Factors associated with herd restriction and de-restriction with bovine tuberculosis in British cattle herds.

The incidence of herd breakdown (HBD) with bovine tuberculosis (bTB) has continued to increase year on year since the 1980s in Great Britain. The management of bTB constitutes a major challenge for government and the cattle industry. Whilst various factors have been implicated with the risk of HBD with bTB, factors involved in recovery are less well described. In this paper, we used a multilevel multistate model to identify the factors affecting the probability of a herd being placed under restriction following a bTB outbreak and the factors involved in those restrictions being lifted. By modelling both transitions within the same model, we control for unobserved herd-specific characteristics, and investigate the frequency of change between the restricted and derestricted states. There were two patterns of herd breakdown: transient (characterised by fast cycling between restricted and derestricted states) and continuous (characterised by rare changes between the two states). The risk of a herd being placed under restriction was dominated by predictors related to cattle movements. The probability of derestriction increased with more regular testing. Some risks affected both transitions, namely loge mean size of neighbouring herds in the test-year, whether the herd bred its own replacements and the foot and mouth disease indicator of whether a bTB test was done between February 2002 and January 2003, possibly because the underlying true state of the herd, as infected or not, meant that these factors increased or reduced the risk of HBD. These results highlight that management of bTB is dependent on the true underlying herd status of bTB infection and that some confusion of the benefits or otherwise of some management practices, e.g. using home bred replacements can be explained by this underlying status.

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.

The spread of bluetongue virus serotype 8 in Great Britain and its control by vaccination.

BACKGROUND: Bluetongue (BT) is a viral disease of ruminants transmitted by Culicoides biting midges and has the ability to spread rapidly over large distances. In the summer of 2006, BTV serotype 8 (BTV-8) emerged for the first time in northern Europe, resulting in over 2000 infected farms by the end of the year. The virus subsequently overwintered and has since spread across much of Europe, causing tens of thousands of livestock deaths. In August 2007, BTV-8 reached Great Britain (GB), threatening the large and valuable livestock industry. A voluntary vaccination scheme was launched in GB in May 2008 and, in contrast with elsewhere in Europe, there were no reported cases in GB during 2008. METHODOLOGY/PRINCIPAL FINDINGS: Here, we use carefully parameterised mathematical models to investigate the spread of BTV in GB and its control by vaccination. In the absence of vaccination, the model predicted severe outbreaks of BTV, particularly for warmer temperatures. Vaccination was predicted to reduce the severity of epidemics, with the greatest reduction achieved for high levels (95%) of vaccine uptake. However, even at this level of uptake the model predicted some spread of BTV. The sensitivity of the predictions to vaccination parameters (time to full protection in cattle, vaccine efficacy), the shape of the transmission kernel and temperature dependence in the transmission of BTV between farms was assessed. CONCLUSIONS/SIGNIFICANCE: A combination of lower temperatures and high levels of vaccine uptake (>80%) in the previously-affected areas are likely to be the major contributing factors in the control achieved in England in 2008. However, low levels of vaccination against BTV-8 or the introduction of other serotypes could result in further, potentially severe outbreaks in future.

Assessing the consequences of an incursion of a vector-borne disease. II. Spread of bluetongue in Scotland and impact of vaccination.

Bluetongue is a viral disease of ruminants transmitted by Culicoides biting midges, which has spread across Europe over the past decade. The disease arrived in south-east England in 2007, raising the possibility that it could pose a risk to the valuable Scottish livestock industry. As part of an assessment of the economic consequences of a bluetongue virus incursion into Scotland commissioned by Scottish Government, we investigated a defined set of feasible incursion scenarios under different vaccination strategies. Our epidemiological simulations, based on expert knowledge, highlighted that infection will rarely spread in Scotland after the initial incursion and will be efficiently controlled by vaccination.

Assessing the consequences of an incursion of a vector-borne disease I. Identifying feasible incursion scenarios for bluetongue in Scotland.

Following the arrival of bluetongue virus serotype 8 (BTV-8) in southeast England in September 2007, the Scottish Government commissioned research to assess the economic consequences of a BTV-8 incursion to Scotland. Here we present the first component of the assessment, which entailed identifying feasible incursion scenarios for the virus. Our analyses focused on three routes of introduction: wind-borne dispersal of infected vectors, import of infected animals and northwards spread of BTV from affected areas in GB. These analyses were further refined by considering the spatial and temporal variation in the probability of onward transmission from an initial incursion.

A modeling framework to describe the transmission of bluetongue virus within and between farms in Great Britain.

