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Background Norovirus is associated with approximately 18% of the global burden of gastroenteritis and affects all age groups. There is currently no licensed vaccine or available antiviral treatment. However, well-designed early warning systems and forecasting can guide nonpharmaceutical approaches to norovirus infection prevention and control. Objective This study evaluates the predictive power of existing syndromic surveillance data and emerging data sources, such as internet searches and Wikipedia page views, to predict norovirus activity across a range of age groups across England. Methods We used existing syndromic surveillance and emerging syndromic data to predict laboratory data indicating norovirus activity. Two methods are used to evaluate the predictive potential of syndromic variables. First, the Granger causality framework was used to assess whether individual variables precede changes in norovirus laboratory reports in a given region or an age group. Then, we used random forest modeling to estimate the importance of each variable in the context of others with two methods: (1) change in the mean square error and (2) node purity. Finally, these results were combined into a visualization indicating the most influential predictors for norovirus laboratory reports in a specific age group and region. Results Our results suggest that syndromic surveillance data include valuable predictors for norovirus laboratory reports in England. However, Wikipedia page views are less likely to provide prediction improvements on top of Google Trends and Existing Syndromic Data. Predictors displayed varying relevance across age groups and regions. For example, the random forest modeling based on selected existing and emerging syndromic variables explained 60% variance in the ≥65 years age group, 42% in the East of England, but only 13% in the South West region. Emerging data sets highlighted relative search volumes, including “flu symptoms,” “norovirus in pregnancy,” and norovirus activity in specific years, such as “norovirus 2016.” Symptoms of vomiting and gastroenteritis in multiple age groups were identified as important predictors within existing data sources. Conclusions Existing and emerging data sources can help predict norovirus activity in England in some age groups and geographic regions, particularly, predictors concerning vomiting, gastroenteritis, and norovirus in the vulnerable populations and historical terms such as stomach flu. However, syndromic predictors were less relevant in some age groups and regions likely due to contrasting public health practices between regions and health information–seeking behavior between age groups. Additionally, predictors relevant to one norovirus season may not contribute to other seasons. Data biases, such as low spatial granularity in Google Trends and especially in Wikipedia data, also play a role in the results. Moreover, internet searches can provide insight into mental models, that is, an individual’s conceptual understanding of norovirus infection and transmission, which could be used in public health communication strategies.

BACKGROUND Norovirus is associated with approximately 18% of the global burden of gastroenteritis and affects all age groups. There is currently no licensed vaccine or available antiviral treatment. However, well-designed early warning systems and forecasting can guide nonpharmaceutical approaches to norovirus infection prevention and control. OBJECTIVE This study evaluates the predictive power of existing syndromic surveillance data and emerging data sources, such as internet searches and Wikipedia page views, to predict norovirus activity across a range of age groups across England. METHODS We used existing syndromic surveillance and emerging syndromic data to predict laboratory data indicating norovirus activity. Two methods are used to evaluate the predictive potential of syndromic variables. First, the Granger causality framework was used to assess whether individual variables precede changes in norovirus laboratory reports in a given region or an age group. Then, we used random forest modeling to estimate the importance of each variable in the context of others with two methods: (1) change in the mean square error and (2) node purity. Finally, these results were combined into a visualization indicating the most influential predictors for norovirus laboratory reports in a specific age group and region. RESULTS Our results suggest that syndromic surveillance data include valuable predictors for norovirus laboratory reports in England. However, Wikipedia page views are less likely to provide prediction improvements on top of Google Trends and Existing Syndromic Data. Predictors displayed varying relevance across age groups and regions. For example, the random forest modeling based on selected existing and emerging syndromic variables explained 60% variance in the ≥65 years age group, 42% in the East of England, but only 13% in the South West region. Emerging data sets highlighted relative search volumes, including “flu symptoms,” “norovirus in pregnancy,” and norovirus activity in specific years, such as “norovirus 2016.” Symptoms of vomiting and gastroenteritis in multiple age groups were identified as important predictors within existing data sources. CONCLUSIONS Existing and emerging data sources can help predict norovirus activity in England in some age groups and geographic regions, particularly, predictors concerning vomiting, gastroenteritis, and norovirus in the vulnerable populations and historical terms such as stomach flu. However, syndromic predictors were less relevant in some age groups and regions likely due to contrasting public health practices between regions and health information–seeking behavior between age groups. Additionally, predictors relevant to one norovirus season may not contribute to other seasons. Data biases, such as low spatial granularity in Google Trends and especially in Wikipedia data, also play a role in the results. Moreover, internet searches can provide insight into mental models, that is, an individual’s conceptual understanding of norovirus infection and transmission, which could be used in public health communication strategies.

The precision by which animal diseases are diagnosed affects our ability to make informed decisions with regards to animal health management, from a clinical and economic perspective. Lameness is a major health condition in dairy cattle. The underlying causes of lameness include bovine digital dermatitis (BDD), which is reported as one of the main causes of infectious lameness in dairy cattle. Presently, the gold standard for BDD diagnosis in dairy cattle is visual inspection of lifted hooves—a labour intensive and subjective method. Research has suggested that Treponema spp. are the main pathogens associated with the establishment of BDD. We explored the potential of indirect enzyme-linked immunosorbent assay (ELISA) as a diagnostic serological tool in the identification of cows at different stages of BDD. Additionally, we evaluated the predictive power of this diagnostic tool on the future occurrence of BDD lesions. A total of 232 cows from three farms were used in the study. Serum samples and hoof health data were collected at three time points: ~ 30 days pre-calving, around calving, and approximately 30 days post-calving. The mean absorbance from the ELISA test was compared across different clinical presentations of BDD as assessed by visual inspection of the hooves according to the M-stage classification system. A transition model was developed to estimate the probability of lesion occurrence in time t + 1 based on the spectrophotometer (absorbance) reading in time t. The mean absorbance reading for both IgG1 and IgG2 anti-Treponema antibodies was associated with disease presence—apart from M4.1 lesions, animals with no lesions had a lower mean when compared to animals with lesions regardless of the score. Additionally, the mean absorbance reading of animals with active lesions was higher when compared to animals with no lesions. However, the anti-Treponema antibody assays failed to identify disease presence in a consistent manner. Moreover, indirect ELISA readings were not a predictor of the future occurrence of BDD lesions. In conclusion, although the levels anti-Treponema antibodies were associated with disease presence, the ELISA test failed to detect disease unequivocally and had no predictive value in the future occurrence of BDD lesions.

