A statistical framework for calculating prospective sample sizes and classifying efficacy results for faecal egg count reduction tests in ruminants, horses and swine.

The faecal egg count reduction test (FECRT) is the primary diagnostic tool used for detecting anthelmintic resistance at the farm level. It is therefore extremely important that the experimental design of a FECRT and the susceptibility classification of the result use standardised and statistically rigorous methods. Several different approaches for improving the analysis of FECRT data have been proposed, but little work has been published on how to address the issue of prospective sample size calculations. Here, we provide a complete and detailed overview of the quantitative issues relevant to a FECRT starting from basic statistical principles. We then present a new approach for determining sample size requirements for the FECRT that is built on a solid statistical framework, and provide a rigorous anthelminthic drug efficacy classification system for use with FECRT in livestock. Our approach uses two separate statistical tests, a one-sided inferiority test for resistance and a one-sided non-inferiority test for susceptibility, and determines a classification of resistant, susceptible or inconclusive based on the combined result. Since this approach is based on two independent one-sided tests, we recommend that a 90 % CI be used in place of the historically used 95 % CI. This maintains the desired Type I error rate of 5 %, and simultaneously reduces the required sample size. We demonstrate the use of this framework to provide sample size calculations that are rooted in the well-understood concept of statistical power. Tailoring to specific host/parasite systems is possible using typical values for expected pre-treatment and post-treatment variability in egg counts as well as within-animal correlation in egg counts. We provide estimates for these parameters for ruminants, horses and swine based on a re-examination of datasets that were available to us from a combination of published data and other sources. An illustrative example is provided to demonstrate the use of the framework, and parameter estimates are presented to estimate the required sample size for a hypothetical FECRT using ivermectin in cattle. The sample size calculation method and classification framework presented here underpin the sample size recommendations provided in the upcoming FECRT WAAVP guidelines for detection of anthelmintic resistance in ruminants, horses, and swine, and have also been made freely available as open-source software via our website (https://www.fecrt.com).

The consequential costs of bovine tuberculosis (bTB) breakdowns in England and Wales.

Bovine tuberculosis (bTB) is a globally distributed zoonotic disease with significant economic impacts. Control measures in Great Britain include testing for and culling diseased animals. Farmers receive compensation for the value of culled animals, but not for the consequential costs of having to comply with testing and associated control measures. Such uncompensated costs can be significant. We present results of a survey of 1,600 dairy and beef farm holdings conducted in England and Wales to update and improve estimates of these consequential costs.Estimated costs are positively skewed and show considerable variance, which is in agreement with previous, smaller scale surveys of bTB: most farms experiencing bTB incur modest costs but some suffer significant costs. Testing, movement restrictions and output losses account for over three quarters of total uncompensated costs. Total costs rise with herd size and duration of controls. The composition of consequential costs changes as total costs increase, with an increasing proportion of the costs being associated with output losses and movement restrictions, and a decreasing proportion of costs associated with testing costs. Consequential costs tend to be higher for dairy than beef herds but this is likely due to larger herd sizes for dairy.Overall we find the total farm costs of bTB surpass those compensated for by Government in Great Britain. This study contributes to the public-private cost-sharing debate as farmers bear some of the economic burden of a disease breakdown. The methodology and results presented are crucial for informed Government and farmer decision-making. The identification of potential risk factors in this study was challenging but is of relevance outside GB.

Patterns of mortality in domesticated ruminants in Ethiopia.

Background: Premature death of livestock is a problem in all ruminant production systems. While the number of premature ruminant deaths in a country is a reasonable indicator for the nation's health, few data sources exist in a country like Ethiopia that can be used to generate valid estimates. The present study aimed to establish if three different data sets, each with imperfect information on ruminant mortality, including abortions, could be combined into improved estimates of nationwide mortality in Ethiopia. Methods: We combined information from a recent survey of ruminant mortality with information from the Living Standards Measurement Study and the Disease Outbreak and Vaccination Reporting dataset. Generalized linear mixed and hurdle models were used for data analysis, with results summarized using predicted outcomes. Results: Analyses indicated that most herds experienced zero mortality and reproductive losses, with rare occasions of larger losses. Diseases causing deaths varied greatly both geographically and over time. There was little agreement between the different datasets. While the models aid the understanding of patterns of mortality and reproductive losses, the degree of variation observed limited the predictive scope. Conclusions: The models revealed some insight into why mortality rates are variable over time and are therefore less useful in measuring production or health status, and it is suggested that alternative measures of productivity, such as number of offspring raised to 1 year old per dam, would be more stable over time and likely more indicative.

