Bayesian Network Modeling Applied to Feline Calicivirus Infection Among Cats in Switzerland.

Bayesian network (BN) modeling is a rich and flexible analytical framework capable of elucidating complex veterinary epidemiological data. It is a graphical modeling technique that enables the visual presentation of multi-dimensional results while retaining statistical rigor in population-level inference. Using previously published case study data about feline calicivirus (FCV) and other respiratory pathogens in cats in Switzerland, a full BN modeling analysis is presented. The analysis shows that reducing the group size and vaccinating animals are the two actionable factors directly associated with FCV status and are primary targets to control FCV infection. The presence of gingivostomatitis and Mycoplasma felis is also associated with FCV status, but signs of upper respiratory tract disease (URTD) are not. FCV data is particularly well-suited to a network modeling approach, as both multiple pathogens and multiple clinical signs per pathogen are involved, along with multiple potentially interrelated risk factors. BN modeling is a holistic approach-all variables of interest may be mutually interdependent-which may help to address issues, such as confounding and collinear factors, as well as to disentangle directly vs. indirectly related variables. We introduce the BN methodology as an alternative to the classical uni- and multivariable regression approaches commonly used for risk factor analyses. We advise and guide researchers about how to use BNs as an exploratory data tool and demonstrate the limitations and practical issues. We present a step-by-step case study using FCV data along with all code necessary to reproduce our analyses in the open-source R environment. We compare and contrast the findings of the current case study using BN modeling with previous results that used classical regression techniques, and we highlight new potential insights. Finally, we discuss advanced methods, such as Bayesian model averaging, a common way of accounting for model uncertainty in a Bayesian network context.

Introducing a drift and diffusion framework for childhood growth research.

Background: Growth trajectories are highly variable between children, making epidemiological analyses challenging both to the identification of malnutrition interventions at the population level and also risk assessment at individual level. We introduce stochastic differential equation (SDE) models into child growth research. SDEs describe flexible dynamic processes comprising: drift - gradual smooth changes - such as physiology or gut microbiome, and diffusion - sudden perturbations, such as illness or infection. Methods: We present a case study applying SDE models to child growth trajectory data from the Haydom, Tanzania and Venda, South Africa sites within the MAL-ED cohort. These data comprise n=460 children aged 0-24 months. A comparison with classical curve fitting (linear mixed models) is also presented. Results: The SDE models offered a wide range of new flexible shapes and parameterizations compared to classical additive models, with performance as good or better than standard approaches. The predictions from the SDE models suggest distinct longitudinal clusters that form distinct 'streams' hidden by the large between-child variability. Conclusions: Using SDE models to predict future growth trajectories revealed new insights in the observed data, where trajectories appear to cluster together in bands, which may have a future risk assessment application. SDEs offer an attractive approach for child growth modelling and potentially offer new insights.

The current and future burden of late-onset dementia in the United Kingdom: Estimates and interventions.

INTRODUCTION: We estimate the burden of late-onset dementia in the United Kingdom through to 2025 and assess the impact of potential interventions. METHODS: We compute disability adjusted life years (DALYs) through to 2025 and consider three interventions, all assumed launched in 2018; (1) an optimistic limiting case of a 100% preventive intervention with immediate uptake of 100% of the population at risk; (2) an intervention which delays onset by 5 years, linear uptake to 50% after 5 years; (3) as (2) but uptake 75% after 5 years. RESULTS: By 2025, the DALY burden will have increased by 42% from the Global Disease Burden 2010 estimate. Intervention results: (1) a 9% decrease by 2025; (2) a 33% increase; and (3) a 28% increase. DISCUSSION: At current prevalence rates, the ability of an intervention to offset the projected increase in DALY burden of dementia in the United Kingdom by 2025 appears low.

Latent porcine circovirus type 2-infected domestic pigs: A potential infection model for the effective development of vaccines against latent or chronic virus induced diseases.

