Using geographically weighted regression to explore the spatially heterogeneous spread of bovine tuberculosis in England and Wales

An understanding of the factors that affect the spread of endemic bovine tuberculosis (bTB) is critical for the development of measures to stop and reverse this spread. Analyses of spatial data need to account for the inherent spatial heterogeneity within the data, or else spatial autocorrelation can lead to an overestimate of the significance of variables. This study used three methods of analysis—least-squares linear regression with a spatial autocorrelation term, geographically weighted regression (GWR) and boosted regression tree (BRT) analysis—to identify the factors that influence the spread of endemic bTB at a local level in England and Wales. The linear regression and GWR methods demonstrated the importance of accounting for spatial differences in risk factors for bTB, and showed some consistency in the identification of certain factors related to flooding, disease history and the presence of multiple genotypes of bTB. This is the first attempt to explore the factors associated with the spread of endemic bTB in England and Wales using GWR. This technique improves on least-squares linear regression approaches by identifying regional differences in the factors associated with bTB spread. However, interpretation of these complex regional differences is difficult and the approach does not lend itself to predictive models which are likely to be of more value to policy makers. Methods such as BRT may be more suited to such a task. Here we have demonstrated that GWR and BRT can produce comparable outputs

Local Cattle and Badger Populations Affect the Risk of Confirmed Tuberculosis in British Cattle Herds

Background: The control of bovine tuberculosis (bTB) remains a priority on the public health agenda in Great Britain, after launching in 1998 the Randomised Badger Culling Trial (RBCT) to evaluate the effectiveness of badger (Meles meles) culling as a control strategy. Our study complements previous analyses of the RBCT data (focusing on treatment effects) by presenting analyses of herd-level risks factors associated with the probability of a confirmed bTB breakdown in herds within each treatment: repeated widespread proactive culling, localized reactive culling and no culling (survey-only). Methodology/Principal Findings: New cases of bTB breakdowns were monitored inside the RBCT areas from the end of the first proactive badger cull to one year after the last proactive cull. The risk of a herd bTB breakdown was modeled using logistic regression and proportional hazard models adjusting for local farm-level risk factors. Inside survey-only and reactive areas, increased numbers of active badger setts and cattle herds within 1500 m of a farm were associated with an increased bTB risk. Inside proactive areas, the number of M. bovis positive badgers initially culled within 1500 m of a farm was the strongest predictor of the risk of a confirmed bTB breakdown. Conclusions/Significance: The use of herd-based models provide insights into how local cattle and badger populations affect the bTB breakdown risks of individual cattle herds in the absence of and in the presence of badger culling. These measures of local bTB risks could be integrated into a risk-based herd testing programme to improve the targeting o

Not all cows are epidemiologically equal:quantifying the risks of bovine viral diarrhoea virus (BVDV) transmission through cattle movements

International audienceMany economically important cattle diseases spread between herds through livestock movements. Traditionally, most transmission models have assumed that all purchased cattle carry the same risk of generating outbreaks in the destination herd. Using data on bovine viral diarrhoea virus (BVDV) in Scotland as a case example, this study provides empirical and theoretical evidence that the risk of disease transmission varies substantially based on the animal and herd demographic characteristics at the time of purchase. Multivariable logistic regression analysis revealed that purchasing pregnant heifers and open cows sold with a calf at foot were associated with an increased risk of beef herds being seropositive for BVDV. Based on the results from a dynamic within-herd simulation model, these findings may be partly explained by the age-related probability of animals being persistently infected with BVDV as well as the herd demographic structure at the time of animal introductions. There was also evidence that an epidemiologically important network statistic, "betweenness centrality" (a measure frequently associated with the potential for herds to acquire and transmit disease), was significantly higher for herds that supplied these particular types of replacement beef cattle. The trends for dairy herds were not as clear, although there was some evidence that open heifers and open lactating cows were associated with an increased risk of BVDV. Overall, these findings have important implications for developing simulation models that more accurately reflect the industry-level transmission dynamics of infectious cattle diseases