Coupling models of cattle and farms with models of badgers for predicting the dynamics of bovine tuberculosis (TB)

Bovine TB is a major problem for the agricultural industry in several countries. TB can be contracted and spread by species other than cattle and this can cause a problem for disease control. In the UK and Ireland, badgers are a recognised reservoir of infection and there has been substantial discussion about potential control strategies. We present a coupling of individual based models of bovine TB in badgers and cattle, which aims to capture the key details of the natural history of the disease and of both species at approximately county scale. The model is spatially explicit it follows a very large number of cattle and badgers on a different grid size for each species and includes also winter housing. We show that the model can replicate the reported dynamics of both cattle and badger populations as well as the increasing prevalence of the disease in cattle. Parameter space used as input in simulations was swept out using Latin hypercube sampling and sensitivity analysis to model outputs was conducted using mixed effect models. By exploring a large and computationally intensive parameter space we show that of the available control strategies it is the frequency of TB testing and whether or not winter housing is practised that have the most significant effects on the number of infected cattle, with the effect of winter housing becoming stronger as farm size increases. Whether badgers were culled or not explained about 5%, while the accuracy of the test employed to detect infected cattle explained less than 3% of the variance in the number of infected cattle

Network epidemiology of cattle and cattle farms in Great Britain

Infectious diseases of livestock can cause substantial production losses and have detrimental impacts upon human health, and animal health and welfare. To limit the impact of diseases, understanding more about the dynamics of transmission can assist in the control and prevention of infectious disease. In particular, understanding infection transmission on networks, ‘network epidemiology’, offers a flexible approach, incorporating between-host heterogeneity in potentially infectious contacts drawn from empirical study of interactions among individual animals, or among farms. Trading animals and optimising productivity are vital to the commercial viability of farms, however they necessarily involve compromises in biosecurity, animal health, and welfare. Better understanding of the relationships among these multiple factors might facilitate the development of sustainable livestock industries that are more resilient to disease outbreaks. In this thesis I examine cattle interactions at two spatial scales, first at a national-level by studying the trading connections among farms, and then at a finer scale by analysing the social interactions among cattle. First, I introduce the concept of superspreaders, hosts that generate many more secondary infections than the rest of the population, and evaluate evidence for the notion that some farms might act as superspreaders of infection. I utilise the example of bovine tuberculosis (bTB) to illustrate this concept and find that farms might act as superspreaders in three main ways; first, via exceptional trading between farms, second, by factors that facilitate high within-herd transmission and trading of high-risk animals, and third, by harbouring undetected infection for long periods. I find mechanisms that align with all three processes in the cattle industry in Great Britain that might allow superspreader farms to contribute to the current bTB epidemic. At a national level, I describe cattle movements among farms over time, finding that some farms consistently act as ‘hubs’ in trading networks, functioning in a similar way to markets, in that they are highly connected to other farms by many direct trades. Utilising the temporal network measure of ‘contact chains’, I quantify the farms that represent potential sources of infection (ingoing contact chains) and the potential farms that a farm might infect over 1 year periods. Farms divide into two groups: those with very few connections (less than 10 farms) that are relatively isolated from the network, and those with very many connections (more than 1000 farms) that are highly connected within the network. I find that a substantial number of farms have over 10,000 farms in both their ingoing and outgoing contact chains, such that, if infected, they might potentially act as superspreaders by being more at risk of both acquiring and spreading infection. Building on my previous analysis, I then characterise the ‘source farms’ in the ingoing contact chains, in terms of their location and bTB history. I find that after controlling for previously-established risk factors for bTB, having more source farms in areas of higher bTB risk in the ingoing contact chain increases the odds of a bTB incident on the root farm, whilst having more source farms in lower risk areas is associated with lower odds of a bTB incident on the root farm. At a finer scale of contacts among animals, I explore interactions among dairy cattle in multiple herds using automated proximity sensors and GPS devices. When aggregated over long periods, cattle interactions appear dense and unstructured, however finer time spatial and temporal perspectives revealed structure and variation in contacts. Herds in our study had variable grazing and housing access, allowing us to determine that cattle interact with more other cows, for longer time periods when they are in buildings compared to contacts at pasture. Cattle exhibited heterogeneity in their number and duration of contacts, and although the majority of cattle interacted more equally with other cows, a small proportion of cows in each group showed evidence of stronger social ties. Next, I consider associations between social interactions, production, and health. I review the existing literature on social parameters such as dominance rank and re-grouping of cattle, and find inconclusive outcomes regarding their impact on milk yield and somatic cell count, an indicator of udder health. I perform my own analysis to examine the relationship between the time cows spend with other cows, milk yield and somatic cell count, and do not find a statistically significant relationship. In considering social preference, cows that had experienced the same number of lactations were more likely to interact, but cows spending more time with cows in the same lactation did not appreciably affect their milk yield or somatic cell count. Finally, I draw together the findings of this thesis and reflect on how the identification of higher-risk farms might be useful in the control of livestock infections, and specifically bTB in Great Britain. I conclude that network analysis is a valuable tool to study the interactions of cattle and cattle farms, identifying unique opportunities for targeted approaches to disease control.Animal Health Veterinary Laboratories Agency BBSRC - BB/M015874/

