Center for Epidemiology and Animal Health
Center for Epidemiology and Animal Health
Willeberg P.,University of California at Davis |
Grubbe T.,Danish Veterinary and Food Administration |
Weber S.,Center for Epidemiology and Animal Health |
Forde-Folle K.,Center for Epidemiology and Animal Health |
Dube C.,Canadian Food Inspection Agency
OIE Revue Scientifique et Technique | Year: 2011
The papers in this issue of the Scientific and Technical Review (the Review) examine uses of modelling as a tool to support the formulation of disease control policy and applications of models for various aspects of animal disease management. Different issues in model development and several types of models are described. The experience with modelling during the 2001 foot and mouth disease outbreak in the United Kingdom underlines how models might be appropriately applied by decision-makers when preparing for and dealing with animal health emergencies. This paper outlines the involvement of the World Organisation for Animal Health (OIE) in epidemiological modelling since 2005, with emphasis on the outcome of the 2007 questionnaire survey of model usage among Member Countries, the subsequent OIE General Session resolution and the 2008 epidemiological modelling workshop at the Centers for Epidemiology and Animal Health in the United States. Many of the workshop presentations were developed into the papers that are presented in this issue of the Review.
Lindstrom T.,Linköping University |
Lindstrom T.,University of Sydney |
Grear D.A.,Colorado State University |
Buhnerkempe M.,Colorado State University |
And 4 more authors.
PLoS ONE | Year: 2013
Networks are rarely completely observed and prediction of unobserved edges is an important problem, especially in disease spread modeling where networks are used to represent the pattern of contacts. We focus on a partially observed cattle movement network in the U.S. and present a method for scaling up to a full network based on Bayesian inference, with the aim of informing epidemic disease spread models in the United States. The observed network is a 10% state stratified sample of Interstate Certificates of Veterinary Inspection that are required for interstate movement; describing approximately 20,000 movements from 47 of the contiguous states, with origins and destinations aggregated at the county level. We address how to scale up the 10% sample and predict unobserved intrastate movements based on observed movement distances. Edge prediction based on a distance kernel is not straightforward because the probability of movement does not always decline monotonically with distance due to underlying industry infrastructure. Hence, we propose a spatially explicit model where the probability of movement depends on distance, number of premises per county and historical imports of animals. Our model performs well in recapturing overall metrics of the observed network at the node level (U.S. counties), including degree centrality and betweenness; and performs better compared to randomized networks. Kernel generated movement networks also recapture observed global network metrics, including network size, transitivity, reciprocity, and assortativity better than randomized networks. In addition, predicted movements are similar to observed when aggregated at the state level (a broader geographic level relevant for policy) and are concentrated around states where key infrastructures, such as feedlots, are common. We conclude that the method generally performs well in predicting both coarse geographical patterns and network structure and is a promising method to generate full networks that incorporate the uncertainty of sampled and unobserved contacts.
Ruder M.G.,Manhattan College |
Lysyk T.J.,Agriculture and Agri Food Canada |
Stallknecht D.E.,University of Georgia |
Foil L.D.,Louisiana State University |
And 4 more authors.
Vector-Borne and Zoonotic Diseases | Year: 2015
Bluetongue virus (BTV) and epizootic hemorrhagic disease virus (EHDV) are arthropod-transmitted viruses in the genus Orbivirus of the family Reoviridae. These viruses infect a variety of domestic and wild ruminant hosts, although the susceptibility to clinical disease associated with BTV or EHDV infection varies greatly among host species, as well as between individuals of the same species. Since their initial detection in North America during the 1950s, these viruses have circulated in endemic and epidemic patterns, with occasional incursions to more northern latitudes. In recent years, changes in the pattern of BTV and EHDV infection and disease have forced the scientific community to revisit some fundamental areas related to the epidemiology of these diseases, specifically in relation to virus-vector-host interactions and environmental factors that have potentially enabled the observed changes. The aim of this review is to identify research and surveillance gaps that obscure our understanding of BT and EHD in North America. © Copyright 2015, Mary Ann Liebert, Inc.