Background Fasciola hepatica (liver fluke) affects grazing animals including horses but the extent to which it affects UK horses is unknown. Objectives To define how liver fluke affects the UK horse population. Study design Descriptive, cross-sectional, observational study. Methods An F. hepatica excretory-secretory antibody detection ELISA with a diagnostic sensitivity of 71% and specificity of 97% was validated and used to analyse serum samples. An abattoir study was performed to determine prevalence. A case-control study of 269 horses compared fluke exposure between horses with liver disease and controls. Data on clinical signs and blood test results were collected for sero-positive horses. Genotyping of adult fluke was used to produce a multilocus genotype for each parasite. Results Four (2.2%) of 183 horses registered in the UK, sampled in the abattoir, had adult flukes in the liver, and the sero-prevalence of F. hepatica was estimated as 8.7%. In the case-control study, horses showing signs consistent with liver disease had significantly higher odds of testing positive for F. hepatica on ELISA than control horses. In 23 sero-positive horses, a range of non-specific clinical signs and blood test abnormalities was reported, with a third of the horses showing no signs. Genotypic analysis of liver flukes from horses provided evidence that these came from the same population as flukes from sheep and cattle. Main limitations Bias could have arisen in the prevalence and case-control studies due to convenience sampling methods, in particular the geographic origin of the horses. Only a small number of horses tested positive so the data on clinical signs are limited. Conclusions Exposure to liver fluke occurs frequently in horses and may be an under-recognised cause of liver disease. Flukes isolated from horses are from the same population as those found in ruminants. When designing and implementing parasite control plans, fluke should be considered, and horses should be tested if appropriate.

Background Norovirus has a higher level of under-reporting in England compared to other intestinal infectious agents such as Campylobacter or Salmonella, despite being recognised as the most common cause of gastroenteritis globally. In England, this under-reporting is a consequence of the frequently mild/self-limiting nature of the disease, combined with the passive surveillance system for infectious diseases reporting. We investigated heterogeneity in passive surveillance system in order to improve understanding of differences in reporting and laboratory testing practices of norovirus in England. Methods The reporting patterns of norovirus relating to age and geographical region of England were investigated using a multivariate negative binomial model. Multiple model formulations were compared, and the best performing model was determined by proper scoring rules based on one-week-ahead predictions. The reporting patterns are represented by epidemic and endemic random intercepts; values close to one and less than one imply a lower number of reports than expected in the given region and age-group. Results The best performing model highlighted atypically large and small amounts of reporting by comparison with the average in England. Endemic random intercept varied from the lowest in East Midlands in those in the under 5 year age-group (0.36, CI 0.18–0.72) to the highest in the same age group in South West (3.00, CI 1.68–5.35) and Yorkshire & the Humber (2.93, CI 1.74–4.94). Reporting by age groups showed the highest variability in young children. Conclusion We identified substantial variability in reporting patterns of norovirus by age and by region of England. Our findings highlight the importance of considering uncertainty in the design of forecasting tools for norovirus, and to inform the development of more targeted risk management approaches for norovirus disease.

COVID-19 has disrupted everyday life worldwide and is the first disease event since the 1918 H1N1 Spanish influenza (flu) pandemic to demand an urgent global healthcare response. There has been much debate on whether the damage of COVID-19 is due predominantly to the pathogen itself or our response to it. We compare SARS-CoV-2 against three other major pandemics (1347 Black Death, 1520's new world smallpox outbreaks, and 1918 Spanish Flu pandemic) over the course of 700 years to unearth similarities and differences in pathogen, social and medical context, human response and behavior, and long-term social and economic impact that should be used to shape COVID-19 decision-making. We conclude that

ObjectivesReporting of COVID-19 cases, deaths and testing has often lacked context for appropriate assessment of disease burden within risk groups. The research considers how routine surveillance data might provide initial insights and identify risk factors, setting COVID-19 deaths early in the pandemic into context. This will facilitate the understanding of wider consequences of a pandemic from the earliest stage, reducing fear, aiding in accurately assessing disease burden and ensuring appropriate disease mitigation.SettingUK, 2020.ParticipantsThe study is a secondary analysis of routine, public domain, surveillance data and information from Office for National Statistics (ONS), National Health Service (NHS) 111 and Public Health England (PHE) on deaths and disease.Primary and secondary outcome measuresOur principal focus is ONS data on deaths mentioning COVID-19 on the death certificate. We also consider information provided in NHS 111 and PHE data summaries.ResultsDeaths with COVID-19 significantly contributed to, yet do not entirely explain, abnormally elevated all-cause mortality in the UK from weeks 12–18 of 2020. Early in the UK epidemic, COVID-19 was the greatest threat to those with underlying illness, rarely endangering people aged under 40 years. COVID-19-related death rates differed by region, possibly reflecting underlying population structure. Risk of COVID-19-related death was greater for healthcare and social care staff and black, Asian and minority ethnic individuals, having allowed for documented risk factors.ConclusionEarly contextualisation of public health data is critical to recognising who gets sick, when and why. Understanding at-risk groups facilitates a targeted response considering indirect consequences of society’s reaction to a pandemic alongside disease-related impacts. COVID-19-related deaths mainly mirror historical patterns, and excess non-COVID-19-related deaths partly reflect reduced access to and uptake of healthcare during lockdown. Future outbreak response will improve through better understanding of connectivity between disease monitoring systems to aid interpretation of disease risk patterns, facilitating nuanced mitigation measures.