Characterization of potential superspreader farms for bovine tuberculosis: A review

This is the final version. Available on open access from Wiley via the DOI in this recordBACKGROUND: Variation in host attributes that influence their contact rates and infectiousness can lead some individuals to make disproportionate contributions to the spread of infections. Understanding the roles of such 'superspreaders' can be crucial in deciding where to direct disease surveillance and controls to greatest effect. In the epidemiology of bovine tuberculosis (bTB) in Great Britain, it has been suggested that a minority of cattle farms or herds might make disproportionate contributions to the spread of Mycobacterium bovis, and hence might be considered 'superspreader farms'. OBJECTIVES AND METHODS: We review the literature to identify the characteristics of farms that have the potential to contribute to exceptional values in the three main components of the farm reproductive number - Rf : contact rate, infectiousness and duration of infectiousness, and therefore might characterize potential superspreader farms for bovine tuberculosis in Great Britain. RESULTS: Farms exhibit marked heterogeneity in contact rates arising from between-farm trading of cattle. A minority of farms act as trading hubs that greatly augment connections within cattle trading networks. Herd infectiousness might be increased by high within-herd transmission or the presence of supershedding individuals, or infectiousness might be prolonged due to undetected infections or by repeated local transmission, via wildlife or fomites. CONCLUSIONS: Targeting control methods on putative superspreader farms might yield disproportionate benefits in controlling endemic bovine tuberculosis in Great Britain. However, real-time identification of any such farms, and integration of controls with industry practices, present analytical, operational and policy challenges.Biotechnology and Biological Sciences Research Council (BBSRC)Animal and Plant Health Agenc

Characterization of potential superspreader farms for bovine tuberculosis:A review

Background: Variation in host attributes that influence their contact rates and infectiousness can lead some individuals to make disproportionate contributions to the spread of infections. Understanding the roles of such ‘superspreaders’ can be crucial in deciding where to direct disease surveillance and controls to greatest effect. In the epidemiology of bovine tuberculosis (bTB) in Great Britain, it has been suggested that a minority of cattle farms or herds might make disproportionate contributions to the spread of Mycobacterium bovis, and hence might be considered ‘superspreader farms’.Objectives and Methods: We review the literature to identify the characteristics of farms that have the potential to contribute to exceptional values in the three main components of the farm reproductive number - Rf: contact rate, infectiousness and duration of infectiousness, and therefore might characterize potential superspreader farms for bovine tuberculosis in Great Britain.Results: Farms exhibit marked heterogeneity in contact rates arising from between-farm trading of cattle. A minority of farms act as trading hubs that greatly augment connections within cattle trading networks. Herd infectiousness might be increased by high within-herd transmission or the presence of supershedding individuals, or infectiousness might be prolonged due to undetected infections or by repeated local transmission, via wildlife or fomites.Conclusions: Targeting control methods on putative superspreader farms might yield disproportionate benefits in controlling endemic bovine tuberculosis in Great Britain. However, real-time identification of any such farms, and integration of controls with industry practices, present analytical, operational and policy challenges.<br/

Identifying genotype specific elevated-risk areas and associated herd risk factors for bovine tuberculosis spread in British cattle

Bovine tuberculosis (bTB) is a chronic zoonosis with major health and economic impact on the cattle industry. Despite extensive control measures in cattle and culling trials in wildlife, the reasons behind the expansion of areas with high incidence of bTB breakdowns in Great Britain remain unexplained. By balancing the importance of cattle movements and local transmission on the observed pattern of cattle outbreaks, we identify areas at elevated risk of infection from specific Mycobacterium bovis genotypes. We show that elevated-risk areas (ERAs) were historically more extensive than previously understood, and that cattle movements alone are insufficient for ERA spread, suggesting the involvement of other factors. For all genotypes, we find that, while the absolute risk of infection is higher in ERAs compared to areas with intermittent risk, the statistically significant risk factors are remarkably similar in both, suggesting that these risk factors can be used to identify incipient ERAs before this is indicated by elevated incidence alone. Our findings identify research priorities for understanding bTB dynamics, improving surveillance and guiding management to prevent further ERA expansion

Complex responses to movement-based disease control: when livestock trading helps