BACKGROUND: Recently much attention has been given to developing national-scale micro-simulation models for livestock diseases that can be used to predict spread and assess the impact of control measures. The focus of these models has been on directly transmitted infections with little attention given to vector-borne diseases such as bluetongue, a viral disease of ruminants transmitted by Culicoides biting midges. Yet BT has emerged over the past decade as one of the most important diseases of livestock. METHODOLOGY/PRINCIPAL FINDINGS: We developed a stochastic, spatially-explicit, farm-level model to describe the spread of bluetongue virus (BTV) within and between farms. Transmission between farms was modeled by a generic kernel, which includes both animal and vector movements. Once a farm acquired infection, the within-farm dynamics were simulated based on the number of cattle and sheep kept on the farm and on local temperatures. Parameter estimates were derived from the published literature and using data from the outbreak of bluetongue in northern Europe in 2006. The model was validated using data on the spread of BTV in Great Britain during 2007. The sensitivity of model predictions to the shape of the transmission kernel was assessed. CONCLUSIONS/SIGNIFICANCE: The model is able to replicate the dynamics of BTV in Great Britain. Although uncertainty remains over the precise shape of the transmission kernel and certain aspects of the vector, the modeling approach we develop constitutes an ideal framework in which to incorporate these aspects as more and better data become available. Moreover, the model provides a tool with which to examine scenarios for the spread and control of BTV in Great Britain.

Adaptive strategies of African horse sickness virus to facilitate vector transmission.

African horse sickness virus (AHSV) is an orbivirus that is usually transmitted between its equid hosts by adult Culicoides midges. In this article, we review the ways in which AHSV may have adapted to this mode of transmission. The AHSV particle can be modified by the pH or proteolytic enzymes of its immediate environment, altering its ability to infect different cell types. The degree of pathogenesis in the host and vector may also represent adaptations maximising the likelihood of successful vectorial transmission. However, speculation upon several adaptations for vectorial transmission is based upon research on related viruses such as bluetongue virus (BTV), and further direct studies of AHSV are required in order to improve our understanding of this important virus.

The population genomics of hepatitis B virus.

Hepatitis B virus (HBV) infection is considered as the fifth leading cause of death due to infectious diseases and has a worldwide prevalence. The particular geographical distribution of the eight previously defined genotypes of HBV suggests that the viral population is highly structured. The presence of such population structure is likely to affect the geographical distribution of polymorphisms involved in disease progression. In this study, we determined the structure of the HBV population using a clustering approach based on the observed allele frequencies at the polymorphic loci. We used all full-genome sequences publicly available and obtained a significant clustering of the HBV population into four main clusters, strongly associated with the current classification into genotypes. One of these main clusters could itself be split into three well-supported subclusters, highlighting the hierarchical nature of the population differentiation between HBV strains. The extremely clear-cut subdivision of the HBV population further indicates that recombination in HBV is not as extensive as previously assumed.

Identification and genetic diversity of two human parvovirus B19 genotype 3 subtypes.

Three genotypes (1-3) of human parvovirus B19 have been identified. Analysis of 13 nearly full-length genotype 3 sequences from Ghana, Europe and Brazil identified two genetically distinct clusters. The classification of genotype 3 strains into two subtypes (B19/3a and B19/3b) is proposed. The rate of evolutionary change of B19 genotype 3 strains (2 x 10(-4) nucleotide substitutions per site per year) was similar to those of B19 genotype 1 and carnivore parvoviruses, supporting the hypothesis that high mutation rates are characteristic of members of the family Parvoviridae. The estimated divergence time between B19/3a and B19/3b is 525 years. In Ghana, subtype B19/3a is predominant.

A novel approach to characterise pathogen candidate genetic polymorphisms involved in clinical outcome.

Understanding the key factors influencing the clinical outcome of an infection is crucial for early diagnosis and optimised treatment. Despite widespread recognition of the importance of the genetics composition of pathogens, most efforts so far have focused on characterising disease and susceptibility genes in humans. Here, we propose a new flexible and powerful methodological framework to detect candidate genetic polymorphisms influencing clinical outcome from pathogen genomes. The rationale is to use well-supported clades in a phylogeny as statistical predictors for clinical outcomes rather than the individual polymorphisms themselves. This greatly increases the statistical power to detect candidate polymorphisms when analysing a large number of variable sites. In a second step, the candidate polymorphisms are recovered by characterising the polymorphisms that most strongly support the clades predicting the clinical outcome. The modelling approach further allows including host factors and testing for possible interactions between factors. We illustrate the approach by an application on a dataset of hepatitis B polymerase genes. The statistical model retains age at infection as well as six candidate polymorphisms as predictors for clinical outcome (acute, chronic and fulminant). The method is straightforward to apply and computationally effective. While the approach is focused on detecting candidate polymorphisms from pathogen genomes, the method might be more broadly applied for characterising the link between genotype and phenotype while statistically controlling for environmental factors.