Background The COVID-19 pandemic has impacted surveillance activities for multiple pathogens. Since March 2020, there was a decline in the number of reports of norovirus and Campylobacter recorded by England’s national laboratory surveillance system. The aim is to estimate and compare the impact of the COVID-19 pandemic on norovirus and Campylobacter surveillance data in England. Methods We utilised two quasi-experimental approaches based on a generalised linear model for sequential count data. The first approach estimates overall impact and the second approach focuses on the impact of specific elements of the pandemic response (COVID-19 diagnostic testing and control measures). The following time series (27, 2015–43, 2020) were used: weekly laboratory-confirmed norovirus and Campylobacter reports, air temperature, conducted Sars-CoV-2 tests and Index of COVID-19 control measures stringency. Results The period of Sars-CoV-2 emergence and subsequent sustained transmission was associated with persistent reductions in norovirus laboratory reports (p = 0.001), whereas the reductions were more pronounced during pandemic emergence and later recovered for Campylobacter (p = 0.075). The total estimated reduction was 47% - 79% for norovirus (12–43, 2020). The total reduction varied by time for Campylobacter, e.g. 19% - 33% in April, 1% - 7% in August. Conclusion Laboratory reporting of norovirus was more adversely impacted than Campylobacter by the COVID-19 pandemic. This may be partially explained by a comparatively stronger effect of behavioural interventions on norovirus transmission and a relatively greater reduction in norovirus testing capacity. Our study underlines the differential impact a pandemic may have on surveillance of gastrointestinal infectious diseases.

International audience; Lameness is a serious concern in the dairy sector, reflecting its high incidence and impact on animal welfare and productivity. Research has provided figures on its frequency using different methodologies, making it difficult to compare results and hindering farm-level decision-making. The study's objectives were to determine the frequency levels of lameness in British dairy cattle through a meta-analysis approach, and to understand the chronological patterns of how lameness cases are detected and classified in scientific research. A systematic review was conducted using PRISMA-P guidelines for article selection. Random-effects models estimated the pooled frequency measure of lameness with heterogeneity managed through subgroup analysis and meta-regression. Sixty-eight papers were identified, 50 included prevalence and 36 incidence data. The pooled prevalence of lameness in British dairy cattle was estimated at 29.5% (95% CI 26.7-32.4%) whilst all-cause lameness incidence rate indicated 30.9 cases of lameness per 100 cow-years (95% CI 24.5-37.9). The pooled cause-specific lameness incidence rate per 100 cow-years was 66.1 (95% CI 24.1-128.8) for white line disease, 53.2 (95% CI 20.5-101.2) for sole ulcer, 53.6 (95% CI 19.2-105.34) for digital dermatitis, with 51.9 (95% CI 9.3-129.2) attributable to other lameness-related lesions. Heterogeneity levels remained high. Sixty-nine papers contributed to a chronological overview of lameness data source. Although the AHDB Dairy mobility scoring system (MSS) was launched in the UK in 2008 and adopted shortly after by the British Dairy sector as the standard tool for assessing lameness, other methods are used depending on the investigator. Automated lameness detection systems may offer a solution for the subjective nature of MSSs, yet it was utilized in one study only. Despite the recognition of under-reporting of lameness from farm records 22 (31.9%) studies used this data source. The diversity of lameness data collection methods and sources was a key finding. It limits the understanding of lameness burden and the refinement of policy making for lameness. Standardizing case definition and research methods would improve knowledge of and ability to manage lameness. Regardless of the measurement method lameness in British dairy cattle is high.

Less than half of stool samples from people symptomatic with infectious intestinal disease (IID) will identify a causative organism. A secondary data analysis was undertaken to explore whether symptomology alone could be used to make inferences about causative organisms. Data were utilised from the Second Study of Infectious Intestinal Disease in the Community. A total of 844 cases were analysed. Few symptoms differentiated individual pathogens, but grouping pathogens together showed that viral IID was more likely when symptom onset was in winter (odds ratio (OR) 2.08, 95% confidence interval (CI) 1.16–3.75) or spring (OR 1.92, 95% CI 1.11–3.33), the patient was aged under 5 years (OR 3.63, 95% CI 2.24–6.03) and there was loss of appetite (OR 2.19, 95% CI 1.29–3.72). The odds of bacterial IID were higher with diarrhoea in the absence of vomiting (OR 3.54, 95% CI 2.37–5.32), diarrhoea which persisted for >3 days (OR 2.69, 95% CI 1.82–3.99), bloody diarrhoea (OR 4.17, 95% CI 1.63–11.83) and fever (OR 1.67, 95% CI 1.11–2.53). Symptom profiles could be of value to help guide clinicians and public health professionals in the management of IID, in the absence of microbiological confirmation.