Until recently, knowledge of the pathogenicity of Circoviridae and Anelloviridae family members was limited. Our previous discoveries provided clues toward resolving this issue based on studies of the latent nature of porcine circovirus type 2 (PCV2) genotype group members. We developed a conventional pig infection model that indicated that weaners already harbored latent PCV2 infection in the thymus, which enabled the viruses to specifically modulate the maturation of T-helper cells. This finding raised the possibility that the thymi of normal fetuses were already infected with PCV2. The present findings further substantiate our hypothesis that PCV2 masquerades as the host by infecting fetuses before they acquire immune-competence. We provide the first demonstration that all domestic pig fetuses preferentially harbor latent PCV2-infected cells in their thymi. These PCV2-infected cells are different from thymocytes and are located in the medulla of the fetal thymus. These latent PCV2-infected cells in fetuses are found at the same location and share characteristics with the infected cells observed in adolescent pigs. Moreover, fetuses also harbor these infected cells in other lymph system organs. We provide the first demonstration that the fetal thymus virus pools are minimally affected by sow vaccination, highlighting the immune-privileged character of this organ. Furthermore, we found a striking reduction in virus-infected cells in the fetal spleen and an increase in PCV2-infected cells in the fetal intestine of anti-PCV2-vaccinated mothers. These data indicate that specific immune response interactions occur between mothers and their progeny that are not dependent on the humoral immunity of the mother and cannot be attributed to the rudimentary humoral responses of the fetuses because these pig fetuses do not have any PCV2-specific antibodies. These shifts in our understanding of the PCV2-infected cell pool will lead to different avenues in the search for effective vaccination strategies against latent and chronic pathogens.

T-cell reprogramming through targeted CD4-coreceptor and T-cell receptor expression on maturing thymocytes by latent Circoviridae family member porcine circovirus type 2 cell infections in the thymus.

Although porcine circovirus type 2 (PCV2)-associated diseases have been evaluated for known immune evasion strategies, the pathogenicity of these viruses remained concealed for decades. Surprisingly, the same viruses that cause panzootics in livestock are widespread in young, unaffected animals. Recently, evidence has emerged that circovirus-like viruses are also linked to complex diseases in humans, including children. We detected PCV2 genome-carrying cells in fetal pig thymi. To elucidate virus pathogenicity, we developed a new pig infection model by in vivo transfection of recombinant PCV2 and the immunosuppressant cofactor cyclosporine A. Using flow cytometry, immunofluorescence and fluorescence in situ hybridization, we found evidence that PCV2 dictates positive and negative selection of maturing T cells in the thymus. We show for the first time that PCV2-infected cells reside at the corticomedullary junction of the thymus. In diseased animals, we found polyclonal deletion of single positive cells (SPs) that may result from a loss of major histocompatibility complex class-II expression at the corticomedullary junction. The percentage of PCV2 antigen-presenting cells correlated with the degree of viremia and, in turn, the severity of the defect in thymocyte maturation. Moreover, the reversed T-cell receptor/CD4-coreceptor expression dichotomy on thymocytes at the CD4(+)CD8(interm) and CD4SP cell stage is viremia-dependent, resulting in a specific hypo-responsiveness of T-helper cells. We compare our results with the only other better-studied member of Circoviridae, chicken anemia virus. Our data show that PCV2 infection leads to thymocyte selection dysregulation, adding a valuable dimension to our understanding of virus pathogenicity.

Dynamics of the force of infection: insights from Echinococcus multilocularis infection in foxes.

Characterizing the force of infection (FOI) is an essential part of planning cost effective control strategies for zoonotic diseases. Echinococcus multilocularis is the causative agent of alveolar echinococcosis in humans, a serious disease with a high fatality rate and an increasing global spread. Red foxes are high prevalence hosts of E. multilocularis. Through a mathematical modelling approach, using field data collected from in and around the city of Zurich, Switzerland, we find compelling evidence that the FOI is periodic with highly variable amplitude, and, while this amplitude is similar across habitat types, the mean FOI differs markedly between urban and periurban habitats suggesting a considerable risk differential. The FOI, during an annual cycle, ranges from (0.1,0.8) insults (95% CI) in urban habitat in the summer to (9.4, 9.7) (95% CI) in periurban (rural) habitat in winter. Such large temporal and spatial variations in FOI suggest that control strategies are optimal when tailored to local FOI dynamics.

Transarticular fixation with cortical screws combined with dorsal laminectomy and partial discectomy as surgical treatment of degenerative lumbosacral stenosis in 17 dogs: clinical and computed tomography follow-up.

