A comparison of discriminant logistic regression and Item Response Theory Likelihood-Ratio Tests for Differential Item Functioning (IRTLRDIF) in polytomous short tests

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An exploration of the drivers to bio-security collective action among a sample of UK cattle and sheep farmers

At present, collective action regarding bio-security among UK cattle and sheep farmers is rare. Despite the occurrence of catastrophic livestock diseases such as bovine spongiform encephalopathy (BSE) and foot and mouth disease (FMD), within recent decades, there are few national or local farmer-led animal health schemes. To explore the reasons for this apparent lack of interest, we utilised a socio-psychological approach to disaggregate the cognitive, emotive and contextual factors driving bio-security behaviour among cattle and sheep farmers in the United Kingdom (UK). In total, we interviewed 121 farmers in South-West England and Wales. The main analytical tools included a content, cluster and logistic regression analysis. The results of the content analysis illustrated apparent 'dissonance' between bio-security attitudes and behaviour.(1) Despite the heavy toll animal disease has taken on the agricultural economy, most study participants were dismissive of the many measures associated with bio-security. Justification for this lack of interest was largely framed in relation to the collective attribution or blame for the disease threats themselves. Indeed, epidemic diseases were largely related to external actors and agents. Reasons for outbreaks included inadequate border control, in tandem with ineffective policies and regulations. Conversely, endemic livestock disease was viewed as a problem for 'bad' farmers and not an issue for those individuals who managed their stock well. As such, there was little utility in forming groups to address what was largely perceived as an individual problem. Further, we found that attitudes toward bio-security did not appear to be influenced by any particular source of information per se. While strong negative attitudes were found toward specific sources of bio-security information, e.g. government leaflets, these appear to simply reflect widely held beliefs. In relation to actual bio-security behaviours, the logistic regression analysis revealed no significant difference between in-scheme and out of scheme farmers. We concluded that in order to support collective action with regard to bio-security, messages need to be reframed and delivered from a neutral source. Efforts to support group formation must also recognise and address the issues relating to perceptions of social connectedness among the communities involved. (c) 2008 Elsevier B.V. All rights reserved.

Use and abuse of mathematical models: an illustration from the 2001 foot and mouth disease epidemic in the United Kingdom

Foot and mouth disease (FMD) is a major threat, not only to countries whose economies rely on agricultural exports, but also to industrialised countries that maintain a healthy domestic livestock industry by eliminating major infectious diseases from their livestock populations. Traditional methods of controlling diseases such as FMD require the rapid detection and slaughter of infected animals, and any susceptible animals with which they may have been in contact, either directly or indirectly. During the 2001 epidemic of FMD in the United Kingdom (UK), this approach was supplemented by a culling policy driven by unvalidated predictive models. The epidemic and its control resulted in the death of approximately ten million animals, public disgust with the magnitude of the slaughter, and political resolve to adopt alternative options, notably including vaccination, to control any future epidemics. The UK experience provides a salutary warning of how models can be abused in the interests of scientific opportunism.

Risk of foot and mouth disease associated with proximity in space and time to infected premises and the implications for control policy during the 2001 epidemic in Cumbria

An analysis was made that calculated the risk of disease for premises in the most heavily affected parts of the county of Cumbria during the foot-and-mouth disease epidemic in the UK in 2001. In over half the cases the occurrence of the disease was not directly attributable to a recently infected premises being located within 1.5 km. Premises more than 1.5 km from recently infected premises faced sufficiently high infection risks that culling within a 1.5 km radius of the infected premises alone could not have prevented the progress of the epidemic. A comparison of the final outcome in two areas of the county, south Penrith and north Cumbria, indicated that focusing on controlling the potential spread of the disease over short distances by culling premises contiguous to infected premises, while the disease continued to spread over longer distances, may have resulted in excessive numbers of premises being culled. Even though the contiguous cull in south Penrith appeared to have resulted in a smaller proportion of premises becoming infected, the overall proportion of premises culled was considerably greater than in north Cumbria, where, because of staff and resource limitations, a smaller proportion of premises contiguous to infected premises was culled

The effects of environmental perturbation and measurement error on estimates of the shape parameter in the theta-logistic model of population regulation

The theta-logistic is a widely used generalisation of the logistic model of regulated biological processes which is used in particular to model population regulation. Then the parameter theta gives the shape of the relationship between per-capita population growth rate and population size. Estimation of theta from population counts is however subject to bias, particularly when there are measurement errors. Here we identify factors disposing towards accurate estimation of theta by simulation of populations regulated according to the theta-logistic model. Factors investigated were measurement error, environmental perturbation and length of time series. Large measurement errors bias estimates of theta towards zero. Where estimated theta is close to zero, the estimated annual return rate may help resolve whether this is due to bias. Environmental perturbations help yield unbiased estimates of theta. Where environmental perturbations are large, estimates of theta are likely to be reliable even when measurement errors are also large. By contrast where the environment is relatively constant, unbiased estimates of theta can only be obtained if populations are counted precisely Our results have practical conclusions for the design of long-term population surveys. Estimation of the precision of population counts would be valuable, and could be achieved in practice by repeating counts in at least some years. Increasing the length of time series beyond ten or 20 years yields only small benefits. if populations are measured with appropriate accuracy, given the level of environmental perturbation, unbiased estimates can be obtained from relatively short censuses. These conclusions are optimistic for estimation of theta. (C) 2008 Elsevier B.V All rights reserved.