FAIR data pipeline: provenance-driven data management for traceable scientific workflows.

Modern epidemiological analyses to understand and combat the spread of disease depend critically on access to, and use of, data. Rapidly evolving data, such as data streams changing during a disease outbreak, are particularly challenging. Data management is further complicated by data being imprecisely identified when used. Public trust in policy decisions resulting from such analyses is easily damaged and is often low, with cynicism arising where claims of 'following the science' are made without accompanying evidence. Tracing the provenance of such decisions back through open software to primary data would clarify this evidence, enhancing the transparency of the decision-making process. Here, we demonstrate a Findable, Accessible, Interoperable and Reusable (FAIR) data pipeline. Although developed during the COVID-19 pandemic, it allows easy annotation of any data as they are consumed by analyses, or conversely traces the provenance of scientific outputs back through the analytical or modelling source code to primary data. Such a tool provides a mechanism for the public, and fellow scientists, to better assess scientific evidence by inspecting its provenance, while allowing scientists to support policymakers in openly justifying their decisions. We believe that such tools should be promoted for use across all areas of policy-facing research. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

The challenges of data in future pandemics.

The use of data has been essential throughout the unfolding COVID-19 pandemic. We have needed it to populate our models, inform our understanding, and shape our responses to the disease. However, data has not always been easy to find and access, it has varied in quality and coverage, been difficult to reuse or repurpose. This paper reviews these and other challenges and recommends steps to develop a data ecosystem better able to deal with future pandemics by better supporting preparedness, prevention, detection and response.

Efficacy of high-throughput transthoracic ultrasonographic screening for on-farm detection of ovine pulmonary adenocarcinoma.

BACKGROUND: The aim of this study was to evaluate the efficacy of high-throughput on-farm transthoracic ultrasound (TUS) to screen for ovine pulmonary adenocarcinoma (OPA), an infectious ovine disease of increasing concern. No other routine diagnosis of preclinical OPA is available, or any vaccine or treatment. METHODS: More than 80,000 rapid TUS scans were applied on farms with a history of OPA. The TUS results from a convenience sample of 171 TUS-negative and 269 TUS-positive sheep were compared with postmortem histology/immunohistochemistry results, the 'gold standard' reference test for OPA diagnosis. These results, together with new data on within-flock prevalence, allowed estimation of the efficacy of rapid TUS screening to identify OPA (defined as tumours of larger than 1 cm) on-farm. RESULTS: The TUS screening had an estimated specificity of 0.998 (95% confidence interval [CI]: 0.998-0.999) and an estimated sensitivity of between 0.76 (95% CI: 0.72-0.79) and 0.99 (95% CI: 0.97-0.99) depending on the presumed false-negative rate applied to the calculation. CONCLUSION: High-throughput TUS should be considered for screening to identify individual sheep with OPA and has potential application to indicate flocks at low risk of OPA. However, lower efficacy is likely if conducted by less experienced persons.

A Tool for Prioritizing Livestock Disease Threats to Scotland.

There are a number of disease threats to the livestock of Scotland that are not presently believed to be circulating in the UK. Here, we present the development of a tool for prioritizing resources for livestock disease threats to Scotland by combining a semi-quantitative model of the chance of introduction of different diseases with a semi-quantitative model of disease impact. Eighteen key diseases were identified and then input into a model framework to produce a semi-quantitative estimate of disease priorities. We estimate this through a model of the potential impacts of the infectious diseases in Scotland that is interpreted alongside a pre-existing generic risk assessment model of the risks of incursion of the diseases. The impact estimates are based on key metrics which influence the practical impact of disease. Metrics included are the rate of spread, the disease mitigation factors, impacts on animal welfare and production, the human health risks and the impacts on wider society. These quantities were adjusted for the size of the Scottish livestock population and were weighted using published scores. Of the 18 livestock diseases included, the model identifies highly pathogenic avian influenza, foot and mouth disease in cattle and bluetongue virus in sheep as having the greatest priority in terms of the combination of chance of introduction and disease impact. Disregarding the weighting for livestock populations and comparing equally between industry sectors, the results demonstrate that Newcastle disease and highly pathogenic avian influenza generally have the greatest potential impact. This model provides valuable information for the veterinary and livestock industries in prioritizing resources in the face of many disease threats. The system can easily be adjusted as disease situations evolve.

