The spatio-temporal epidemiology of Bovine spongiform encephalopathy and Foot-and-mouth disease in Great Britain : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy at Massey University
Great Britain suffered two of the most globally notable animal disease epidemics of recent decades — the bovine spongiform encephalopathy (BSE) epidemic which began in November 1986, and the foot-and-mouth disease (FMD) epidemic which lasted from February to September 2001. This thesis applies various analytical techniques to these two quite different epidemics: a rapidly spreading highly contagious disease for which urgent decisions are essential (FMD), and a feed-borne non-contagious disease with an exceptionally long incubation period (BSE). The BSE epidemic, in particular, presented major investigational challenges because its recent emergence meant that its epidemiological features were not yet fully clear. The studies of BSE reported here showed that the control measures recommended as a consequence of the first epidemiological study of the new disease were remarkably effective. The July 1988 meat and bone meal ban resulted in a 60% reduction in BSE risk for cattle born in the first 12 months after its introduction. Descriptive spatial analyses, using kernel density and regression techniques showed a marked concentration of BSE risk in the south of Great Britain. Following the July 1988 meat and bone meal ban BSE risk shifted to the east of the country, an effect partly explained by cross contamination of cattle feed with high-protein concentrates destined for the pig and poultry industry. Detailed investigation of the earliest BSE-exposed farm holdings identified the south of England as an area of excess exposure density. While interpretation of these findings is complicated by the fact that the disease must have been present for some years before it was first diagnosed, the evidence suggests initial amplification in the south provided risk material which progressively distributed the disease to the rest of the country. In contrast to BSE, FMD presents different challenges, in that affected farms can be diagnosed rapidly, but it is difficult to accurately evaluate the relative importance of the different mechanisms of transmission, and hence determine how best to apply control efforts. Foci of FMD infection of matched size in the English counties of Cumbria and Devon were compared to dissect out factors contributing to the two quite different epidemic patterns in these areas. This analysis showed evidence of strong spatio-temporal interaction of infection risk in Cumbria, due initially to cattle herds as the dominant influence, with a later growth in the role of sheep as a source of infection. During the FMD epidemic a stochastic spatial simulation model was used extensively as an aid for decision making. After the epidemic was over the predictive accuracy of earlier real-time modelling was assessed for the whole of Britain and the most concentrated focus of disease in Cumbria. The model predicted the temporal epidemic curve closely at both levels, and predicted the national spatial pattern of infection with high specificities (over 99%) and useful sensitivities (37% to 71%). It was concluded that the model predicted FMD-infected locations within 0 to 14 days after simulation start date with sufficient accuracy to guide surveillance activities and to provide estimates of resources required for contingency planning. The spatial accuracy of predictions might be further improved through the use of a series of sub-regional models, better-capturing the characteristics of individual outbreak foci that typically emerge during extended large scale, multicentred epidemics. The studies presented in this thesis demonstrate that the application of temporal, spatial and spatio-temporal analytical methods can enhance the understanding of the epidermiological features of diseases in animal populations. The value in applying these methods of analysis comes from the ability to identify high and low disease-risk time frames and locations, allowing more focused allocation of investigative resources.