty, 86–99 percent) through proper use of appropriate N95 respirators (a type of mechanical filter worn over the nose and mouth), dedicated clothing (such as Tyvek coveralls), and gloves. Should any North American bats become infected with SARS-CoV-2, the expert panel estimated that there is an approximately 33-percent chance the virus could spread within a bat population. This study, conducted by the U.S. Geological Survey in cooperation with the U.S. Fish and Wildlife Service, identified several critical uncertainties that could affect the estimate of risks associated with SARS-CoV-2 entering bat populations—notably, the underlying probability that a human would be shedding virus while working with bats, the likelihood of the virus replicating in bat tissue, and the likelihood of transmission of the virus within bat populations. Ongoing empirical work during May–October 2020 may shed light on these issues. Follow-up work is needed to better understand the probability of transmission of SARS-CoV-2 to bats from the general public; the manner in which the probabilities of exposure, infection, and transmission would differ during hibernation compared to the breeding season; and the likelihood of important effects, like morbidity and mortality in bats, the possibility of zoonosis from a North American bat reservoir, and effects of and on other wildlife.

North America↗

USGS2020

Removal of chronic Mycoplasma ovipneumoniae carrier ewes eliminates pneumonia in a bighorn sheep population

Chronic pathogen carriage is one mechanism that allows diseases to persist in populations. We hypothesized that persistent or recurrent pneumonia in bighorn sheep ( Ovis canadensis ) populations may be caused by chronic carriers of Mycoplasma ovipneumoniae ( Mo ). Our experimental approach allowed us to address a conservation need while investigating the role of chronic carriage in disease persistence. We tested our hypothesis in two bighorn sheep populations in South Dakota, USA. We identified and removed Mo chronic carriers from the Custer State Park (treatment) population. Simultaneously, we identified carriers but did not remove them from the Rapid City population (control). We predicted removal would result in decreased pneumonia, mortality, and Mo prevalence. Both population ranges had similar habitat and predator communities but were sufficiently isolated to preclude intermixing. We classified chronic carriers as adults that consistently tested positive for Mo carriage over a 20‐month sampling period ( n = 2 in the treatment population; n = 2 in control population). We failed to detect Mo or pneumonia in the treatment population after chronic carrier removal, while both remained in the control. Mortality hazard for lambs was reduced by 72% in the treatment population relative to the control (CI = 36%, 91%). There was also a 41% reduction in adult mortality hazard attributable to the treatment, although this was not statistically significant (CI = 82% reduction, 34% increase). Synthesis and Applications : These results support the hypothesis that Mo is a primary causative agent of persistent or recurrent respiratory disease in bighorn sheep populations and can be maintained by a few chronic carriers. Our findings provide direction for future research and management actions aimed at controlling pneumonia in wild sheep and may apply to other diseases.

South Dakota↗

USGS2020

Long-term survival of Pseudogymnoascus destructans at elevated temperatures

White-nose syndrome is an emerging fungal disease that has devastated hibernating bat populations across eastern North America. The causal pathogen, Pseudogymnoascus destructans (PD), is a psychrophilic fungus with a known maximal growth temperature of 20 C. Although it is widely speculated that PD is primarily spread between hibernacula by the movement of bats, experimental evidence is lacking to demonstrate that PD can endure temperatures experienced by active bats for periods of time that would facilitate dispersal of viable fungus. We used an in vitro culture-based approach to study the survival of PD conidia on three artificial growth media and bat fur. The fungus was incubated at three temperatures it might realistically be exposed to on nonhibernating bats or in the environment outside of caves and mines (24 C, 30 C, and 37 C). When incubated on artificial media, we found that PD conidia were able to survive for a maximum of 150 d when exposed to temperatures of 24 C, 60 d at 30 C, and 15 d at 37 C. At all temperatures, maximal survival duration was recorded when conidia were incubated on brain–heart infusion agar with 10% volume of sheep ( Ovis aries ) blood. When incubated on bat fur, viable PD was recovered at 180 d, 60 d, and 5 d when exposed to temperatures of 24 C, 30 C, and 37 C, respectively. Our results suggest that viable PD conidia may be able to survive on or within the bodies of bats, which may facilitate long-distance dispersal. The long-term viability of the fungus on various fomites may differ, and therefore must be assessed for each potential substrate.