Evaluating Diagnostic Tests With Near-Perfect Specificity: Use of a Hui-Walter Approach When Designing a Trial of a DIVA Test for Bovine Tuberculosis.

Active surveillance of rare infectious diseases requires diagnostic tests to have high specificity, otherwise the false positive results can outnumber the true cases detected, leading to low positive predictive values. Where a positive result can have economic consequences, such as the cull of a bovine Tuberculosis (bTB) positive herd, establishing a high specificity becomes particularly important. When evaluating new diagnostic tests against a "gold standard" reference test with assumed perfect sensitivity and specificity, calculation of sample sizes are commonly done using a normal approximation to the binomial distribution, although this approach can be misleading. As the expected specificity of the evaluated diagnostic test nears 100%, the errors arising from this approximation are appreciable. Alternatively, it is straightforward to calculate the sample size by using more appropriate confidence intervals, while precisely quantifying the effect of sampling variability using the binomial distribution. However, regardless of the approach, if specificity is high the sample size required becomes large, and the gold standard may be prohibitively costly. An alternative to a gold standard test is to use at least two imperfect, conditionally independent tests, and to analyse the results using a variant of the approach initially proposed by Hui and Walter. We show how this method performs for tests with near-perfect specificity; in particular we show that the sample size required to deliver useful bounds on the precision becomes very large for both approaches. We illustrate these concepts using simulation studies carried out to support the design of a trial of a bTB vaccine and a diagnostic that is able to "Differentiate Infected and Vaccinated Animals" (DIVA). Both test characteristics and the efficacy of the bTB vaccine will influence the sample size required for the study. We propose an improved methodology using a two stage approach to evaluating diagnostic tests in low disease prevalence populations. By combining an initial gold standard pilot study with a larger study analyzed using a Hui-Walter approach, the sample size required for each study can be reduced and the precision of the specificity estimate improved, since information from both studies is combined.

Animal Health Surveillance in Scotland in 2030: Using Scenario Planning to Develop Strategies in the Context of "Brexit".

Animal health surveillance is necessary to protect human and animal health, rural economies, and the environment from the consequences of large-scale disease outbreaks. In Scotland, since the Kinnaird review in 2011, efforts have been made to engage with stakeholders to ensure that the strategic goals of surveillance are better aligned with the needs of the end-users and other beneficiaries. The aims of this study were to engage with Scottish surveillance stakeholders and multidisciplinary experts to inform the future long-term strategy for animal health surveillance in Scotland. In this paper, we describe the use of scenario planning as an effective tool for the creation and exploration of five plausible long-term futures; we describe prioritization of critical drivers of change (i.e., international trade policy, data-sharing philosophies, and public versus private resourcing of surveillance capacity) that will unpredictably influence the future implementation of animal health surveillance activities. We present 10 participant-developed strategies to support 3 long-term visions to improve future resilience of animal health surveillance and contingency planning for animal and zoonotic disease outbreaks in Scotland. In the absence of any certainty about the nature of post-Brexit trade agreements for agriculture, participants considered the best investments for long-term resilience to include data collection strategies to improve animal health benchmarking, user-benefit strategies to improve digital literacy in farming communities, and investment strategies to increase veterinary and scientific research capacity in rural areas. This is the first scenario planning study to explore stakeholder beliefs and perceptions about important environmental, technological, societal, political, and legal drivers (in addition to epidemiological "risk factors") and effective strategies to manage future uncertainties for both the Scottish livestock industry and animal health surveillance after Brexit. This insight from stakeholders is important to improve uptake and implementation of animal heath surveillance activities and the future resilience of the livestock industry. The conclusions drawn from this study are applicable not only to Scotland but to other countries and international organizations involved in global animal health surveillance activities.