OBJECTIVE: To describe clinical outcome and technical outcome assessed using computed tomography (CT) in dogs with degenerative lumbosacral stenosis (DLSS) treated by dorsal laminectomy, partial discectomy, and transarticular screw fixation. STUDY DESIGN: Retrospective observational case series. ANIMALS: Dogs with DLSS (n = 17). METHODS: Dogs with neurologic and magnetic resonance imaging (MRI) findings compatible with DLSS treated by dorsal laminectomy, partial discectomy and transarticular screw fixation were enrolled. Pre- and postoperative neurologic status was compared. Lumbosacral (LS) angle in extension and misalignment in preoperative MRI were compared with the postoperative CT. Residual mobility of the LS joint after fixation was also evaluated. Status of screws, presence of new bone formation over screw heads/articular facets and presence of adjacent segment disease (ASD) were assessed. RESULTS: Median CT follow-up was 12 months. Clinical improvement was seen in 13 dogs, 2 dogs had intermittent LS pain, and 2 dogs needed revision surgery. In 5 dogs, screws were either pulled out or broken. Reduction of LS angle in extension and misalignment was achieved. Residual mobility of the LS segment was present and ASD was not recognized. CONCLUSIONS: Transarticular screw fixation in dogs with DLSS is associated with a considerable number of technical failures and does not result in rigid stabilization; however, this did not significantly adversely influence clinical outcome.

Applying Bayesian network modelling to understand the links between on-farm biosecurity practice during the 2007 equine influenza outbreak and horse managers' perceptions of a subsequent outbreak.

Australia experienced its first ever outbreak of equine influenza in August 2007. Horses on 9359 premises were infected over a period of 5 months before the disease was successfully eradicated through the combination of horse movement controls, on-farm biosecurity and vaccination. In a previous premises-level case-control study of the 2007 equine influenza outbreak in Australia, the protective effect of several variables representing on-farm biosecurity practices were identified. Separately, factors associated with horse managers' perceptions of the effectiveness of biosecurity measures have been identified. In this analysis we applied additive Bayesian network modelling to describe the complex web of associations linking variables representing on-farm human behaviours during the 2007 equine influenza outbreak (compliance or lack thereof with advised personal biosecurity measures) and horse managers' perceptions of the effectiveness of such measures in the event of a subsequent outbreak. Heuristic structure discovery enabled identification of a robust statistical model for 31 variables representing biosecurity practices and perceptions of the owners and managers of 148 premises. The Bayesian graphical network model we present statistically describes the associations linking horse managers' on-farm biosecurity practices during an at-risk period in the 2007 outbreak and their perceptions of whether such measures will be effective in a future outbreak. Practice of barrier infection control measures were associated with a heightened perception of preparedness, whereas horse managers that considered their on-farm biosecurity to be more stringent during the outbreak period than normal practices had a heightened perception of the effectiveness of other measures such as controlling access to the premises. Past performance in an outbreak setting may indeed be a reliable predictor of future perceptions, and should be considered when targeting infection control guidance to horse owners and managers.

Improving epidemiologic data analyses through multivariate regression modelling.

: Regression modelling is one of the most widely utilized approaches in epidemiological analyses. It provides a method of identifying statistical associations, from which potential causal associations relevant to disease control may then be investigated. Multivariable regression - a single dependent variable (outcome, usually disease) with multiple independent variables (predictors) - has long been the standard model. Generalizing multivariable regression to multivariate regression - all variables potentially statistically dependent - offers a far richer modelling framework. Through a series of simple illustrative examples we compare and contrast these approaches. The technical methodology used to implement multivariate regression is well established - Bayesian network structure discovery - and while a relative newcomer to the epidemiological literature has a long history in computing science. Applications of multivariate analysis in epidemiological studies can provide a greater understanding of disease processes at the population level, leading to the design of better disease control and prevention programs.

Identifying associations in Escherichia coli antimicrobial resistance patterns using additive Bayesian networks.

