Space–time interaction as an indicator of local spread during the 2001 FMD outbreak in the UK

• Published in: Preventive veterinary medicine 2007-04, Vol.79 (1), p.3-19
• Main Authors: Picado, A., Guitian, F.J., Pfeiffer, D.U.
• Format: Article
• Language: English
• Subjects: Animals; Cattle; decision making; decision support systems; Deer; disease control; disease incidence; disease models; disease outbreaks; Disease Outbreaks - veterinary; disease prevalence; disease surveillance; Disease transmission; Foot-and-mouth disease; Foot-and-Mouth Disease - epidemiology; Foot-and-Mouth Disease - prevention & control; Foot-and-Mouth Disease - transmission; geographical distribution; geographical variation; Goats; livestock; prediction; Predictive Value of Tests; risk assessment; Sheep; space and time; Space-Time Clustering; Space–time K-function; spatial variation; Spatiotemporal epidemiology; Swine; temporal variation; United Kingdom
• ISSN: 0167-5877; 1873-1716
• DOI: 10.1016/j.prevetmed.2006.11.009

During the 2001 FMD outbreak in the UK, decisions on the level of implementation of control measures were supported by predictive models. Models were mainly used as macro-level tools to predict the behaviour of the disease in the whole country rather than at the local level. Here we explore the use of the magnitude and characteristics of the space–time interaction as an indicator of local spread and, indirectly, of the effectiveness of control measures aimed at reducing short-range transmission during the course of a major livestock disease epidemic. The spatiotemporal evolution patterns are described in the four main clusters that were observed during the outbreak by means of the hazard rate and space–time K-function ( K( s, t)). For each local outbreak, the relative measure D 0( s, t), derived from K( s, t), which represents the excess risk attributable to the space–time interaction was calculated for consecutive 20-day temporal windows to represent the dynamics of the space–time interaction. The dynamics of the spatiotemporal interaction were very different among the four local clusters, suggesting that the intensity of local spread, and therefore the effectiveness of control measures, markedly differed between local outbreaks. The large heterogeneity observed in the relative impact of being close in time and space to an infected premises suggests that the decision making in relation to control of the outbreak would have benefited from indicators of local spread which could be used to complement global predictive modelling results. Despite its limitations, our results suggest that the real-time analysis of the space–time interaction can be a valuable decision support tool during the course of a livestock disease epidemic.