Combining Slaughterhouse Surveillance Data with Cattle Tracing Scheme and Environmental Data to Quantify Environmental Risk Factors for Liver Fluke in Cattle.

Liver fluke infection causes serious disease (fasciolosis) in cattle and sheep in many regions of the world, resulting in production losses and additional economic consequences due to condemnation of the liver at slaughter. Liver fluke depends on mud snails as an intermediate host and infect livestock when ingested through grazing. Therefore, environmental factors play important roles in infection risk and climate change is likely to modify this. Here, we demonstrate how slaughterhouse data can be integrated with other data, including animal movement and climate variables to identify environmental risk factors for liver fluke in cattle in Scotland. We fitted a generalized linear mixed model to the data, with exposure-weighted random and fixed effects, an approach which takes into account the amount of time cattle spent at different locations, exposed to different levels of risk. This enabled us to identify an increased risk of liver fluke with increased animal age, rainfall, and temperature and for farms located further to the West, in excess of the risk associated with a warmer, wetter climate. This model explained 45% of the variability in liver fluke between farms, suggesting that the unexplained 55% was due to factors not included in the model, such as differences in on-farm management and presence of wet habitats. This approach demonstrates the value of statistically integrating routinely recorded slaughterhouse data with other pre-existing data, creating a powerful approach to quantify disease risks in production animals. Furthermore, this approach can be used to better quantify the impact of projected climate change on liver fluke risk for future studies.

Model-Based Policymaking: A Framework to Promote Ethical "Good Practice" in Mathematical Modeling for Public Health Policymaking.

Mathematical models are increasingly relied upon as decision support tools, which estimate risks and generate recommendations to underpin public health policies. However, there are no formal agreements about what constitutes professional competencies or duties in mathematical modeling for public health. In this article, we propose a framework to evaluate whether mathematical models that assess human and animal disease risks and control strategies meet standards consistent with ethical "good practice" and are thus "fit for purpose" as evidence in support of policy. This framework is derived from principles of biomedical ethics: independence, transparency (autonomy), beneficence/non-maleficence, and justice. We identify ethical risks associated with model development and implementation and consider the extent to which scientists are accountable for the translation and communication of model results to policymakers so that the strengths and weaknesses of the scientific evidence base and any socioeconomic and ethical impacts of biased or uncertain predictions are clearly understood. We propose principles to operationalize a framework for ethically sound model development and risk communication between scientists and policymakers. These include the creation of science-policy partnerships to mutually define policy questions and communicate results; development of harmonized international standards for model development; and data stewardship and improvement of the traceability and transparency of models via a searchable archive of policy-relevant models. Finally, we suggest that bespoke ethical advisory groups, with relevant expertise and access to these resources, would be beneficial as a bridge between science and policy, advising modelers of potential ethical risks and providing overview of the translation of modeling advice into policy.

Scenario planning: The future of the cattle and sheep industries in Scotland and their resiliency to disease.

In this paper, we present a description of foresighting activities undertaken by EPIC, Scotland's Centre of Expertise on Animal Disease Outbreaks, to investigate the future uncertainty of animal health security in the Scottish sheep and cattle sectors. Using scenario planning methodologies, we explored four plausible but provocative long-term futures which identify dynamics underpinning the resilience of these agricultural sectors to animal disease. These scenarios highlight a number of important drivers that influence disease resilience: industry demographics, the role of government support and regulation and the capacity for technological innovation to support the industry to meet local and global market demand. Participants in the scenario planning exercises proposed creative, robust strategies that policy makers could consider implementing now to enhance disease control and industry resilience in multiple, uncertain futures. Using these participant-led strategies as a starting point, we offer ten key questions for policy makers and stakeholders to provoke further discussion about improving resiliency and disease preparedness. We conclude with a brief discussion of the value of scenario planning, not only for the development of futures which will inform disease contingency plans and improve industry resilience, but as a mechanism for dialogue and information sharing between stakeholders and government.

