Spatial analysis of Mycobacterium bovis infection in white-tailed deer (Odocoileus virginianus) in Michigan, USA

Linking bovine tuberculosis on cattle farms to white-tailed deer and environmental variables using Bayesian hierarchical analysis

Bovine tuberculosis is a bacterial disease caused by Mycobacterium bovis in livestock and wildlife with hosts that include Eurasian badgers (Meles meles), brushtail possum (Trichosurus vulpecula), and white-tailed deer (Odocoileus virginianus). Risk-assessment efforts in Michigan have been initiated on farms to minimize interactions of cattle with wildlife hosts but research on M. bovis on cattle farms has not investigated the spatial context of disease epidemiology. To incorporate spatially explicit data, initial likelihood of infection probabilities for cattle farms tested for M. bovis, prevalence of M. bovis in white-tailed deer, deer density, and environmental variables for each farm were modeled in a Bayesian hierarchical framework. We used geo-referenced locations of 762 cattle farms that have been tested for M. bovis, white-tailed deer prevalence, and several environmental variables that may lead to long-term survival and viability of M. bovis on farms and surrounding habitats (i.e., soil type, habitat type). Bayesian hierarchical analyses identified deer prevalence and proportion of sandy soil within our sampling grid as the most supported model. Analysis of cattle farms tested for M. bovis identified that for every 1% increase in sandy soil resulted in an increase in odds of infection by 4%. Our analysis revealed that the influence of prevalence of M. bovis in white-tailed deer was still a concern even after considerable efforts to prevent cattle interactions with white-tailed deer through on-farm mitigation and reduction in the deer population. Cattle farms test positive for M. bovis annually in our study area suggesting that the potential for an environmental source either on farms or in the surrounding landscape may contributing to new or re-infections with M. bovis. Our research provides an initial assessment of potential environmental factors that could be incorporated into additional modeling efforts as more knowledge of deer herd factors and cattle farm prevalence is documented.

Opportunities for improved serodiagnosis of human tuberculosis, bovine tuberculosis, and paratuberculosis

Mycobacterial infections—tuberculosis (TB), bovine tuberculosis (bTB), and Johne's disease (JD)—are major infectious diseases of both human and animals. Methods presently in use for diagnosis of mycobacterial infections include bacterial culture, nucleic acid amplification, tuberculin skin test, interferon-γ assay, and serology. Serological tests have several advantages over other methods, including short turn-around time, relatively simple procedures, and low cost. However, current serodiagnostic methods for TB, bTB and JD exhibit low sensitivity and/or specificity. Recent studies that have aimed to develop improved serodiagnostic tests have mostly focused on identifying useful species-specific protein antigens. A review of recent attempts to improve diagnostic test performance indicates that the use of multiple antigens can improve the accuracy of serodiagnosis of these mycobacterial diseases. Mycobacteria also produce a variety of species-specific nonprotein molecules; however, only a few such molecules (e.g., cord factor and lipoarabinomannan) have so far been evaluated for their effectiveness as diagnostic antigens. For TB and bTB, there has been recent progress in developing laboratory-free diagnostic methods. New technologies such as microfluidics and “Lab-on-Chip” are examples of promising new technologies that can underpin development of laboratory-free diagnostic devices for these mycobacterial infections.

Spatial-temporal model of haemorrhagic disease in white-tailed deer in south-east USA, 1983 to 2000

The present study constructed a spatial-temporal statistical model to identify the risk and protective factors for haemorrhagic disease (HD) in white-tailed deer in the five states of Alabama, Georgia, South Carolina, North Carolina and Tennessee. The response variable was binary, indicating the presence or absence of HD in an individual county, measured annually from 1983 to 2000. Predictor variables included climatic factors of temperature, rainfall, wind speed and dew point, remotely sensed data of normalised difference vegetation index (NDVI) and land surface temperature derived from archived remotely sensed advanced very-high-resolution radiometer (AVHRR) satellite data, elevation, a spatial autocorrelation (SA) term and a temporal autocorrelation term. This study first applied principal component factor analysis to reduce the volume of climatic data and remotely sensed data. Then, a generalised linear mixed model framework (GLMM) was used to develop a spatial-temporal statistical model. The results showed that the area under receiver operating characteristic curve (ROC) was 0.728, indicating a good overall fit of the model. The total prediction accuracy over the 18 year period with optimal cut-off probability was 67 per cent. The prediction accuracy for individual years ranged from 48 to 75 per cent.

Characterization of the risk of deer-cattle interactions in Minnesota by use of an on-farm environmental assessment tool

OBJECTIVE

To characterize the risk of interactions that may lead to the transmission of Mycobacterium bovis between cattle and white-tailed deer (Odocoileus virginianus) on farms in northern Minnesota.

SAMPLE

53 cattle farms in northwestern Minnesota adjacent to an area where bovine tuberculosis-infected cattle and deer were detected.

PROCEDURES

A semiquantitative deer-cattle interaction assessment tool was used for the 53 cattle herds. Farm risk scores were analyzed on the basis of deer damage to stored feed.

RESULTS

27 (51%) farms reported deer damage to stored cattle feeds within the year previous to the farm visit. A strong association was found between increases in the percentage of land that could serve as deer cover and deer damage to stored feeds on a farm. The total risk score was significantly associated with the probability of a farm having deer damage. By use of a logistic regression model, the total risk score and proportion of nonagricultural land around a farm could be used to predict the likelihood of deer damage to stored feeds.

CONCLUSIONS AND CLINICAL RELEVANCE

Management practices on many farms in northwestern Minnesota allowed potential deer-cattle interactions. The on-farm risk assessment tool served as a valuable tool for prioritizing the biosecurity risks for farms. Continued development of biosecurity is needed to prevent potential transmission of bovine tuberculosis between deer and cattle, especially on farms that have a higher risk of deer damage.