Journal of Wildlife Diseases↗

USGS2020

An ecological and conservation perspective

Natural ecosystems are facing unprecedented threats which directly threaten human well-being through decreases in critical ecosystem services (IPBES 2019). The top five drivers causing the largest global impacts to biodiversity and ecosystem services include: 1) changes in land and sea use; 2) direct exploitation of organisms; 3) climate change; 4) pollution, and 5) invasive alien species (IPBES 2019). Although One Health acknowledges the link between the health of humans, animals, and the environment, One Health discussions have historically focused on the prevention and control of infectious disease at the human-animal interface rather than these large-scale drivers of health. While One Health has succeeded in bringing awareness to the need for proactive disease control measures such as strengthened biosecurity and vaccine development (e.g., Machalaba et al., 2018; Middleton et al., 2014), disease is only one component of health. In this chapter, we explore the potential for One Health to shift its focus from disease prevention to health promotion to more fully integrate solutions that protect the health of humans, animals, and the ecosystems on which we all depend for our economies, livelihoods, food security, and health. This shift will facilitate a more seamless inclusion of ecological health and environmental conservation in the One Health paradigm and can serve as the basis for a comprehensive approach to complex problems at the root of global health. We also suggest a framework for creating and applying health metrics for wildlife and ecological systems that will be essential for measuring the success of actions aimed at maintaining or shifting systems to desired states.

Book chapter↗

USGS2019

Chronic wasting disease—Research by the U.S. Geological Survey and partners

Introduction Chronic wasting disease (CWD) is the only transmissible spongiform encephalopathy, a class of invariably fatal neurodegenerative mammalian diseases associated with a misfolded cellular prion protein found in wild free-ranging animals. Because it has a long incubation period, affected animals in Cervidae (the deer family; referred to as “cervids”) may not show signs of disease for several years. While signs are not specific to CWD, affected cervids (deer, elk, moose, and reindeer) show changes in appearance (such as progressive weight loss) and changes in behavior such as stumbling, tremors, and teeth grinding. CWD can be transmitted by direct contact or through a contaminated environment. The causative prion agent is highly resistant to degradation. In recent decades, CWD has transitioned from a novel, obscure prion disease of cervids with limited geographical distribution, to a disease that poses substantial ecological, agricultural, and economic risks across large regions of North America. Since its discovery in free-ranging elk and deer populations in the western United States in the 1980s, CWD has been reported in captive or free-ranging cervid populations in 26 States, 3 Canadian Provinces, the Republic of South Korea, Finland, Sweden, and Norway. In addition, the proportion of CWD-infected animals is increasing in many areas where the disease is already established. In some heavily affected areas, total cervid numbers have decreased over time due to CWD, which suggests that these cervid populations may not be sustainable in the long-term. The U.S. Geological Survey (USGS) conducts wildlife disease surveillance and research to support management of CWD-affected species and their habitats. The scientific information is relevant to governmental agencies that manage wildlife and their habitats including the U.S. Fish and Wildlife Service, the National Park Service, the U.S. Department of Agriculture, and other Federal, State, and Tribal agencies as well as conservation partners (non-governmental organizations, businesses, and private landowners). Each project description in this report (1–30) includes the non-USGS collaborators (Federal, State, Tribal agencies, universities) and a USGS point of contact (principal investigator). If there are USGS publications associated with the project, a publication list is provided at the end of each project description.

Alberta, California, Colorado, Idaho, Iowa, Kansas↗

USGS2019

Artificial intelligence and avian influenza: Using machine learning to enhance active surveillance for avian influenza viruses