Norovirus (NoV) is the greatest cause of infectious intestinal disease in the UK. The burden associated with foodborne outbreaks is underestimated in part because data are dispersed across different organisations. Each looks at outbreaks through a different lens. To estimate the burden of NoV from seafood including shellfish we used a capture-recapture technique using datasets from three different organisations currently involved in collecting information on outbreaks. The number of outbreaks of NoV related to seafood including shellfish in England was estimated for the period of 2004–2011. The combined estimates were more than three times as high (N= 360 using Chao's sample coverage approach) as the individual count from organisation three (N= 115), which captured more outbreaks than the other two organisations. The estimates were calculated for both independence and dependence between the datasets. There was evidence of under-reporting of NoV outbreaks and inconsistency of reporting between organisations, which means that, currently, more than one data source needs to be used to estimate as accurately as possible the total number of NoV outbreaks and associated cases. Furthermore, either the integration of reporting mechanisms or simplifying the process of reporting outbreaks to organisations is essential for understanding and, hence, controlling disease burden.

Background Outbreaks of infectious gastroenteritis in care homes are common, with norovirus a frequent cause. In England there is no co-ordinated national surveillance system. We aimed to estimate the burden of these outbreaks. Methods Using a generalised linear mixed effects regression model we described the relationship between the observed number of care home outbreaks and covariates. Estimated model parameters were used to infer uplift in the number of outbreaks expected if all areas were subjected to enhanced surveillance. From this we then estimated the total burden of care home gastroenteritis outbreaks in this period. Results We estimated a total of 14,146 care home gastroenteritis outbreaks in England during 2014–2016; this is 47% higher than the reported total and a rate of 32.4 outbreaks per 100 care homes per year. The median number of outbreaks from the model estimates was 31 (IQR 20–46) compared to 19 (IQR 12–34) reported from routine surveillance. Conclusions This estimated care home gastroenteritis burden in England indicates that current surveillance substantially underestimates the number of outbreaks, by almost half. Improving this surveillance could provide better epidemiological knowledge of the burden of norovirus to inform public health policy, particularly with the advent of norovirus vaccines. Electronic supplementary material The online version of this article (10.1186/s12879-018-3642-3) contains supplementary material, which is available to authorized users.

This article discusses the use of Bayesian methods for performing uncertainty analysis in complex computer models, focusing on a mechanistic model that has been applied in a risk assessment of contamination of farm-pasteurized milk with the bacterium Vero-cytotoxigenic E. coli (VTEC) O157. The VTEC model has uncertain input parameters, which makes outputs from the model used to inform the risk assessment also uncertain. The question that arises is how to reduce output uncertainty in the most efficient manner possible. The article first provides an overview of microbial risk assessment before analysing the frequency and consequences of food-borne outbreaks associated with VTEC O157. It then introduces the risk assessment model, along with model input distributions. Finally, it presents the results of a variance-based sensitivity analysis that was conducted to identify the most important uncertain model inputs.

Fasciola hepatica is a parasitic trematode of global importance in livestock. Control strategies reliant on anthelmintics are unsustainable due to the emergence of drug resistance. Vaccines are under development, but efficacies are variable. Evidence from experimental infection suggests that vaccine efficacy may be affected by parasite-induced immunomodulation. Little is known about the immune response to F. hepatica following natural exposure. Hence, we analyzed the immune responses over time in calves naturally exposed to F. hepatica infection. Cohorts of replacement dairy heifer calves (n = 42) with no prior exposure to F. hepatica, on three commercial dairy farms, were sampled over the course of a grazing season. Exposure was determined through an F. hepatica-specific serum antibody enzyme-linked immunosorbent assay (ELISA) and fluke egg counts. Concurrent changes in peripheral blood leukocyte subpopulations, lymphocyte proliferation, and cytokine responses were measured. Relationships between fluke infection and immune responses were analyzed by using multivariable linear mixed-effect models. All calves from one farm showed evidence of exposure, while cohorts from the remaining two farms remained negative over the grazing season. A type 2 immune response was associated with exposure, with increased interleukin-4 (IL-4) production, IL-5 transcription, and eosinophilia. Suppression of parasite-specific peripheral blood mononuclear cell (PBMC) proliferation was evident, while decreased mitogen-stimulated gamma interferon (IFN-γ) production suggested immunomodulation, which was not restricted to parasite-specific responses. Our findings show that the global immune response is modulated toward a nonproliferative type 2 state following natural challenge with F. hepatica. This has implications in terms of the timing of the administration of vaccination programs and for host susceptibility to coinfecting pathogens.

Fasciola hepatica is a parasitic trematode of global importance in livestock. Control strategies reliant on anthelmintics are unsustainable due to the emergence of drug resistance. Vaccines are under development, but efficacies are variable. Evidence from experimental infection suggests that vaccine efficacy may be affected by parasite-induced immunomodulation. Little is known about the immune response to F. hepatica following natural exposure. Hence, we analyzed the immune responses over time in calves naturally exposed to F. hepatica infection. Cohorts of replacement dairy heifer calves ( n = 42) with no prior exposure to F. hepatica , on three commercial dairy farms, were sampled over the course of a grazing season. Exposure was determined through an F. hepatica -specific serum antibody enzyme-linked immunosorbent assay (ELISA) and fluke egg counts. Concurrent changes in peripheral blood leukocyte subpopulations, lymphocyte proliferation, and cytokine responses were measured. Relationships between fluke infection and immune responses were analyzed by using multivariable linear mixed-effect models. All calves from one farm showed evidence of exposure, while cohorts from the remaining two farms remained negative over the grazing season. A type 2 immune response was associated with exposure, with increased interleukin-4 (IL-4) production, IL-5 transcription, and eosinophilia. Suppression of parasite-specific peripheral blood mononuclear cell (PBMC) proliferation was evident, while decreased mitogen-stimulated gamma interferon (IFN-γ) production suggested immunomodulation, which was not restricted to parasite-specific responses. Our findings show that the global immune response is modulated toward a nonproliferative type 2 state following natural challenge with F. hepatica . This has implications in terms of the timing of the administration of vaccination programs and for host susceptibility to coinfecting pathogens.