News Article | February 4, 2016
Computer scientists and statisticians at Colorado State University are turning disease outbreak planning exercises into a game. They’re creating powerful new software that can predict, simulate and analyze a major disease outbreak — all in the form of an intuitive, multiplayer game. Researchers led by Shrideep Pallickara, associate professor of computer science in the College of Natural Sciences, are in year one of a three-year, $2.04 million Department of Homeland Security Science and Technology Directorate grant. The project is aimed at connecting the latest, greatest computing and data management technology to the fight against widespread livestock disease. Livestock disease outbreaks can spread far and fast across the U.S. From foot and mouth disease in cattle to avian influenza, the illnesses can wreak havoc on animals, the industrial food system and the economy. “When a disease breaks out, you need to know — how severe is it? How long will it last? How many field personnel do you need? What are the economic consequences? How will commodity prices be affected? What will happen if you start vaccinating?” Pallickara said. Computer scientists are used to dealing with hundreds or thousands of variables and running what-if scenarios. The Department of Homeland Security, the U.S. Department of Agriculture/Center for Epidemiology and Animal Health, and other outbreak specialists such as the Federal Emergency Management Agency, respond to emergencies by identifying a handful of scenarios. Then they can change parameters for each scenario — adjusting variables including disease biology and virulence — to help determine action plans for things like vaccine stockpiles, vaccine efficacy and deploying field personnel. But that whole process can take hours or days; meanwhile, the disease spreads. “In these cases, sometimes hours elapse between modifying your scenario, running it and getting your response back,” Pallickara said. “What we do instead is, given a national scale outbreak scenario, we generate 100,000 variants, run them in a computing cloud that generates several billion files, and then do the analytics on all this data. So, even if a user is trying to change something in real time, we have already learned what will happen. This involves a lot of back-end processing, which allows us to make real-time predictions.” Group gaming and why it works Disease planners often work in isolation and don’t understand each other’s rationale or how decisions affect one another. This project tackles that problem by enabling collaborative decisions, allowing epidemiologists and state and federal officials to work together using a unique real-time planning tool — a multiplayer computer game called “Symphony.” A single-player version called “Sonata” will be released first. Why use group gaming to plan for disease outbreaks? Because concepts tend to “stick” better when people use them in game playing, the researchers say. The idea is to put different decision makers — from policymakers to field agent and veterinarians — in each others’ shoes. The researchers envision all these constituents together in a virtual room, doing a planning exercise with the game and real-time visualizations, such as heat maps of potential danger zones. Also on the team is Sangmi Pallickara, an assistant professor in Computer Science, who is leading the big data component of the project — the management of about 1 trillion files. Jay Breidt, professor in statistics, will provide statistical models and expertise. Others on the team include a veterinary epidemiologist and an economist from Kansas State University, and a disease spread model designer. “From a computer scientist’s perspective, creating a disease outbreak planning tool introduces a host of interesting challenges,” Shrideep Pallickara said. Machine learning, statistical models and ensemble learning all become part of the process. And lots of computing power to crunch a petabyte of information over 10 million hours of CPU computing time. “It is not enough for our tool to be accurate. It has to be useful. It has to be in real time,” Pallickara said. “The players — the DHS, the USDA, CEAH, state and federal officials — need to see a response in less than 100 milliseconds.”
McClure M.L.,Conservation Science Partners |
Burdett C.L.,Colorado State University |
Farnsworth M.L.,Conservation Science Partners |
Lutman M.W.,Animal and Plant Health Inspection Service |
And 4 more authors.