Influenza A viruses are one of the most significant viral groups globally with substantial impacts on human, domestic animal and wildlife health. Wild birds are the natural reservoirs for these viruses, and active surveillance within wild bird populations provides critical information about viral evolution forming the basis of risk assessments and countermeasure development. Unfortunately, active surveillance programs are often resource‐intensive, and thus, enhancing programs for increased efficiency is paramount. Machine learning, a branch of artificial intelligence applications, provides statistical learning procedures that can be used to gain novel insights into disease surveillance systems. We use a form of machine learning, gradient boosted trees, to estimate the probability of isolating avian influenza viruses (AIV) from wild bird samples collected during surveillance for AIVs from 2006 to 2011 in the United States. We examined several predictive features including age, sex, bird type, geographic location and matrix gene rRT‐PCR results. Our final model had high predictive power and only included geographic location and rRT‐PCR results as important predictors. The highest predicted viral isolation probability was for samples collected from the north‐central states and the south‐eastern region of Alaska. Lower rRT‐PCR Ct‐values are associated with increased likelihood of AIV isolation, and the model estimated 16% probability of isolating AIV from samples declared negative (i.e., ≥35 Ct‐value) using the rRT‐PCR screening test and standard protocols. Our model can be used to prioritize previously collected samples for isolation and rapidly evaluate AIV surveillance designs to maximize the probability of viral isolation given limited resources and laboratory capacity.

Transboundary and Emerging Diseases↗

USGS2018

Predicting the initial spread of novel Asian origin influenza A viruses in the continental USA by wild waterfowl

Using data on waterfowl band recoveries, we identified spatially explicit hotspots of concentrated waterfowl movement to predict occurrence and spatial spread of a novel influenza A virus (clade 2.3.4.4) introduced from Asia by waterfowl from an initial outbreak in North America in November 2014. In response to the outbreak, the hotspots of waterfowl movement were used to help guide sampling for clade 2.3.4.4 viruses in waterfowl as an early warning for the US poultry industry during the outbreak . After surveillance sampling of waterfowl, we tested whether there was greater detection of clade 2.3.4.4 viruses inside hotspots. We found that hotspots defined using kernel density estimates of waterfowl band recoveries worked well in predicting areas with higher prevalence of the viruses in waterfowl. This approach exemplifies the value of ecological knowledge in predicting risk to agricultural security.

Transboundary and Emerging Diseases↗

USGS2018

Challenges and opportunities developing mathematical models of shared pathogens of domestic and wild animals

Diseases that affect both wild and domestic animals can be particularly difficult to prevent, predict, mitigate, and control. Such multi-host diseases can have devastating economic impacts on domestic animal producers and can present significant challenges to wildlife populations, particularly for populations of conservation concern. Few mathematical models exist that capture the complexities of pathogens, yet development of such models would allow us to estimate and compare the potential effectiveness of management actions for mitigating or suppressing disease in wildlife and/or livestock host populations. We conducted a workshop in March 2014 to identify the challenges associated with developing models of pathogen transmission across the wildlife-livestock interface. The development of mathematical models of pathogen transmission at this interface is hampered by the difficulties associated with describing the host-pathogen systems including: 1) the identity of wildlife hosts, their distributions, and movement patterns, 2) the pathogen transmission pathways between wildlife and domestic animals, 3) the effects of the disease and concomitant mitigation efforts on wild and domestic animal populations, and 4) barriers to communication between sectors. To promote the development of mathematical models of transmission at this interface, we recommend further integration of modern quantitative techniques and improvement of communication among wildlife biologists, mathematical modelers, veterinary medicine professionals, producers, and other stakeholders concerned with the consequences of pathogen transmission at this important, yet poorly understood, interface.

Veterinary Sciences↗

USGS2018

Applying a Bayesian weighted surveillance approach to detect chronic wasting disease in white‐tailed deer

Surveillance is critical for early detection of emerging and re‐emerging infectious diseases. Weighted surveillance leverages heterogeneity in infection risk to increase sampling efficiency. Here, we apply a Bayesian approach to estimate weights for 16 surveillance classes of white‐tailed deer in Wisconsin, USA, relative to hunter‐harvested yearling males. We used these weights to conduct a surveillance programme for detecting chronic wasting disease (CWD) in white‐tailed deer at Shenandoah National Park (SHEN) in Virginia, USA. Generally, for surveillance, risk of infection increased with age and was greater in males. Clinical suspect deer had the highest risk, with weight estimates of 33.33 and 9.09 for community‐reported and hunter‐reported suspect deer, respectively. Fawns had the lowest risk with an estimated weight of 0.001. We used surveillance weights for Wisconsin deer to determine sampling effort required to detect a CWD‐positive case in SHEN if prevalence in yearling males ≥0.025. The sampling required to detect CWD was 37–91 adult deer, depending on the adult male:female ratio in the surveillance stream. We collected rectal biopsies from 49 female and 21 male adult deer, and 10 additional samples from vehicle‐killed deer. CWD was not detected and we concluded with 95% probability that prevalence in the reference population (yearling males) was between 0.0% and 3.6%. Synthesis and applications . Our approach allows managers to estimate relative surveillance weights for different host classes and quantify limits of disease detection in real time when only a sample of animals from a population can be tested, resulting in considerable cost savings for agencies performing wildlife disease detection surveillance. Additionally, it provides a rigorous means of estimating prevalence limits when a disease/pathogen is not detected in a sample set. It is therefore applicable to other wildlife, domestic animal and human disease systems, which can be characterized by surveillance classes with heterogeneous probability of infection. This methodology is also extendable to other disciplines such as invasive species, environmental toxicology, and generally, any ecological question seeking to efficiently use scarce financial and human resources to maximize the detection probability of a rare event.