Livestock disease controls are often linked to movements between farms, for example, via quarantine and pre- or post-movement testing. Designing effective controls, therefore, benefits from accurate assessment of herd-to-herd transmission. Household models of human infections make use of R*, the number of groups infected by an initial infected group, which is a metapopulation level analogue of the basic reproduction number R0 that provides a better characterization of disease spread in a metapopulation. However, existing approaches to calculate R* do not account for individual movements between locations which means we lack suitable tools for livestock systems. We address this gap using next-generation matrix approaches to capture movements explicitly and introduce novel tools to calculate R* in any populations coupled by individual movements. We show that depletion of infectives in the source group, which hastens its recovery, is a phenomenon with important implications for design and efficacy of movement-based controls. Underpinning our results is the observation that R* peaks at intermediate livestock movement rates. Consequently, under movement-based controls, infection could be controlled at high movement rates but persist at intermediate rates. Thus, once control schemes are present in a livestock system, a reduction in movements can counterintuitively lead to increased disease prevalence. We illustrate our results using four important livestock diseases (bovine viral diarrhoea, bovine herpes virus, Johne's disease and Escherichia coli O157) that each persist across different movement rate ranges with the consequence that a change in livestock movements could help control one disease, but exacerbate another

A big-data spatial, temporal and network analysis of bovine tuberculosis between wildlife (badgers) and cattle

postprin

Impact of imperfect test sensitivity on determining risk factors : the case of bovine tuberculosis

Background Imperfect diagnostic testing reduces the power to detect significant predictors in classical cross-sectional studies. Assuming that the misclassification in diagnosis is random this can be dealt with by increasing the sample size of a study. However, the effects of imperfect tests in longitudinal data analyses are not as straightforward to anticipate, especially if the outcome of the test influences behaviour. The aim of this paper is to investigate the impact of imperfect test sensitivity on the determination of predictor variables in a longitudinal study. Methodology/Principal Findings To deal with imperfect test sensitivity affecting the response variable, we transformed the observed response variable into a set of possible temporal patterns of true disease status, whose prior probability was a function of the test sensitivity. We fitted a Bayesian discrete time survival model using an MCMC algorithm that treats the true response patterns as unknown parameters in the model. We applied our approach to epidemiological data of bovine tuberculosis outbreaks in England and investigated the effect of reduced test sensitivity in the determination of risk factors for the disease. We found that reduced test sensitivity led to changes to the collection of risk factors associated with the probability of an outbreak that were chosen in the ‘best’ model and to an increase in the uncertainty surrounding the parameter estimates for a model with a fixed set of risk factors that were associated with the response variable. Conclusions/Significance We propose a novel algorithm to fit discrete survival models for longitudinal data where values of the response variable are uncertain. When analysing longitudinal data, uncertainty surrounding the response variable will affect the significance of the predictors and should therefore be accounted for either at the design stage by increasing the sample size or at the post analysis stage by conducting appropriate sensitivity analyses

Contact chains of cattle farms in Great Britain

This is the final version. Available from the publisher via the DOI in this record.Further information and figures supporting this article have been uploaded as part of the electronic supplementary material. Underlying data consist of every movement of cattle between all farms in Great Britain. Aside from the size of the dataset, there are substantial issues of confidentiality (locations, trading practices) and commercial sensitivity in these data. They are collated and managed by Defra, via the Animal and Plant Health Agency, who grant access to the data with specific permissions for specific studies. In practice, this means that the data can be used for the stated purpose only, and making the data publicly accessible would not conform to the licence the authors have been granted to use these data. With the agreement of the journal’s Editorial Office, the authors will not be able to make the dataset available on this occasion, but encourage readers, referees and editors to contact the Animal and Plant Health Agency data manager for data access requests. At the time of submission, the data manager is Andy Mitchell ([email protected])Network analyses can assist in predicting the course of epidemics. Time-directed paths or ‘contact chains’ provide a measure of hostconnectedness across specified timeframes, and so represent potential pathways for spread of infections with different epidemiological characteristics. We analysed networks and contact chains of cattle farms in Great Britain using Cattle Tracing System data from 2001 to 2015. We focused on the potential for between-farm transmission of bovine tuberculosis, a chronic infection with potential for hidden spread through the network. Networks were characterized by scale-free type properties, where individual farms were found to be influential ‘hubs’ in the network. We found a markedly bimodal distribution of farms with either small or very large ingoing and outgoing contact chains (ICCs and OCCs). As a result of their cattle purchases within 12-month periods, 47% of British farms were connected by ICCs to more than 1000 other farms and 16% were connected to more than 10 000 other farms. As a result of their cattle sales within 12-month periods, 66% of farms had OCCs that reached more than 1000 other farms and 15% reached more than 10 000 other farms. Over 19 000 farms had both ICCs and OCCs reaching more than 10 000 farms for two or more years. While farms with more contacts in their ICCs or OCCs might play an important role in disease spread, farms with extensive ICCs and OCCs might be particularly important by being at higher risk of both acquiring and disseminatinginfections.Biotechnology and Biological Science Research Council (BBSRC