While the genesis of antimicrobial resistance (AMR) in animal production is a high profile topic in the media and the scientific community, it is still not well understood. The epidemiology of AMR is complex. This complexity is demonstrated by extensive biological and evolutionary mechanisms which are potentially impacted by farm management and husbandry practices - the risk factors. Many parts of this system have yet to be fully described. Notably, the occurrence of multiple resistance patterns is the rule rather than exception - the multivariate problem. A first essential step in the development of any comprehensive risk factor analysis - whose goal is the prevention or reduction of AMR - is to describe those associations between different patterns of resistance which are systematic. That is, have sufficient statistical support for these patterns to be considered robust features of the underlying epidemiological system, and whose presence must therefore be incorporated into any risk factor analysis of AMR for it to be meaningful with respect to the farm environment. Presented here is a case study that seeks to identify systematic associations between patterns of resistance to 13 different antimicrobials in Escherichia coli isolates obtained from composite finisher (>80 kg) pig faecal samples obtained from Canada's five major pork producing provinces. The use of a Bayesian network analysis approach allowed us to identify many systematic associations between individual antimicrobial resistances. Sixteen of these resistances are corroborated with existing literature. These associations are distributed between several important classes of antimicrobials including the ß-lactams, folate biosynthesis inhibitors, tetracyclines, aminoglycosides and quinolones. This study presents an exciting first step towards the larger and far more ambitious goal of developing generic and holistic risk factor analyses for on-farm occurrence of AMR. Analyses of this nature would combine multivariate response variables (joint patterns of resistance) with multi-factorial causal factors from within the livestock production environment thereby permitting a more complete understanding of the epidemiology of antimicrobial resistance.

Untangling the complex inter-relationships between horse managers' perceptions of effectiveness of biosecurity practices using Bayesian graphical modelling.

On-farm biosecurity practices have been promoted in many animal industries to protect animal populations from infections. Current approaches based on regression modelling techniques for assessing biosecurity perceptions and practices are limited for analysis of the interrelationships between multivariate data. A suitable approach, which does not require background knowledge of relationships, is provided by Bayesian network modelling. Here we apply such an approach to explore the complex interrelationships between the variables representing horse managers' perceptions of effectiveness of on-farm biosecurity practices. The dataset was derived from interviews conducted with 200 horse managers in Australia after the 2007 equine influenza outbreak. Using established computationally intensive techniques, an optimal graphical statistical model was identified whose structure was objectively determined, directly from the observed data. This methodology is directly analogous to multivariate regression (i.e. multiple response variables). First, an optimal model structure was identified using an exact (exhaustive) search algorithm, followed by pruning the selected model for over-fitting by the parametric bootstrapping approach. Perceptions about effectiveness of movement restrictions and access control were linked but were generally segregated from the perceptions about effectiveness of personal and equipment hygiene. Horse managers believing in the effectiveness of complying with movement restrictions in stopping equine influenza spread onto their premises were also more likely to believe in the effectiveness of reducing their own contact with other horses and curtailing professional visits. Similarly, the variables representing the effectiveness of disinfecting vehicles, using a disinfectant footbath, changing into clean clothes on arrival at the premises and washing hands before contact with managed horses were clustered together. In contrast, horse managers believing in the effectiveness of disinfecting vehicles (hygiene measure) were less likely to believe in the effectiveness of controlling who has access to managed horses (access control). The findings of this analysis provide new insights into the relationships between perceptions of effectiveness of different biosecurity measures. Different extension education strategies might be required for horse managers believing more strongly in the effectiveness of access control or hygiene measures.

Understanding the associations between on-farm biosecurity practice and equine influenza infection during the 2007 outbreak in Australia.

In a previous premises-level case-control study of the 2007 equine influenza outbreak in Australia, the protective effect of several variables representing on-farm biosecurity practices was identified. However, using logistic regression it was not possible to definitively identify individual effects and associations between each of the personal biosecurity measures implemented by horse premises owners and managers in the face of the outbreak. In this study we apply Bayesian network modelling to identify the complex web of associations between these variables, horse premises infection status and other premises-level covariates. We focussed this analysis primarily on the inter-relationship between the nine variables representing on-farm personal biosecurity measures (of people residing on the premises and those visiting), and all other variables from the final logistic regression model of our previous analysis. Exact structure discovery was used to identify the globally optimal model from across the landscape of all directed acyclic graphs possible for our dataset. Bootstrapping was used to adjust the model for over-fitting. Our final Bayesian graphic network model included 18 variables linked by 23 arcs, each arc analogous to a single multivariable generalised linear model, combined in a probabilistically coherent way. Amongst the personal biosecurity measures, having a footbath in place, certain practices of visitors (hand-washing, changing clothes and shoes) in contact with the horses, and the regularity of horse handling were statistically associated with premises infection status. The results of this in-depth analysis provide new insight into the complex web of direct and indirect associations between risk factors and horse premises infection status during the first 7 weeks of the 2007 equine influenza outbreak in Australia. In future outbreaks, unnecessary contact and handling of horses should be avoided, especially by those coming from off the premises. Prior to any such contact, persons handling horses should use a footbath (if present), change their clothes and shoes, and wash their hands.