Journal of Applied Ecology↗

USGS2018

Factors influencing elk recruitment across ecotypes in the Western United States

Ungulates are key components in ecosystems and economically important for sport and subsistence harvest. Yet the relative importance of the effects of weather conditions, forage productivity, and carnivores on ungulates are not well understood. We examined changes in elk ( Cervus canadensis ) recruitment (indexed as age ratios) across 7 states and 3 ecotypes in the northwestern United States during 1989–2010, while considering the effects of predator richness, forage productivity, and precipitation. We found a broad‐scale, long‐term decrease in elk recruitment of 0.48 juveniles/100 adult females/year. Weather conditions (indexed as summer and winter precipitation) showed small, but measurable, influences on recruitment. Forage productivity on summer and winter ranges (indexed by normalized difference vegetation index [NDVI] metrics) had the strongest effect on elk recruitment relative to other factors. Relationships between forage productivity and recruitment varied seasonally and regionally. The productivity of winter habitat was more important in southern parts of the study area, whereas annual variation in productivity of summer habitat had more influence on recruitment in northern areas. Elk recruitment varied by up to 15 juveniles/100 adult females across the range of variation in forage productivity. Areas with more species of large carnivores had relatively low elk recruitment, presumably because of increased predation. Wolves ( Canis lupus ) were associated with a decrease of 5 juveniles/100 adult females, whereas grizzly bears ( Ursus arctos ) were associated with an additional decrease of 7 juveniles/100 adult females. Carnivore species can have a critical influence on ungulate recruitment because their influence rivals large ranges of variation in environmental conditions. A more pressing concern, however, stems from persistent broad‐scale decreases in recruitment across the distribution of elk in the northwestern United States, irrespective of carnivore richness. Our results suggest that wildlife managers interested in improving recruitment of elk consider the combined effects of habitat and predators. Efforts to manage summer and winter ranges to increase forage productivity may have a positive effect on recruitment.

Journal of Wildlife Management↗

USGS2018

Chronic wasting disease—Status, science, and management support by the U.S. Geological Survey

The U.S. Geological Survey (USGS) investigates chronic wasting disease (CWD) at multiple science centers and cooperative research units across the Nation and supports the management of CWD through science-based strategies. CWD research conducted by USGS scientists has three strategies: (1) to understand the biology, ecology, and causes and distribution of CWD; (2) to assess and predict the spread and persistence of CWD in wildlife and the environment; and (3) to develop tools for early detection, diagnosis, surveillance, and control of CWD.

Open-File Report↗

USGS2017

Using expert knowledge to incorporate uncertainty in cause-of-death assignments for modeling of cause-specific mortality