PLoS ONE | Year: 2015
Wild pigs (Sus scrofa), also known as wild swine, feral pigs, or feral hogs, are one of the most widespread and successful invasive species around the world. Wild pigs have been linked to extensive and costly agricultural damage and present a serious threat to plant and animal communities due to their rooting behavior and omnivorous diet. We modeled the current distribution of wild pigs in the United States to better understand the physiological and ecological factors that may determine their invasive potential and to guide future study and eradication efforts. Using national-scale wild pig occurrence data reported between 1982 and 2012 by wildlife management professionals, we estimated the probability of wild pig occurrence across the United States using a logistic discrimination function and environmental covariates hypothesized to influence the distribution of the species. Our results suggest the distribution of wild pigs in the U.S. was most strongly limited by cold temperatures and availability of water, and that they were most likely to occur where potential home ranges had higher habitat heterogeneity, providing access to multiple key resources including water, forage, and cover. High probability of occurrence was also associated with frequent high temperatures, up to a high threshold. However, this pattern is driven by pigs' historic distribution in warm climates of the southern U.S. Further study of pigs' ability to persist in cold northern climates is needed to better understand whether low temperatures actually limit their distribution. Our model highlights areas at risk of invasion as those with habitat conditions similar to those found in pigs' current range that are also near current populations. This study provides a macro-scale approach to generalist species distribution modeling that is applicable to other generalist and invasive species. © 2015 This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
PubMed | Center for Epidemiology and Animal Health, Conservation Science Partners, Animal and Plant Health Inspection Service and Colorado State University
Type: Journal Article | Journal: PloS one | Year: 2015
Wild pigs (Sus scrofa), also known as wild swine, feral pigs, or feral hogs, are one of the most widespread and successful invasive species around the world. Wild pigs have been linked to extensive and costly agricultural damage and present a serious threat to plant and animal communities due to their rooting behavior and omnivorous diet. We modeled the current distribution of wild pigs in the United States to better understand the physiological and ecological factors that may determine their invasive potential and to guide future study and eradication efforts. Using national-scale wild pig occurrence data reported between 1982 and 2012 by wildlife management professionals, we estimated the probability of wild pig occurrence across the United States using a logistic discrimination function and environmental covariates hypothesized to influence the distribution of the species. Our results suggest the distribution of wild pigs in the U.S. was most strongly limited by cold temperatures and availability of water, and that they were most likely to occur where potential home ranges had higher habitat heterogeneity, providing access to multiple key resources including water, forage, and cover. High probability of occurrence was also associated with frequent high temperatures, up to a high threshold. However, this pattern is driven by pigs historic distribution in warm climates of the southern U.S. Further study of pigs ability to persist in cold northern climates is needed to better understand whether low temperatures actually limit their distribution. Our model highlights areas at risk of invasion as those with habitat conditions similar to those found in pigs current range that are also near current populations. This study provides a macro-scale approach to generalist species distribution modeling that is applicable to other generalist and invasive species.
Mu J.E.,Oregon State University |
McCarl B.A.,Texas A&M University |
Hagerman A.,Center for Epidemiology and Animal Health |
Bessler D.,Texas A&M University
Journal of Integrative Agriculture | Year: 2015
This paper examines the U.S. meat demand impacts of the announced outbreaks of bovine spongiform encephalopathy (BSE) and avian influenza (AI). Findings indicate that beef and chicken demand was negatively affected by BSE and AI disease outbreaks. Specifically, in the short run, U.S. consumers shift demand due to both outbreaks but more so due to domestic disease outbreaks than for outbreaks occurring overseas-the impact of U.S. AI outbreaks is about 0.5% for beef and the impact of U.S. BSE cases is around -0.42% for beef and 0.4% for pork, respectively. Regarding the BSE shock on meat demand, there is a high rate of beef demand adjusted from disturbance to the long-run equilibrium and a lower adjustment rate for chicken demand because of the repeated outbreaks of AI worldwide. In the long run, information related to severe, persistently recurring overseas animal disease outbreaks changes U.S. consumers' meat consumption patterns. Although effects of animal diseases on U.S. meat demand were statistically significant, the magnitudes were small-the impact of WHO reported human death numbers for AI is 0.005% for beef, -0.002% for pork, and -0.006% for chicken and the impact of U.S. BSE cases is 1.1% for pork and -0.7% for chicken. © 2015 Chinese Academy of Agricultural Sciences.
Holtkamp D.J.,Iowa State University |
Kliebenstein J.B.,Iowa State University |
Neumann E.J.,Massey University |
Zimmerman J.J.,Iowa State University |
And 6 more authors.