E. coli O157 on Scottish cattle farms: evidence of local spread and persistence using repeat cross-sectional data.

BACKGROUND: Escherichia coli (E. coli) O157 is a virulent zoonotic strain of enterohaemorrhagic E. coli. In Scotland (1998-2008) the annual reported rate of human infection is 4.4 per 100,000 population which is consistently higher than other regions of the UK and abroad. Cattle are the primary reservoir. Thus understanding infection dynamics in cattle is paramount to reducing human infections.A large database was created for farms sampled in two cross-sectional surveys carried out in Scotland (1998-2004). A statistical model was generated to identify risk factors for the presence of E. coli O157 on farms. Specific hypotheses were tested regarding the presence of E. coli O157 on local farms and the farms previous status. Pulsed-field gel electrophoresis (PFGE) profiles were further examined to ascertain whether local spread or persistence of strains could be inferred. RESULTS: The presence of an E. coli O157 positive local farm (average distance: 5.96 km) in the Highlands, North East and South West, farm size and the number of cattle moved onto the farm 8 weeks prior to sampling were significant risk factors for the presence of E. coli O157 on farms. Previous status of a farm was not a significant predictor of current status (p = 0.398). Farms within the same sampling cluster were significantly more likely to be the same PFGE type (p < 0.001), implicating spread of strains between local farms. Isolates with identical PFGE types were observed to persist across the two surveys, including 3 that were identified on the same farm, suggesting an environmental reservoir. PFGE types that were persistent were more likely to have been observed in human clinical infections in Scotland (p < 0.001) from the same time frame. CONCLUSIONS: The results of this study demonstrate the spread of E. coli O157 between local farms and highlight the potential link between persistent cattle strains and human clinical infections in Scotland. This novel insight into the epidemiology of Scottish E. coli O157 paves the way for future research into the mechanisms of transmission which should help with the design of control measures to reduce E. coli O157 from livestock-related sources.

Successful immunization against a parasitic nematode by vaccination with recombinant proteins.

Infection of humans and livestock with parasitic nematodes can have devastating effects on health and production, affecting food security in both developed and developing regions. Despite decades of research, the development of recombinant sub-unit vaccines against these pathogens has been largely unsuccessful. We have developed a strategy to identify protective antigens from Teladorsagia circumcincta, the major pathogen causing parasitic gastroenteritis in small ruminants in temperate regions, by studying IgA responses directed at proteins specific to post-infective larvae. Antigens were also selected on the basis of their potential immunomodulatory role at the host/parasite interface. Recombinant versions of eight molecules identified by immunoproteomics, homology with vaccine candidates in other nematodes and/or with potential immunoregulatory activities, were therefore administered to sheep in a single vaccine formulation. The vaccine was administered three times with Quil A adjuvant and the animals subsequently subjected to a repeated challenge infection designed to mimic field conditions. Levels of protection in the vaccinates were compared to those obtained in sheep administered with Quil A alone. The trial was performed on two occasions. In both trials, vaccinates had significantly lower mean fecal worm egg counts (FWECs) over the sampling period, with a mean reduction in egg output of 70% (Trial 1) and 58% (Trial 2). During the period of peak worm egg shedding, vaccinates shed 92% and 73% fewer eggs than did controls in Trials 1 and 2, respectively. At post mortem, vaccinates had 75% (Trial 1) and 56% (Trial 2) lower adult nematode burdens than the controls. These levels of protection are the highest observed in any system using a nematode recombinant sub-unit vaccine in the definitive ruminant host and indicate that control of parasitic helminths via vaccination with recombinant subunit vaccine cocktails is indeed an alternative option in the face of multi-drug resistance.

Genomic variations associated with attenuation in Mycobacterium avium subsp. paratuberculosis vaccine strains.