Background Liver fluke infection caused by the parasite Fasciola hepatica is a major cause of production losses to the cattle industry in the UK. To investigate farm-level risk factors for fluke infection, a randomised method to recruit an appropriate number of herds from a defined geographical area into the study was required. The approach and hurdles that were encountered in designing and implementing this study are described. The county of Shropshire, England, was selected for the study because of the variation between farms in exposure to fluke infection observed in an earlier study. Results From a sampling list of 569 holdings in Shropshire randomly drawn from the RADAR cattle population dataset, 396 (69.6%) holdings were successfully contacted by telephone and asked if they would be interested in taking part in the study. Of 296 farmers who agreed to receive information packs by post, 195 (65.9%) agreed to take part in the study. Over the period October 2014 – April 2015 visits were made to 100 dairy and 95 non-dairy herds. During the farm visits 40 faecal samples +/− bulk-tank milk samples were collected and a questionnaire administered. Composite faecal samples were analysed for the presence of F. hepatica eggs by sedimentation and bulk tank milk samples were tested with an antibody ELISA for F. hepatica. Forty-five (49%) of non-dairy herds were positive for liver fluke infection as determined by the finding of one or more fluke eggs, while 36 (36%) dairy herds had fluke positive faecal samples and 41 (41%) dairy herds were positive for F. hepatica antibody. Eighty-seven (45.8%) farmers said that they monitored their cattle for liver fluke infection and 118 (62.1%) reported that they used flukicide drugs in their cattle. Conclusions Using a protocol of contacting farmers directly by telephone and subsequently sending information by post, 79% of the target sample size was successfully recruited into the study. A dataset of farm-specific information on possible risk factors for liver fluke infection and corresponding liver-fluke infection status was generated for the development of statistical models to identify risk factors for liver fluke infection at the farm-level.

Aims: To investigate the prevalence and temporal patterns of antimicrobial resistance in wild rodents with no apparent exposure to antimicrobials. Methods and Results: Two sympatric populations of bank voles and wood mice were trapped and individually monitored over a 2- year period for faecal carriage of antimicrobial-resistant Escherichia coli. High prevalences of ampicillin-, chloramphenicol-, tetracycline- and trimethoprim-resistant E. coli were observed. A markedly higher prevalence of antimicrobial-resistant E. coli was found in wood mice than in bank voles, with the prevalence in both increasing over time. Superimposed on this trend was a seasonal cycle with a peak prevalence of resistant E. coli in mice in early- to mid-summer and in voles in late summer and early autumn. Conclusions: These sympatric rodent species had no obvious contact with antimicrobials, and the difference in resistance profiles between rodent species and seasons suggests that factors present in their environment are unlikely to be drivers of such resistance. Significance and Impact of the Study: These findings suggest that rodents may represent a reservoir of antimicrobial-resistant bacteria, transmissible to livestock and man. Furthermore, such findings have implications for human and veterinary medicine regarding antimicrobial usage and subsequent selection of antimicrobial-resistant organisms.

Milk sold as pasteurized has historically been implicated in the UK and worldwide as a vehicle for outbreaks of food-borne gastrointestinal disease, with a number of causative pathogenic organisms. One such organism is Verocytotoxigenic Escherichia coli, or E. coli, O157 (VTEC O157). We present a quantitative assessment of likely exposure to VTEC O157 via milk sold as pasteurized in the UK. Particular interest in our assessment concerns whether there is any differential risk between milk which is processed in on- and off-farm dairies. We model the milk production chain from the farm through to the point of retail and make a comparison between these two production environments. Our model is an example of the Modular Process Risk Modelling (MPRM) approach and represents uncertainty and variability in input parameters using probability distributions. We conclude that milk processed on farm poses the comparatively greater risk, although that risk is still small.

SUMMARYUsing data from a cohort study conducted by the Veterinary Laboratories Agency (VLA), evidence of spatial clustering at distances up to 30 km was found for S. Agama and S. Dublin (P values of 0·001) and borderline evidence was found for spatial clustering of S. Typhimurium (P=0·077). The evolution of infection status of study farms over time was modelled using a Markov Chain model with transition probabilities describing changes in status at each of four visits, allowing for the effect of sampling visit. The degree of geographical clustering of infection, having allowed for temporal effects, was assessed by comparing the residual deviance from a model including a measure of recent neighbourhood infection levels with one excluding this variable. The number of cases arising within a defined distance and time period of an index case was higher than expected. This provides evidence for spatial and spatio-temporal clustering, which suggests either a contagious process (e.g. through direct or indirect farm-to-farm transmission) or geographically localized environmental and/or farm factors which increase the risk of infection. The results emphasize the different epidemiology of the three Salmonella serovars investigated.

We consider the problem of estimating bacterial concentration in a substance, given microbial count data. A Bayesian approach is proposed which naturally allows the incorporation of both plate-count data and extra information from confirmatory tests such as genotyping by polymerase chain reaction (PCR). The estimation methods yield posterior credible regions for bacterial concentration, in contrast to the previous methods, which generally only produce point estimates. The approach is illustrated with specific reference to the enumeration of the food-borne pathogen Escherichia coli O157 by spiral plating, although the methodology can be applied to any bacterium or counting method of interest. The results obtained provide guidance to the experimenter as to the number of confirmatory tests which should be performed, and also suggest that in the initial plate count one should err on the side of including rather than excluding colonies whose genotype seems unclear.