Assessment of diagnostic accuracy of a commercial ELISA for the detection of Toxoplasma gondii infection in pigs compared with IFAT, TgSAG1-ELISA and Western blot, using a Bayesian latent class approach.

Serological methods are the most commonly used diagnostic tools to detect Toxoplasma gondii infections in pigs. In the absence of a readily available 'gold standard', an estimation of diagnostic accuracy is difficult to assess. A commercial ELISA (PrioCHECK® Toxoplasma Ab porcine ELISA, Prionics, Schlieren, Switzerland) for the diagnosis of T. gondii infection in pigs was evaluated in naturally infected animals from two distinct populations; indoor and outdoor living animals. An assessment of diagnostic accuracy, using a Bayesian latent class approach with adjustment for within indoor and outdoor farm clustering using random effects, was performed. Tests used for comparison were: IFAT; ELISA using native affinity-purified P30 (SAG1) T. gondii tachyzoite surface antigen (TgSAG1-ELISA); and Western blot with T. gondii tachyzoites lysate. The data set comprised 297 pig serum samples across outdoor (n=149) and indoor (n=148) farms in Argentina. The estimated sensitivity and specificity for the commercial ELISA were 98.9% (95% credible interval: 96.2; 100) and 92.7% (95% credible interval: 87.7; 96.6), respectively. The analysis of sera and plasma from pigs (n=6) experimentally inoculated with 5,000 T. gondii oocysts revealed a pronounced antibody response beginning 2 weeks p.i. until the end of the observation period (11 weeks p.i.) in all animals. Meat juice obtained from inoculated animals after euthanasia also tested positive. These results suggest that the PrioCHECK® Toxoplasma Ab porcine ELISA may be a useful tool to perform serological diagnosis of T. gondii infections in pigs to control Toxoplasma infection in pigs and humans.

Investigating the impact of fasciolosis on cattle carcase performance.

Liver fluke is a manifestation of bovine fasciolosis and its presence is compulsorily investigated as part of routine official abattoir inspections. It is known that the presence of fasciolosis negatively influences beef production, interfering with weight gain and fertility. Recent reports suggest increased occurrence of this parasite worldwide. This paper aims to investigate the impact of fasciolosis on beef cattle performance by examining the association of liver fluke with carcase characteristics and its value. Cattle slaughtered between 2005 and 2010 (328, 137 cattle (of which 12.6% were positive to liver fluke) sourced from 2278 farms) are analysed adjusting for the effect of gender, age, breed, season and year. Carcases with liver fluke have lower cold weight than those carcases free of fluke, estimated coefficient -0.63 kg (95% confidence intervals (CI) -0.93, -0.33). Carcases with liver fluke have lower price than those carcases free of fluke, estimated coefficient -£1.5 (95% CI -2.24, -0.74). The presence of liver fluke is associated with lower carcase conformation scores compared to carcases with fluke absence, proportional odds ratio (POR) 0.89 (95% CI 0.87-0.91). Similarly, the presence of liver fluke is associated with lower levels of fat in the carcase compared to carcases with fluke absence, POR 0.97 (95% CI 0.95-0.99). These results indicate a potential negative effect of the parasitism on carcase performance. The downgrading of the carcase impacts its value and therefore the price paid to the farmer. Both farmers and abattoir operators share a common interest in the control of fasciolosis in order to optimise the profitability of beef production. This study shows the utility of abattoir post-mortem inspection as a tool to monitoring animal health and production.

A tutorial in estimating the prevalence of disease in humans and animals in the absence of a gold standard diagnostic.

: Epidemiological methods for estimating disease prevalence in humans and other animals in the absence of a gold standard diagnostic test are well established. Despite this, reporting apparent prevalence is still standard practice in public health studies and disease control programmes, even though apparent prevalence may differ greatly from the true prevalence of disease. Methods for estimating true prevalence are summarized and reviewed. A computing appendix is also provided which contains a brief guide in how to easily implement some of the methods presented using freely available software.

Revealing the complexity of health determinants in resource-poor settings.

