| |  | Congdon, Peter | Mixtures of spatial and unstructured effects for spatially discontinuous health outcomes read moreAbstract: Mixture models are used for spatially adaptive smoothing of health event data (e.g. mortality or illness totals). Such models allow for spatial pooling of strength where appropriate but adopt a mixture strategy that also reflects health risks that are discordant with those of surrounding areas. Mixing is either discrete or based on beta densities. A fully Bayesian estimation and specification strategy is applied with fit based on DIC and BIC criteria. Illustrative applications are to long term illness in 133 London small areas, where event counts are large, and to lip cancer in Scottish counties where the majority of event totals are under 10. | 2007 |
| | | Viboud, C. | Synchrony, Waves, and Spatial Hierarchies in the Spread of Influenza read moreAbstract: Sorry no abstract available for this article | 2006 |
| | | Savill, N. J. | Topographic determinants of foot and mouth disease transmission in the UK 2001 epidemic. read moreAbstract: BACKGROUND: A key challenge for modelling infectious disease dynamics is to understand the spatial spread of infection in real landscapes. This ideally requires a parallel record of spatial epidemic spread and a detailed map of susceptible host density along with relevant transport links and geographical features. RESULTS: Here we analyse the most detailed such data to date arising from the UK 2001 foot and mouth epidemic. We show that Euclidean distance between infectious and susceptible premises is a better predictor of transmission risk than shortest and quickest routes via road, except where major geographical features intervene. CONCLUSION: Thus, a simple spatial transmission kernel based on Euclidean distance suffices in most regions, probably reflecting the multiplicity of transmission routes during the epidemic. | 2006 |
| | | Wong, Weng-Keen | Whats Strange About Recent Events (WSARE): An Algorithm for the Early Detection of Disease Outbreaks read moreAbstract: Traditional biosurveillance algorithms detect disease outbreaks by looking for peaks in a univariate time series of health-care data. Current health-care surveillance data, however, are no longer simply univariate data streams. Instead, a wealth of spatial, temporal, demographic and symptomatic information is available. We present an early disease outbreak detection algorithm called Whats Strange About Recent Events (WSARE), which uses a multivariate approach to improve its timeliness of detection. WSARE employs a rule-based technique that compares recent health-care data against data from a baseline distribution and finds subgroups of the recent data whose proportions have changed the most from the baseline data. In addition, health-care data also pose difficulties for surveillance algorithms because of inherent temporal trends such as seasonal effects and day of week variations. WSARE approaches this problem using a Bayesian network to produce a baseline distribution that accounts for these temporal trends. The algorithm itself incorporates a wide range of ideas, including association rules, Bayesian networks, hypothesis testing and permutation tests to produce a detection algorithm that is careful to evaluate the significance of the alarms that it raises. | 2005 |
| | | Woolhouse, M. | Epidemiology: Foot-and-mouth disease under control in the UK read moreAbstract: Following the first reported case on 20 February this year, foot-and-mouth disease spread to over 1,500 livestock farms in the United Kingdom by the end of April. From late March, the Ministry of Agriculture, Fisheries and Food (MAFF) required livestock on infected farms to be culled within 24 hours of the disease being reported and those on neighbouring farms within 48 hours. Here we investigate whether progress towards meeting these targets has had a detectable impact on the course of the epidemic in the United Kingdom. We conclude that it has now been brought under control, but it will be important to contain rapidly any new outbreaks in previously unaffected areas. | 2001 |
| | | Bernardinelli, L. | Bayesian Analysis of space-time variation in disease risk read moreAbstract: Sorry no abstract available for this article | 1995 |
| | | Besag, Julian | The detection of clusters in rare diseases read moreAbstract: Sorry no abstract available for this article | 1991 |
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