Journal of Swine Health and Production | Year: 2013
Objective: To estimate the current annual economic impact of porcine reproductive and respiratory syndrome virus (PRRSV) on the US swine industry. Materials and methods: Data for the analysis was compiled from the US Department of Agriculture, a survey of swine veterinarians on the incidence and impact of PRRSV, and production records (2005 to 2010) from commercial farms with known PRRSV status. Animal-level economic impact of productivity losses and other costs attributed to PRRSV were estimated using an enterprise budgeting approach and extrapolated to the national level on the basis of the US breedingherd inventory, number of pigs marketed, and number of pigs imported for growing. Results: The total cost of productivity losses due to PRRSV in the US national breeding and growing-pig herd was estimated at US $664 million annually, an increase from the US $560 million annual cost estimated in 2005. The 2011 study differed most significantly from the 2005 study in the allocation of losses between the breeding and the growing-pig herd. Losses in the breeding herd accounted for 12% of the total cost of PRRSV in the 2005 study, compared to 45% in the current analysis. Implications: Despite over 25 years of experience and research, porcine reproductive and respiratory syndrome remains a costly disease of pigs in the United States. Since 2005, some progress has been made in dealing with the cost of productivity losses due to the disease in the growing pig, but these were offset by greater losses in the breeding herd.
PubMed | Michigan State University, Center for Epidemiology and Animal Health and National Wildlife Research Center
Type: | Journal: Preventive veterinary medicine | Year: 2016
Direct and indirect contacts among individuals drive transmission of infectious disease. When multiple interacting species are susceptible to the same pathogen, risk assessment must include all potential host species. Bovine tuberculosis (bTB) is an example of a disease that can be transmitted among several wildlife species and to cattle, although the potential role of several wildlife species in spillback to cattle remains unclear. To better understand the complex network of contacts and factors driving disease transmission, we fitted proximity logger collars to beef and dairy cattle (n=37), white-tailed deer (Odocoileus virginianus; n=29), raccoon (Procyon lotor; n=53), and Virginia opossum (Didelphis virginiana; n=79) for 16 months in Michigans Lower Peninsula, USA. We determined inter- and intra-species direct and indirect contact rates. Data on indirect contact was calculated when collared animals visited stationary proximity loggers placed at cattle feed and water resources. Most contact between wildlife species and cattle was indirect, with the highest contact rates occurring between raccoons and cattle during summer and fall. Nearly all visits (>99%) to cattle feed and water sources were by cattle, whereas visitation to stored cattle feed was dominated by deer and raccoon (46% and 38%, respectively). Our results suggest that indirect contact resulting from wildlife species visiting cattle-related resources could pose a risk of disease transmission to cattle and deserves continued attention with active mitigation.
PubMed | Center for Epidemiology and Animal Health and Iowa State University
Type: Journal Article | Journal: Vector borne and zoonotic diseases (Larchmont, N.Y.) | Year: 2015
A cross-sectional study was performed to identify operation-level risk factors associated with prevalence of antibody to Bunyamwera (BUN) serogroup viruses in sheep in the United States. Sera were obtained from 5150 sheep in 270 operations located in 22 states (three in the west, nine central states, and 10 in the east) and tested at a dilution of 1:20 by a plaque reduction neutralization test (PRNT) using Cache Valley virus (CVV). Antibodies that neutralized CVV were identified in 1455 (28%) sheep. Animal-level seroprevalence was higher in the east (49%) than the central (17%) and western (10%) states. A convenient subset (n = 509) of sera with antibodies that neutralized CVV was titrated and further analyzed by PRNT using all six BUN serogroup viruses that occur in the United States: CVV, Lokern virus (LOKV), Main Drain virus (MDV), Northway virus (NORV), Potosi virus (POTV), and Tensaw virus (TENV). Antibodies to CVV and LOKV were identified in sheep in all three geographic regions; MDV and POTV activity was detected in the central and eastern states, NORV activity was restricted to the west, and antibodies to TENV were not detected in any sheep. Several management factors were significantly associated with the presence of antibodies to BUN serogroup viruses. For instance, sheep housed during the lambing season inside structures that contained four walls and a roof and a door closed most of the time were more likely to be seropositive than other sheep. In contrast, herded/open-range sheep were less likely to be seropositive than their counterparts. These data can be used by producers to implement strategies to reduce the likelihood of BUN serogroup virus infection and improve the health and management practices of sheep.