Mapping bovine tuberculosis in Great Britain using environmental data G.R. William Wint, Timothy P. Robinson, David M. Bourn, Peter A. Durr, Simon I. Hay, Sarah E. Randolph and David J. Rogers Trends in Microbiology 10.1016/S0966-842X(02)02444-7 The incidence of bovine tuberculosis (BTB) is increasing in Great Britain, exacerbated by the temporary suspension of herd testing in 2001 for fear of spreading the much more contagious foot and mouth disease. The transmission pathways of BTB remain poorly understood. Current hypotheses suggest the disease is introduced into susceptible herds from a wildlife reservoir (principally the Eurasian Badger) and/or from cattle purchased from infected areas, while the role of climatic factors in transmission has generally been ignored. Here, we show how remotely sensed satellite data, which provide good indicators of a variety of climatic factors, can be used to describe the distribution of BTB in Great Britain in 1997, and suggest how such data could be used to produce BTB risk maps for the future.   Bovine tuberculosis (BTB), which is caused by Mycobacterium bovis, was once widespread in Great Britain, but is now focused in south-west England, south-west Wales and parts of the Midlands ( Fig. 1). Scattered cases occur throughout the mainland and outbreaks have been reported recently in mid-Wales. The distribution of BTB is routinely monitored by the Department for Environment, Food and Rural Affairs (DEFRA), although a coherent management strategy has yet to be framed within a descriptive model of disease transmission. The development of such models is hampered by the fact that our knowledge of the M. bovis transmission pathways is incomplete. The biology of the host undoubtedly plays a major role in transmission and although the effect of climate on the natural history of the pathogen in the field is largely unknown, it is likely to have a significant influence on the disease [1]. Potential correlations between climatic factors and the occurrence of BTB, which so far have not been investigated extensively, can be obtained at a fairly fine spatial resolution ( Box 1) from satellite observations [2,3] . Satellite data have already been used to describe the distribution and abundance of several diseases in many countries worldwide, including malaria [4,5] , schistosomiasis [6], trypanosomiasis [7–9] , tick-borne diseases [10], West Nile Virus in the USA [11], the vectors of African horse sickness in South Africa [12] and blue tongue in the Mediterranean basin [13]. Given these successes with vector-borne or indirectly transmitted diseases, we assessed the use of the same approach to describe the distribution of BTB in Great Britain, as a potential complement to existing monitoring procedures. Data, images and image processing BTB data were derived from the VETNET database for the period 1988–1997. These are the geo-referenced BTB monitoring data for the whole of mainland Great Britain, covering >80 000 holdings annually, and thus provide a reliable indication of BTB distribution. Analyses were restricted to the presence or absence of the disease within a herd as it proved impossible to estimate incidence or prevalence reliably from the database. Only data for 1997 were used, giving approximately 500 infected sites. Disease data are often spatially clustered, which reduces the statistical significance of distribution models. A subset of the data from the southern Midlands was therefore examined for such spatial autocorrelation, which appeared to be minimal beyond distances of about 2 or 3 km. This suggested that autocorrelation in the BTB data would be reduced by amalgamating the records into spatial units of >3 km, so the data indicating the presence or absence of BTB were aggregated into 5 km grid squares before analysis. A broad range of anthropogenic, biological, demographic, climatic and topographic variables was assessed as predictors ( Box 1). Data extraction and model construction All predictor data were converted to 0.01 degree resolution and stored in IDRISI (geographical analysis software; http://www.idrisi.clarku.edu) raster images in latitude/longitude format. From each image, data values were extracted for a series of data points corresponding to BTB-positive and BTB-negative locations for 1997. After filtering to remove any records with incomplete data, and then adjusting absence sample sizes to give approximately equal numbers of observations of positive and negative sites, the data were subjected to step-wise forward logistic regression analysis using the Statistical Package for the Social Sciences (SPSS; http://www.spss.com) to establish the relationship between the predictor variables and the presence or absence of disease. Although this method partially compensates for correlations between predictor variables, possible co-linearity means that the precise order in which variables are included in the model should be treated with some caution. The output of logistic regression models, as widely used in distribution studies [14], is a prediction of the probability of presence for each sample site. The threshold probability that most accurately distinguishes presence from absence in logistic regression tends to vary with the relative numbers of presence and absence observations used; with equal sample sizes, a threshold of 0.5 is likely to provide a reasonable balance between minimising the prediction of false negatives and false positives, and is thus appropriate for an exploratory model such as this. The accuracy of the various logistic regression models was assessed using the Kappa index of agreement [15], which ranges from 0 (no predictive skill) to 1 (perfect prediction), with values >0.4 regarded as acceptable and >0.75 as excellent [16]. Once the best models had been determined, they were applied to the full 1 km resolution imagery to produce output maps predicting the probability of BTB presence throughout Great Britain.   Go to the Full Article > >  As a BioMedNet member you have free full-text access to this article within BioMedNet Reviews for 14 days  You will have full-text access to all articles if your institute subscribes. Recommend a subscription to your librarian  There are 15,000 more Review articles in BioMedNet Reviews  Please send us your feedback on this magazine		BioMedNet Magazine 11th - 24th September 2002 Magazine Home Email to a Friend Full Article PDF printer ready version   Further Reading* From Predicting Mosquito Habitat to Malaria Seasons Using Remotely Sensed Data: Practice, Problems and Perspectives [Review] S.I. Hay, R.W. Snow and D.J. Rogers Parasitology Today 1998,14:306-313   Spatial trypanosomosis management: from data-layers to decision making [Review] Guy Hendrickx, Stéphane de La Rocque, Robin Reid and Willy Wint Trends in Parasitology 2001, 17:35-41   Disease model: pulmonary tuberculosis [Disease Model] David N. McMurray Trends in Molecular Medicine 2001, 7:135-137   Risk Maps: Transmission and Burden of Vector-borne Diseases [Comment] U. Kitron Parasitology Today2000, 16:324-325     * Full text access to the journal articles above is available to BioMedNet Reviews institutional subscribers     Related Links in BioMedNet Reviews Journal Table of Contents Table of Contents by email Related Full Text Articles Browse Microbiology Browse Techniques & Methods Search BioMedNet Reviews