We examine the common clinical belief that there is an interaction between epilepsy type and the two standard anti-epileptic drugs, valproate and carbamazepine, using data from several randomized clinical trials. Epilepsy type is not always easy to define, and three possible reclassifications are investigated to see whether misclassification of epilepsy type within the trials has potentially masked such an interaction. Regression modelling is employed to investigate whether heterogeneity between trial results can be explained by patient factors. Our work suggests that uncertainty in epilepsy type classification should be recognized in future studies. We also generate the hypothesis that the interaction of drug effect with age may reflect the perceived interaction with epilepsy type. We suggest that in any context where misclassification is likely, it is worth considering the use of an explicit ‘unclassified’ group, and investigating whether additional covariates can answer the substantive question. Copyright © 2002 John Wiley & Sons, Ltd.

The paper compares non-parametric (design-based) and parametric (model-based) approaches to the analysis of data in the form of replicated spatial point patterns in two or more experimental groups. Basic questions for data of this kind concern estimating the properties of the underlying spatial point process within each experimental group, and comparing the properties between groups. A non-parametric approach, building on work by Diggle et. al. (1991), summarizes each pattern by an estimate of the reduced second moment measure or K-function (Ripley (1977)) and compares mean K-functions between experimental groups using a bootstrap testing procedure. A parametric approach fits particular classes of parametric model to the data, uses the model parameter estimates as summaries and tests for differences between groups by comparing fits with and without the assumption of common parameter values across groups. The paper discusses how either approach can be implemented in the specific context of a single-factor replicated experiment and uses simulations to show how the parametric approach can be more efficient when the underlying model assumptions hold, but potentially misleading otherwise.

Helen Clough and Patrick Brown are statisticians working in a cross-disciplinary veterinary epidemiology collaboration, uniting statisticians, mathematical modellers, veterinarians, molecular biologists and microbiologists. They describe the strengths that statisticians bring to the collaboration and the creative tensions that arise when both statistical and mathematical modelling techniques are used.

Salmonella spp are a major foodborne zoonotic cause of human illness. Consumption of pork products is believed to be a major source of human salmonellosis and Salmonella control throughout the food-chain is recommended. A number of on-farm interventions have been proposed, and some have been implemented in order to try to achieve Salmonella control. In this study we utilize previously developed models describing Salmonella dynamics to investigate the potential effects of a range of these on-farm interventions. As the models indicated that the number of bacteria shed in the faeces of an infectious animal was a key factor, interventions applied within a high-shedding scenario were also analysed. From simulation of the model, the probability of infection after Salmonella exposure was found to be a key driver of Salmonella transmission. The model also highlighted that minimising physiological stress can have a large effect but only when shedding levels are not excessive. When shedding was high, weekly cleaning and disinfection was not effective in Salmonella control. However it is possible that cleaning may have an effect if conducted more often. Furthermore, separating infectious animals, shedding bacteria at a high rate, from the rest of the population was found to be able to minimise the spread of Salmonella.

Highlights • We model Salmonella transmission around varying structures of pig farm within the UK. • We formulate and analyse the basic reproduction number for this system. • Identify the shedding rate to be of particular importance in Salmonella spread. • Highlight differences in Salmonella dynamics between differing farm structures., A multi-group semi-stochastic model is formulated to describe Salmonella dynamics on a pig herd within the UK and assess whether farm structure has any effect on the dynamics. The models include both direct transmission and indirect (via free-living infectious units in the environment and airborne infection). The basic reproduction number R0 is also investigated. The models estimate approximately 24.6% and 25.4% of pigs at slaughter weight will be infected with Salmonella within a slatted-floored and solid-floored unit respectively, which corresponds to values found in previous abattoir and farm studies, suggesting that the model has reasonable validity. Analysis of the models identified the shedding rate to be of particular importance in the control of Salmonella spread, a finding also evident in an increase in the R0 value.

SUMMARYSalmonellais the second most commonly reported human foodborne pathogen in England and Wales, and antimicrobial-resistant strains ofSalmonellaare an increasing problem in both human and veterinary medicine. In this work we used a generalized linear spatial model to estimate the spatial and temporal patterns of antimicrobial resistance inSalmonellaTyphimurium in England and Wales. Of the antimicrobials considered we found a common peak in the probability that anS. Typhimurium incident will show resistance to a given antimicrobial in late spring and in mid to late autumn; however, for one of the antimicrobials (streptomycin) there was a sharp drop, over the last 18 months of the period of investigation, in the probability of resistance. We also found a higher probability of resistance in North Wales which is consistent across the antimicrobials considered. This information contributes to our understanding of the epidemiology of antimicrobial resistance inSalmonella.

Salmonella spp. are important food-borne pathogens. Abattoir studies demonstrated that almost a quarter of British finisher pigs might carry Salmonella, which led to the introduction by the British Pig Executive of their Zoonoses Action Plan (ZAP) to monitor the Salmonella status of United Kingdom pig farms by testing meat juice samples using an ELISA system. We used the K-function and approaches from the field of geostatistics to study routine data from ZAP. We demonstrated that there is statistical evidence that geographically localized anomalies of Salmonella infection were present in one of three regions studied. The physical mechanisms underlying this structure remain unclear: spatial structure might be present as a result of shared spatially structured (second-order) or non-spatially structured (first-order) risk factors, transmission processes, or a combination of both. We have demonstrated a way to use routinely collected surveillance data to enhance the knowledge of spatial disease epidemiology.

