Predicting Norovirus in England Using Existing and Emerging Syndromic Data: Infodemiology Study.

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.

Diagnosis of Bovine Digital Dermatitis: Exploring the Usefulness of Indirect ELISA.

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.

Differential impact of the COVID-19 pandemic on laboratory reporting of norovirus and Campylobacter in England: A modelling approach.

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.

Societal Impacts of Pandemics: Comparing COVID-19 With History to Focus Our Response.

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 <100 years ago, pandemic disease events were still largely uncontrolled and unexplained. The extensive damage wreaked by historical pandemics on health, economy, and society was a function of pathogen characteristics and lack of public health resources. Though there remain many similarities in patterns of disease spread and response from 1300 onwards, the major risks posed by COVID-19 arise not from the pathogen, but from indirect effects of control measures on health and core societal activities. Our understanding of the epidemiology and effective treatment of this virus has rapidly improved and attention is shifting toward the identification of long-term control strategies that balance consideration of health in at risk populations, societal behavior, and economic impact. Policymakers should use lessons from previous pandemics to develop appropriate risk assessments and control plans for now-endemic COVID-19, and for future pandemics.

Use of routine death and illness surveillance data to provide insight for UK pandemic planning: lessons from COVID-19.

OBJECTIVES: Reporting 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. SETTING: UK, 2020. PARTICIPANTS: The 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 MEASURES: Our 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. RESULTS: Deaths 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. CONCLUSION: Early 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.

Estimating the burden of care home gastroenteritis outbreaks in England, 2014-2016.

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.

Foodborne and Food-Handler Norovirus Outbreaks: A Systematic Review.

Norovirus (NoV) is the commonest cause of gastrointestinal disease in the United Kingdom and in many developed countries, causing diarrhea and vomiting in millions of cases worldwide annually. Transmission is most often mediated from person to person. NoV infection has, however, additionally been associated with the consumption of food, either through the consumption of food contaminated at source such as seafood, berries, and salad, or as a consequence of the foodstuff being contaminated in some way by a food handler during processing or serving. A systematic review of outbreaks attributed to NoV between January 2003 and July 2017 was conducted to assess the contribution of food handlers to the burden of NoV, and to identify foods commonly associated with NoV outbreaks. A total of 3021 articles were screened, of which 27 met the definition of confirmed foodborne outbreaks and 47 met the criteria for definite food-handler NoV outbreaks. Of all food types, shellfish were implicated in the greatest number of definite foodborne outbreaks. Food handlers contributed to definite food-handler outbreaks involving a diverse range of foodstuffs and in a wide variety of settings, including weddings and military establishments. More genotypes of NoV were found in people who were ill than in samples from food and food handlers. The potential for both food products and food handlers to contribute to the burden of NoV infection is demonstrated conclusively.

Development and validation of a protocol to identify and recruit participants into a large scale study on liver fluke in cattle.

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.

Effectiveness of simulated interventions in reducing the estimated prevalence of Salmonella in UK pig herds.

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.

Semi-stochastic models for Salmonella infection within finishing pig units in the UK.

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.

Evidence from the UK Zoonoses Action Plan in favour of localised anomalies of Salmonella infection on United Kingdom pig farms.

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.

Quantifying exposure to vero-cytotoxigenic Escherichia coli O157 in milk sold as pasteurized: a model-based approach.

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.

Case studies in Bayesian microbial risk assessments.

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.

Ecological differences and coexistence in a guild of microparasites: Bartonella in wild rodents.

The study of ecological differences among coexisting microparasites has been largely neglected, but it addresses important and unusual issues because there is no clear distinction in such cases between conventional (resource) and apparent competition. Here patterns in the population dynamics are examined for four species of Bartonella (bacterial parasites) coexisting in two wild rodent hosts, bank voles (Clethrionomys glareolus) and wood mice (Apodemus sylvaticus). Using generalized linear modeling and mixed effects models, we examine, for these four species, seasonal patterns and dependencies on host density (both direct and delayed) and, having accounted for these, any differences in prevalence between the two hosts. Whereas previous studies had failed to uncover species differences, here all four were different. Two, B. doshiae and B. taylorii, were more prevalent in wood mice, and one, B. birtlesii, was more prevalent in bank voles. B. birtlesii, B. grahamii, and B. taylorii peaked in prevalence in the fall, whereas B. doshiae peaked in spring. For B. birtlesii in bank voles, density dependence was direct, but for B. taylorii in wood mice density dependence was delayed. B. birtlesii prevalence in wood mice was related to bank vole density. The implications of these differences for species coexistence are discussed.

Vero-cytotoxigenic Escherichia coli O157 in pasteurized milk containers at the point of retail: a qualitative approach to exposure assessment.

Dairies within the United Kingdom are classified into two groups, namely, off-farm and on-farm dairies (the latter often being small scale). We propose a model for the probability of milk sold as pasteurized reaching the point of retail contaminated with Vero-cytotoxigenic Escherichia coli (VTEC) O157 from each of these two pathways. We evaluate qualitatively the exposures inherent in each, and compare and contrast the two situations. The model framework is generic, in that it can in principle be used, with the relevant data modifications, to provide a qualitative assessment of the likely exposure from milk sold as pasteurized to any potentially milk-borne pathogenic organism. Furthermore, the methodological approaches presented are widely applicable in the microbial risk assessment field. The specific example presented will be of particular interest to the UK dairy and public health communities: we conclude that the exposure potential per liter consumed from milk processed in off-farm dairies is negligible, whereas the exposure potential per liter consumed from milk processed on-farm is low, but not sufficiently small to be regarded as negligible. We also identify areas of data sparsity, which need to be addressed for quantitative risk assessment to proceed, and highlight the critical points in the pasteurized milk production chain, which, in the event of a breakdown, have the potential to increase the risk to the consumer.

Is equine colic seasonal? Novel application of a model based approach.

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.

Statistical issues in the assessment of the evidence for an interaction between factors in epilepsy trials.

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.