BACKGROUND: Mycobacterium avium subspecies paratuberculosis (MAP) whole cell vaccines have been widely used tools in the control of Johne's disease in animals despite being unable to provide complete protection. Current vaccine strains derive from stocks created many decades ago; however their genotypes, underlying mechanisms and relative degree of their attenuation are largely unknown. RESULTS: Using mouse virulence studies we confirm that MAP vaccine strains 316 F, II and 2e have diverse but clearly attenuated survival and persistence characteristics compared with wild type strains. Using a pan genomic microarray we characterise the genomic variations in a panel of vaccine strains sourced from stocks spanning over 40 years of maintenance. We describe multiple genomic variations specific for individual vaccine stocks in both deletion (26-32 Kbp) and tandem duplicated (11-40 Kbp) large variable genomic islands and insertion sequence copy numbers. We show individual differences suitable for diagnostic differentiation between vaccine and wild type genotypes and provide evidence for functionality of some of the deleted MAP-specific genes and their possible relation to attenuation. CONCLUSIONS: This study shows how culture environments have influenced MAP genome diversity resulting in large tandem genomic duplications, deletions and transposable element activity. In combination with classical selective systematic subculture this has led to fixation of specific MAP genomic alterations in some vaccine strain lineages which link the resulting attenuated phenotypes with deficiencies in high reactive oxygen species handling.

Accounting for uncertainty in model-based prevalence estimation: paratuberculosis control in dairy herds.

BACKGROUND: A common approach to the application of epidemiological models is to determine a single (point estimate) parameterisation using the information available in the literature. However, in many cases there is considerable uncertainty about parameter values, reflecting both the incomplete nature of current knowledge and natural variation, for example between farms. Furthermore model outcomes may be highly sensitive to different parameter values. Paratuberculosis is an infection for which many of the key parameter values are poorly understood and highly variable, and for such infections there is a need to develop and apply statistical techniques which make maximal use of available data. RESULTS: A technique based on Latin hypercube sampling combined with a novel reweighting method was developed which enables parameter uncertainty and variability to be incorporated into a model-based framework for estimation of prevalence. The method was evaluated by applying it to a simulation of paratuberculosis in dairy herds which combines a continuous time stochastic algorithm with model features such as within herd variability in disease development and shedding, which have not been previously explored in paratuberculosis models. Generated sample parameter combinations were assigned a weight, determined by quantifying the model's resultant ability to reproduce prevalence data. Once these weights are generated the model can be used to evaluate other scenarios such as control options. To illustrate the utility of this approach these reweighted model outputs were used to compare standard test and cull control strategies both individually and in combination with simple husbandry practices that aim to reduce infection rates. CONCLUSIONS: The technique developed has been shown to be applicable to a complex model incorporating realistic control options. For models where parameters are not well known or subject to significant variability, the reweighting scheme allowed estimated distributions of parameter values to be combined with additional sources of information, such as that available from prevalence distributions, resulting in outputs which implicitly handle variation and uncertainty. This methodology allows for more robust predictions from modelling approaches by allowing for parameter uncertainty and combining different sources of information, and is thus expected to be useful in application to a large number of disease systems.

Pathogenic potential to humans of bovine Escherichia coli O26, Scotland.

Escherichia coli O26 and O157 have similar overall prevalences in cattle in Scotland, but in humans, Shiga toxin-producing E. coli O26 infections are fewer and clinically less severe than E. coli O157 infections. To investigate this discrepancy, we genotyped E. coli O26 isolates from cattle and humans in Scotland and continental Europe. The genetic background of some strains from Scotland was closely related to that of strains causing severe infections in Europe. Nonmetric multidimensional scaling found an association between hemolytic uremic syndrome (HUS) and multilocus sequence type 21 strains and confirmed the role of stx(2) in severe human disease. Although the prevalences of E. coli O26 and O157 on cattle farms in Scotland are equivalent, prevalence of more virulent strains is low, reducing human infection risk. However, new data on E. coli O26-associated HUS in humans highlight the need for surveillance of non-O157 enterohemorrhagic E. coli and for understanding stx(2) phage acquisition.

GTP-cyclohydrolase and development in Teladorsagia circumcincta and Dictyocaulus viviparus (Nematoda: Strongylida).