Implicit and explicit use of expert knowledge to inform ecological analyses is becoming increasingly common because it often represents the sole source of information in many circumstances. Thus, there is a need to develop statistical methods that explicitly incorporate expert knowledge, and can successfully leverage this information while properly accounting for associated uncertainty during analysis. Studies of cause-specific mortality provide an example of implicit use of expert knowledge when causes-of-death are uncertain and assigned based on the observer's knowledge of the most likely cause. To explicitly incorporate this use of expert knowledge and the associated uncertainty, we developed a statistical model for estimating cause-specific mortality using a data augmentation approach within a Bayesian hierarchical framework. Specifically, for each mortality event, we elicited the observer's belief of cause-of-death by having them specify the probability that the death was due to each potential cause. These probabilities were then used as prior predictive values within our framework. This hierarchical framework permitted a simple and rigorous estimation method that was easily modified to include covariate effects and regularizing terms. Although applied to survival analysis, this method can be extended to any event-time analysis with multiple event types, for which there is uncertainty regarding the true outcome. We conducted simulations to determine how our framework compared to traditional approaches that use expert knowledge implicitly and assume that cause-of-death is specified accurately. Simulation results supported the inclusion of observer uncertainty in cause-of-death assignment in modeling of cause-specific mortality to improve model performance and inference. Finally, we applied the statistical model we developed and a traditional method to cause-specific survival data for white-tailed deer, and compared results. We demonstrate that model selection results changed between the two approaches, and incorporating observer knowledge in cause-of-death increased the variability associated with parameter estimates when compared to the traditional approach. These differences between the two approaches can impact reported results, and therefore, it is critical to explicitly incorporate expert knowledge in statistical methods to ensure rigorous inference.

Ecology and Evolution↗

USGS2017

Semi-quantitative assessment of disease risks at the human, livestock, wildlife interface for the Republic of Korea using a nationwide survey of experts: A model for other countries

Wildlife-associated diseases and pathogens have increased in importance; however, management of a large number of diseases and diversity of hosts is prohibitively expensive. Thus, the determination of priority wildlife pathogens and risk factors for disease emergence is warranted. We used an online questionnaire survey to assess release and exposure risks, and consequences of wildlife-associated diseases and pathogens in the Republic of Korea (ROK). We also surveyed opinions on pathways for disease exposure, and risk factors for disease emergence and spread. For the assessment of risk, we employed a two-tiered, statistical K -means clustering algorithm to group diseases into three levels (high, medium and low) of perceived risk based on release and exposure risks, societal consequences and the level of uncertainty of the experts’ opinions. To examine the experts’ perceived risk of routes of introduction of pathogens and disease amplification and spread, we used a Bayesian, multivariate normal order-statistics model. Six diseases or pathogens, including four livestock and two wildlife diseases, were identified as having high risk with low uncertainty. Similarly, 13 diseases were characterized as having high risk with medium uncertainty with three of these attributed to livestock, six associated with human disease, and the remainder having the potential to affect human, livestock and wildlife (i.e., One Health). Lastly, four diseases were described as high risk with high certainty, and were associated solely with fish diseases. Experts identified migration of wildlife, international human movement and illegal importation of wildlife as the three routes posing the greatest risk of pathogen introduction into ROK. Proximity of humans, livestock and wildlife was the most significant risk factor for promoting the spread of wildlife-associated diseases and pathogens, followed by high density of livestock populations, habitat loss and environmental degradation, and climate change. This study provides useful information to decision makers responsible for allocating resources to address disease risks. This approach provided a rapid, cost-effective method of risk assessment of wildlife-associated diseases and pathogens for which the published literature is sparse.

Transboundary and Emerging Diseases↗

USGS2017

When mechanism matters: Bayesian forecasting using models of ecological diffusion

Ecological diffusion is a theory that can be used to understand and forecast spatio-temporal processes such as dispersal, invasion, and the spread of disease. Hierarchical Bayesian modelling provides a framework to make statistical inference and probabilistic forecasts, using mechanistic ecological models. To illustrate, we show how hierarchical Bayesian models of ecological diffusion can be implemented for large data sets that are distributed densely across space and time. The hierarchical Bayesian approach is used to understand and forecast the growth and geographic spread in the prevalence of chronic wasting disease in white-tailed deer ( Odocoileus virginianus ). We compare statistical inference and forecasts from our hierarchical Bayesian model to phenomenological regression-based methods that are commonly used to analyse spatial occurrence data. The mechanistic statistical model based on ecological diffusion led to important ecological insights, obviated a commonly ignored type of collinearity, and was the most accurate method for forecasting.