Application of logistic regression for fault analysis in an industrial printing process

A statistical technique for fault analysis in industrial printing is reported. The method specifically deals with binary data, for which the results of the production process fall into two categories, rejected or accepted. The method is referred to as logistic regression, and is capable of predicting future fault occurrences by the analysis of current measurements from machine parts sensors. Individual analysis of each type of fault can determine which parts of the plant have a significant influence on the occurrence of such faults; it is also possible to infer which measurable process parameters have no significant influence on the generation of these faults. Information derived from the analysis can be helpful in the operator's interpretation of the current state of the plant. Appropriate actions may then be taken to prevent potential faults from occurring. The algorithm is being implemented as part of an applied self-learning expert system.

Epidemic K-Means clustering

The K-Means algorithm for cluster analysis is one of the most influential and popular data mining methods. Its straightforward parallel formulation is well suited for distributed memory systems with reliable interconnection networks. However, in large-scale geographically distributed systems the straightforward parallel algorithm can be rendered useless by a single communication failure or high latency in communication paths. This work proposes a fully decentralised algorithm (Epidemic K-Means) which does not require global communication and is intrinsically fault tolerant. The proposed distributed K-Means algorithm provides a clustering solution which can approximate the solution of an ideal centralised algorithm over the aggregated data as closely as desired. A comparative performance analysis is carried out against the state of the art distributed K-Means algorithms based on sampling methods. The experimental analysis confirms that the proposed algorithm is a practical and accurate distributed K-Means implementation for networked systems of very large and extreme scale.

Complete sequence and molecular epidemiology of IncK epidemic plasmid encoding bla(CTX-M-14)

Antimicrobial drug resistance is a global challenge for the 21st century with the emergence of resistant bacterial strains worldwide. Transferable resistance to beta-lactam antimicrobial drugs, mediated by production of extended-spectrum beta-lactamases (ESBLs), is of particular concern. In 2004, an ESBL-carrying IncK plasmid (pCT) was isolated from cattle in the United Kingdom. The sequence was a 93,629-bp plasmid encoding a single antimicrobial drug resistance gene, bla(CTX-M-14). From this information, PCRs identifying novel features of pCT were designed and applied to isolates from several countries, showing that the plasmid has disseminated worldwide in bacteria from humans and animals. Complete DNA sequences can be used as a platform to develop rapid epidemiologic tools to identify and trace the spread of plasmids in clinically relevant pathogens, thus facilitating a better understanding of their distribution and ability to transfer between bacteria of humans and animals.

What makes a Salmonella strain epidemic? An expert opinion workshop

An expert opinion workshop was held on the subject of the cause, identification and control of new and emerging Salmonella strains. Experts were invited to complete questionnaires, contribute to structured discussions and take part in cluster group tasks. Outputs of the workshop included that, with current surveillance methods, it might take up to 2.5 years from the first introduction of a new strain into the UK livestock population to its identification as a human epidemic strain. In order to reduce the time to detection and provide more effective control options, several recommendations were made, including better back-tracing of human cases to their source, which would require more effective communication between those responsible for human and veterinary surveillance.

Epidemic multidrug-resistant (MDR-AmpC) Salmonella enterica serovar Newport strains contain three phage regions and a MDR resistance plasmid

Multidrug-resistant (MDR-AmpC) Salmonella enterica serovar Newport has caused serious disease in animals and humans in North America, whereas in the UK S. enterica serovar Newport is not associated with severe disease and usually sensitive to antibiotics; MDR S. Newport (not AmpC) strains have only been isolated from poultry. We found that UK poultry strains belonged to MLST type ST166 and were distinct from cattle isolates for being able to utilize D-tagotose and when compared by pulsed-field gel electrophoresis (PFGE), comparative genomic hybridization (CGH) and diversity arrays technology (DArT). Cattle strains belonged to the ST45 complex differing from ST166 at all seven loci. PFGE showed that 19 out of 27 cattle isolates were more than 85% similar to each other and some UK and US strains were indistinguishable. Both CGH and DArT identified genes (including phage-related ones) that were uniquely present in the US isolates and two such genes identified by DArT showed sequence similarities with the pertussis-like (artAB) toxin. This work demonstrates that MDR-AmpC S. Newport from the USA are genetically closely related to pan-susceptible strains from the UK, but contained three extra phage regions and a MDR plasmid.

Regularized logistic models for probabilistic forecasting and diagnostics

Logistic models are studied as a tool to convert dynamical forecast information (deterministic and ensemble) into probability forecasts. A logistic model is obtained by setting the logarithmic odds ratio equal to a linear combination of the inputs. As with any statistical model, logistic models will suffer from overfitting if the number of inputs is comparable to the number of forecast instances. Computational approaches to avoid overfitting by regularization are discussed, and efficient techniques for model assessment and selection are presented. A logit version of the lasso (originally a linear regression technique), is discussed. In lasso models, less important inputs are identified and the corresponding coefficient is set to zero, providing an efficient and automatic model reduction procedure. For the same reason, lasso models are particularly appealing for diagnostic purposes.