GTP-Cyclohydrolase (GTP-CH) is necessary for the production of tetrahydrobiopterin, a required cofactor for the three aromatic amino acid hydroxylases and nitric oxide synthases. The gene encoding GTP-CH is transcribed at high levels in infective third larval stages of a number of parasitic trichostrongylid nematodes. We explore the potential role of GTP-CH within the processes of nematode development and environmentally-induced hypobiosis. For two species of parasitic nematode that are of major economic and welfare importance to livestock in temperate regions, Teladorsagia circumcincta and Dictyocaulus viviparus, we have demonstrated that each of the pre-parasitic larval stages transcribe high mean levels of cat-4 (the gene encoding GTP-CH). Using quantitative real-time polymerase chain reaction analysis and two different isolates of D. viviparus, only one of which is capable of entering hypobiosis, we have shown that there were only minor differences between these isolates in mean cat-4 transcript levels, both during the parasitic stages and during the earlier environmental life cycle stages (L(1)-L(3)). Taken together, these data indicate that, although both species of nematode produce high levels of cat-4 transcript in pre-parasitic larval stages, GTP-CH levels are unlikely to be involved in the induction of parasite hypobiosis. Alternative roles for GTP-CH in larval development are discussed.

Prevalence of Escherichia coli O157: H7 and serogroups O26, O103, O111 and O145 in sheep presented for slaughter in Scotland.

Sheep have been proposed as a source of human verocytotoxigenic Escherichia coli infection on a number of occasions but few prevalence studies have focused on identifying rates of carriage of these pathogens in this species. The purpose of this work was to establish the frequency of excretion of E. coli of serogroups O157, O26, O103, O111 and O145 in sheep presented for slaughter in Scotland and to examine their carriage of known virulence determinants. The study involved microbiological isolation of E. coli from 1082 sheep presented for slaughter in four Scottish abattoirs between July 2005 and June 2006. Using faecal enrichment and immunomagnetic separation, the isolation rate from these samples was 3.4 % for E. coli serogroup O157, 5.2 % for E. coli serogroup O26, 2.3 % for E. coli serogroup O103 and 0.1 % for E. coli serogroup O145. E. coli O111 was not isolated. In the last month of testing, which coincided with sorbitol-fermenting E. coli O157 (SFO157) cases in children in Scotland, all 83 recta received were screened and tested negative for SFO157 strains. The study found no verocytotoxin-positive strains amongst the E. coli serogroup O103 or O145 isolates. Verocytotoxin-positive strains were identified amongst isolates of E. coli serotypes O157 : H7 and O26 : H11. E. coli O157 : H7 was not isolated from samples collected between January and March, a statistically significant drop (P<0.001) in mean shedding relative to other months. There was evidence (P = 0.003) of higher shedding of O157 in adults and hoggs than in lambs. E. coli O26 : H11 was isolated throughout the year, with a statistically significant peak in shedding in the third quarter (P = 0.003). The results showed that sheep presented for slaughter in Scotland may carry strains of E. coli, particularly of serogroups O157 and O26, which can be presumed to have potential to cause human infection. They did not support a hypothesis that human cases of E. coli O157 : H7 are higher in any particular Scottish region as a direct consequence of a higher rate of faecal carriage in sheep in that region.

Spread of E. coli O157 infection among Scottish cattle farms: stochastic models and model selection.

Identifying risk factors for the presence of Escherichia coli O157 infection on cattle farms is important for understanding the epidemiology of this zoonotic infection in its main reservoir and for informing the design of interventions to reduce the public health risk. Here, we use data from a large-scale field study carried out in Scotland to fit both "SIS"-type dynamical models and statistical risk factor models. By comparing the fit (assessed using maximum likelihood) of different dynamical models we are able to identify the most parsimonious model (using the AIC statistic) and compare it with the model suggested by risk factor analysis. Both approaches identify 2 key risk factors: the movement of cattle onto the farm and the number of cattle on the farm. There was no evidence for a role of other livestock species or seasonality, or of significant risk of local spread. However, the most parsimonious dynamical model does predict that farms can infect other farms through routes other than cattle movement, and that there is a nonlinear relationship between the force of infection and the number of infected farms. An important prediction from the most parsimonious model is that although only approximately 20% farms may harbour E. coli O157 infection at any given time approximately 80% may harbour infection at some point during the course of a year.