Ecology Letters↗

USGS2017

The Bayesian group lasso for confounded spatial data

Generalized linear mixed models for spatial processes are widely used in applied statistics. In many applications of the spatial generalized linear mixed model (SGLMM), the goal is to obtain inference about regression coefficients while achieving optimal predictive ability. When implementing the SGLMM, multicollinearity among covariates and the spatial random effects can make computation challenging and influence inference. We present a Bayesian group lasso prior with a single tuning parameter that can be chosen to optimize predictive ability of the SGLMM and jointly regularize the regression coefficients and spatial random effect. We implement the group lasso SGLMM using efficient Markov chain Monte Carlo (MCMC) algorithms and demonstrate how multicollinearity among covariates and the spatial random effect can be monitored as a derived quantity. To test our method, we compared several parameterizations of the SGLMM using simulated data and two examples from plant ecology and disease ecology. In all examples, problematic levels multicollinearity occurred and influenced sampling efficiency and inference. We found that the group lasso prior resulted in roughly twice the effective sample size for MCMC samples of regression coefficients and can have higher and less variable predictive accuracy based on out-of-sample data when compared to the standard SGLMM.

Journal of Agricultural, Biological, and Environme↗

USGS2017

A dynamic spatio-temporal model for spatial data

Analyzing spatial data often requires modeling dependencies created by a dynamic spatio-temporal data generating process. In many applications, a generalized linear mixed model (GLMM) is used with a random effect to account for spatial dependence and to provide optimal spatial predictions. Location-specific covariates are often included as fixed effects in a GLMM and may be collinear with the spatial random effect, which can negatively affect inference. We propose a dynamic approach to account for spatial dependence that incorporates scientific knowledge of the spatio-temporal data generating process. Our approach relies on a dynamic spatio-temporal model that explicitly incorporates location-specific covariates. We illustrate our approach with a spatially varying ecological diffusion model implemented using a computationally efficient homogenization technique. We apply our model to understand individual-level and location-specific risk factors associated with chronic wasting disease in white-tailed deer from Wisconsin, USA and estimate the location the disease was first introduced. We compare our approach to several existing methods that are commonly used in spatial statistics. Our spatio-temporal approach resulted in a higher predictive accuracy when compared to methods based on optimal spatial prediction, obviated confounding among the spatially indexed covariates and the spatial random effect, and provided additional information that will be important for containing disease outbreaks.

Wisconsin↗

USGS2017

A framework for modeling emerging diseases to inform management

The rapid emergence and reemergence of zoonotic diseases requires the ability to rapidly evaluate and implement optimal management decisions. Actions to control or mitigate the effects of emerging pathogens are commonly delayed because of uncertainty in the estimates and the predicted outcomes of the control tactics. The development of models that describe the best-known information regarding the disease system at the early stages of disease emergence is an essential step for optimal decision-making. Models can predict the potential effects of the pathogen, provide guidance for assessing the likelihood of success of different proposed management actions, quantify the uncertainty surrounding the choice of the optimal decision, and highlight critical areas for immediate research. We demonstrate how to develop models that can be used as a part of a decision-making framework to determine the likelihood of success of different management actions given current knowledge.

Emerging Infectious Diseases↗

USGS2017

No evidence of infection or exposure to Highly Pathogenic Avian Influenzas in peridomestic wildlife on an affected poultry facility

We evaluated the potential transmission of avian influenza viruses (AIV) in wildlife species in three settings in association with an outbreak at a poultry facility: 1) small birds and small mammals on a poultry facility that was affected with highly pathogenic AIV (HPAIV) in April 2015; 2) small birds and small mammals on a nearby poultry facility that was unaffected by HPAIV; and 3) small birds, small mammals, and waterfowl in a nearby natural area. We live-captured small birds and small mammals and collected samples from hunter-harvested waterfowl to test for active viral shedding and evidence of exposure (serum antibody) to AIV and the H5N2 HPAIV that affected the poultry facility. We detected no evidence of shedding or specific antibody to AIV in small mammals and small birds 5 mo after depopulation of the poultry. We detected viral shedding and exposure to AIV in waterfowl and estimated approximately 15% viral shedding and 60% antibody prevalence. In waterfowl, we did not detect shedding or exposure to the HPAIV that affected the poultry facility. We also conducted camera trapping around poultry carcass depopulation composting barns and found regular visitation by four species of medium-sized mammals. We provide preliminary data suggesting that peridomestic wildlife were not an important factor in the transmission of AIV during the poultry outbreak, nor did small birds and mammals in natural wetland settings show wide evidence of AIV shedding or exposure, despite the opportunity for exposure.

Journal of Wildlife Diseases↗