Background Colic is an important cause of mortality and morbidity in domesticated horses yet many questions about this condition remain to be answered. One such question is: does season have an effect on the occurrence of colic? Time-series analysis provides a rigorous statistical approach to this question but until now, to our knowledge, it has not been used in this context. Traditional time-series modelling approaches have limited applicability in the case of relatively rare diseases, such as specific types of equine colic. In this paper we present a modelling approach that respects the discrete nature of the count data and, using a regression model with a correlated latent variable and one with a linear trend, we explored the seasonality of specific types of colic occurring at a UK referral hospital between January 1995–December 2004. Results Six- and twelve-month cyclical patterns were identified for all colics, all medical colics, epiploic foramen entrapment (EFE), equine grass sickness (EGS), surgically treated and large colon displacement/torsion colic groups. A twelve-month cyclical pattern only was seen in the large colon impaction colic group. There was no evidence of any cyclical pattern in the pedunculated lipoma group. These results were consistent irrespective of whether we were using a model including latent correlation or trend. Problems were encountered in attempting to include both trend and latent serial dependence in models simultaneously; this is likely to be a consequence of a lack of power to separate these two effects in the presence of small counts, yet in reality the underlying physical effect is likely to be a combination of both. Conclusion The use of a regression model with either an autocorrelated latent variable or a linear trend has allowed us to establish formally a seasonal component to certain types of colic presented to a UK referral hospital over a 10 year period. These patterns appeared to coincide with either times of managemental change or periods when horses are more likely to be intensively managed. Further studies are required to identify the determinants of the observed seasonality. Importantly, this type of regression model has applications beyond the study of equine colic and it may be useful in the investigation of seasonal patterns in other, relatively rare, conditions in all species.

This chapter describes progress towards the development of more objective methods for discriminating between the neuropathological features produced by different TSE strains, in particular with respect to the vacuolation of brain tissue. We examine data on patterns of vacuolation in the brain tissue of mice challenged with different TSEs, and assess three separate aspects of the pattern: vacuole counts; vacuole sizes; and spatial distribution of vacuoles. The long-term goal is to develop a discriminant rule which can be used to identify a particular TSE strain should it arise in a group of animals.

Water samples were taken systematically from a 100-km 2 area of mainly dairy farmland in northwestern England and examined for Campylobacter spp. Pulsed-field gel electrophoresis-restriction fragment length polymorphism (PFGE-RFLP) and flaA strain typing of Campylobacter jejuni and Campylobacter coli isolates were done. Data on the water source and the adjacent environment were recorded and examined as explanatory variables. Campylobacter spp. were isolated from 40.5% ( n = 119) of the water samples tested. C. jejuni was isolated from 14.3%, C. coli was isolated from 18.5%, and Campylobacter lari was isolated from 4.2% of the samples. Campylobacter hyointestinalis was not isolated from any water source. The difference in prevalence between water types (trough, running, and standing) was significant ( P = 0.001). C. jejuni was the species most commonly isolated from trough-water and running-water sources, while C. coli was the most frequently isolated from standing water ( P < 0.001). No association was found between the presence of Escherichia coli and that of Campylobacter spp. The final multivariable logistic regression model for Campylobacter spp. included the following variables: water source, soil type, aspect, and amount of cattle fecal material in the environment (fecal pat count). Strain typing demonstrated a diverse population of C. jejuni and the presence of a common C. coli flaA type that was widely distributed throughout the area. Most of the isolates within the common flaA type were discriminated by PFGE-RFLP. These findings suggest a possible role for environmental water in the epidemiology of Campylobacter spp. in a farming environment.

Humans are exposed to Campylobacter spp. in a range of sources via both food and environmental pathways. For this study, we explored the frequency and distribution of thermophilic Campylobacter spp. in a 10- by 10-km square rural area of Cheshire, United Kingdom. The area contains approximately 70, mainly dairy, farms and is used extensively for outdoor recreational activities. Campylobacter spp. were isolated from a range of environmental samples by use of a systematic sampling grid. Livestock (mainly cattle) and wildlife feces and environmental water and soil samples were cultured, and isolates were presumptively identified by standard techniques. These isolates were further characterized by PCR. Campylobacter jejuni was the most prevalent species in all animal samples, ranging from 11% in samples from nonavian wildlife to 36% in cattle feces, and was isolated from 15% of water samples. Campylobacter coli was commonly found in water (17%) and sheep (21%) samples, but rarely in other samples. Campylobacter lari was recovered from all sample types, with the exception of sheep feces, and was found in moderate numbers in birds (7%) and water (5%). Campylobacter hyointestinalis was only recovered from cattle (7%) and birds (1%). The spatial distribution and determinants of C. jejuni in cattle feces were examined by the use of model-based spatial statistics. The distribution was consistent with very localized within-farm or within-field transmission and showed little evidence of any larger-scale spatial dependence. We concluded that there is a potentially high risk of human exposure to Campylobacter spp., particularly C. jejuni , in the environment of our study area. The prevalence and likely risk posed by C. jejuni -positive cattle feces in the environment diminished as the fecal material aged. After we took into account the age of the fecal material, the absence or presence of rain, and the presence of bird feces, there was evidence of significant variation in the prevalence of C. jejuni -positive cattle feces between grazing fields but no evidence of spatial clustering beyond this resolution. The spatial pattern of C. jejuni is therefore consistent with that for an organism that is ubiquitous in areas contaminated with cattle feces, with a short-scale variation in infection intensity that cannot be explained solely by variations in the age of the fecal material. The observed pattern is not consistent with large-scale transmission attributable to watercourses, wildlife territories, or other geographical features that transcend field and farm boundaries.