An epidemiologic systems analysis of diarrhea in children in Pakistan is presented. Application of additive Bayesian network modeling to 2005-2006 data from the Pakistan Social and Living Standards Measurement Survey reveals the complexity of child diarrhea as a disease system. The key distinction between standard analytical approaches, such as multivariable regression, and Bayesian network analyses is that the latter attempt to not only identify statistically associated variables but also, additionally and empirically, separate these into those directly and indirectly dependent upon the outcome variable. Such discrimination is vastly more ambitious but has the potential to reveal far more about key features of complex disease systems. Additive Bayesian network analyses across 41 variables from the Pakistan Social and Living Standards Measurement Survey identified 182 direct dependencies but with only 3 variables: 1) access to a dry pit latrine (protective; odds ratio = 0.67); 2) access to an atypical water source (protective; odds ratio = 0.49); and 3) no formal garbage collection (unprotective; odds ratio = 1.32), supported as directly dependent with the presence of diarrhea. All but 2 of the remaining variables were also, in turn, directly or indirectly dependent upon these 3 key variables. These results are contrasted with the use of a standard approach (multivariable regression).

Identifying associations between pig pathologies using a multi-dimensional machine learning methodology.

BACKGROUND: Abattoir detected pathologies are of crucial importance to both pig production and food safety. Usually, more than one pathology coexist in a pig herd although it often remains unknown how these different pathologies interrelate to each other. Identification of the associations between different pathologies may facilitate an improved understanding of their underlying biological linkage, and support the veterinarians in encouraging control strategies aimed at reducing the prevalence of not just one, but two or more conditions simultaneously. RESULTS: Multi-dimensional machine learning methodology was used to identify associations between ten typical pathologies in 6485 batches of slaughtered finishing pigs, assisting the comprehension of their biological association. Pathologies potentially associated with septicaemia (e.g. pericarditis, peritonitis) appear interrelated, suggesting on-going bacterial challenges by pathogens such as Haemophilus parasuis and Streptococcus suis. Furthermore, hepatic scarring appears interrelated with both milk spot livers (Ascaris suum) and bacteria-related pathologies, suggesting a potential multi-pathogen nature for this pathology. CONCLUSIONS: The application of novel multi-dimensional machine learning methodology provided new insights into how typical pig pathologies are potentially interrelated at batch level. The methodology presented is a powerful exploratory tool to generate hypotheses, applicable to a wide range of studies in veterinary research.

The contribution of simple random sampling to observed variations in faecal egg counts.

It has been over 100 years since the classical paper published by Gosset in 1907, under the pseudonym "Student", demonstrated that yeast cells suspended in a fluid and measured by a haemocytometer conformed to a Poisson process. Similarly parasite eggs in a faecal suspension also conform to a Poisson process. Despite this there are common misconceptions how to analyse or interpret observations from the McMaster or similar quantitative parasitic diagnostic techniques, widely used for evaluating parasite eggs in faeces. The McMaster technique can easily be shown from a theoretical perspective to give variable results that inevitably arise from the random distribution of parasite eggs in a well mixed faecal sample. The Poisson processes that lead to this variability are described and illustrative examples of the potentially large confidence intervals that can arise from observed faecal eggs counts that are calculated from the observations on a McMaster slide. Attempts to modify the McMaster technique, or indeed other quantitative techniques, to ensure uniform egg counts are doomed to failure and belie ignorance of Poisson processes. A simple method to immediately identify excess variation/poor sampling from replicate counts is provided.

Network modeling of BVD transmission.

Endemic diseases of cattle, such as bovine viral diarrhea, have significant impact on production efficiency of food of animal origin with consequences for animal welfare and climate change reduction targets. Many modeling studies focus on the local scale, examining the on-farm dynamics of this infectious disease. However, insight into prevalence and control across a network of farms ultimately requires a network level approach. Here, we implement understanding of infection dynamics, gained through these detailed on-farm modeling studies, to produce a national scale model of bovine viral diarrhea virus transmission. The complex disease epidemiology and on-farm dynamics are approximated using SIS dynamics with each farm treated as a single unit. Using a top down approach, we estimate on-farm parameters associated with contraction and subsequent clearance from infection at herd level. We examine possible control strategies associated with animal movements between farms and find measures targeted at a small number of high-movement farms efficient for rapid and sustained prevalence reduction.