We examine the common clinical belief that there is an interaction between epilepsy type and the two standard anti-epileptic drugs, valproate and carbamazepine, using data from several randomized clinical trials. Epilepsy type is not always easy to define, and three possible reclassifications are investigated to see whether misclassification of epilepsy type within the trials has potentially masked such an interaction. Regression modelling is employed to investigate whether heterogeneity between trial results can be explained by patient factors. Our work suggests that uncertainty in epilepsy type classification should be recognized in future studies. We also generate the hypothesis that the interaction of drug effect with age may reflect the perceived interaction with epilepsy type. We suggest that in any context where misclassification is likely, it is worth considering the use of an explicit 'unclassified' group, and investigating whether additional covariates can answer the substantive question.

Background Carbamazepine and valproate are drugs of first choice for epilepsy. Despite the lack of hard evidence from individual randomized controlled trials, there is strong clinical belief that valproate is the drug of choice for generalized epilepsies and carbamazepine for partial epilepsies. Objectives To overview the best evidence comparing carbamazepine and valproate monotherapy Search methods We searched the Cochrane Epilepsy Group's Specialized Register (27 July 2007), the Cochrane Central Register of Controlled Trials (CENTRAL) (The Cochrane Library Issue 3, 2007), and MEDLINE (1966 to July 2007). No language restrictions were imposed. We also contacted pharmaceutical companies and researchers in the field. Selection criteria Randomized controlled trials comparing carbamazepine and valproate monotherapy for epilepsy. Data collection and analysis This was an individual patient data review. Outcome measures were time to withdrawal of allocated treatment, time to 12-month remission, and time to first seizure post randomization. Data were analysed using the stratified logrank test with results expressed as hazard ratios (HR) with 95% confidence intervals (CIs), where HR > 1 indicates an event is more likely on valproate. A test for an interaction between treatment and epilepsy type (partial versus generalized) was also undertaken. Main results Results data were available for 1265 participants from five trials, representing 85% of the participants recruited into the eight trials that met our inclusion criteria. The main overall results (HR) were: time to treatment withdrawal 0.97 (95% CI 0.79 to 1.18); 12 month remission 0.87 (95% CI 0.74 to 1.02); first seizure 1.09 (95% CI 0.96 to 1.25) suggesting no overall difference for these outcomes. The test for an interaction between treatment and epilepsy type was non significant for time to treatment withdrawal and 12-month remission, but significant for time to first seizure. The age distribution of adults classified as having a generalized epilepsy indicate that significant numbers of individuals may have had their epilepsy misclassified. Authors' conclusions We have found some evidence to support the policy of using carbamazepine as the first treatment of choice in partial epilepsies, but no evidence to support the choice of valproate in generalized epilepsies, but confidence intervals are too wide to confirm equivalence. Misclassification of people with epilepsy may have confounded our results, and has important implications for the design and conduct of future trials.

Antimicrobial products are used in the treatment of many conditions in companion animals and their use is important in maintaining animal health and welfare. Responsible antimicrobial usage is an important element in long-term infectious disease control. In order to maintain the effectiveness of

Background The quantification of uncertainty and variability is a key component of quantitative risk analysis. Recent advances in Bayesian statistics make it ideal for integrating multiple sources of information, of different types and quality, and providing a realistic estimate of the combined uncertainty in the final risk estimates. Methods We present two case studies related to foodborne microbial risks. In the first, we combine models to describe the sequence of events resulting in illness from consumption of milk contaminated with VTEC O157. We used Monte Carlo simulation to propagate uncertainty in some of the inputs to computer models describing the farm and pasteurisation process. Resulting simulated contamination levels were then assigned to consumption events from a dietary survey. Finally we accounted for uncertainty in the dose-response relationship and uncertainty due to limited incidence data to derive uncertainty about yearly incidences of illness in young children. Options for altering the risk were considered by running the model with different hypothetical policy-driven exposure scenarios. In the second case study we illustrate an efficient Bayesian sensitivity analysis for identifying the most important parameters of a complex computer code that simulated VTEC O157 prevalence within a managed dairy herd. This was carried out in 2 stages, first to screen out the unimportant inputs, then to perform a more detailed analysis on the remaining inputs. The method works by building a Bayesian statistical approximation to the computer code using a number of known code input/output pairs (training runs). Results We estimated that the expected total number of children aged 1.5-4.5 who become ill due to VTEC O157 in milk is 8.6 per year, with 95% uncertainty interval (0,11.5). The most extreme policy we considered was banning on-farm pasteurisation of milk, which reduced the estimate to 6.4 with 95% interval (0,11). In the second case study the effective number of inputs was reduced from 30 to 7 in the screening stage, and just 2 inputs were found to explain 82.8% of the output variance. A combined total of 500 runs of the computer code were used. Conclusion These case studies illustrate the use of Bayesian statistics to perform detailed uncertainty and sensitivity analyses, integrating multiple information sources in a way that is both rigorous and efficient.

Erratum to “Bayesian methods for estimating pathogen prevalence within groups of animals from faecal-pat sampling” [Prev. Vet. Med. 58 (2003) 145–169] H.E. Clough a,∗, D. Clancy b, P.D. O’Neill c, N.P. French a a Department of Veterinary Clinical Sciences, University of Liverpool, “Leahurst”, Neston, South Wirral CH64 7TE, UK b Department of Mathematical Sciences, Division of Statistics and Operational Research, University of Liverpool, Peach Street, Liverpool L69 7ZL, UK c School of Mathematical Sciences, University of Nottingham, University Park, Nottingham NG7 2RD, UK