A case-association cluster detection and visualisation tool with an application to Legionnaires' disease.
Bottom Line: Statistical methods used in spatio-temporal surveillance of disease are able to identify abnormal clusters of cases but typically do not provide a measure of the degree of association between one case and another.This paper presents a model-based approach, which on the basis of available location data, provides a measure of the strength of association between cases in space and time and which is used to designate and visualise the most likely groupings of cases.The method was developed as a prospective surveillance tool to signal potential outbreaks, but it may also be used to explore groupings of cases in outbreak investigations.
Affiliation: Microbial Risk Assessment, Emergency Response Department, Health Protection Agency, Porton Down, Salisbury, Wiltshire, SP4 0JG, U.K.Show MeSH
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Mentions: Figure 4 plots the frequency with which outbreaks of different sizes arise in the case series and in the modelled groupings at the 0.15 distance level. There are 40 outbreaks involving only two cases in the case series but 386 such outbreaks in the modelled groupings. Overall, the model appears to be more likely to associate cases into common groupings than empirical procedure. Thus, the model finds 144 groupings of three cases, in comparison with the 18 that are given by the empirical data. The model also records higher numbers of groups of four, five, six and seven cases. It finds fewer groups of eight cases, and two more large outbreaks ( > 10 cases), compared with the empirical series.
Affiliation: Microbial Risk Assessment, Emergency Response Department, Health Protection Agency, Porton Down, Salisbury, Wiltshire, SP4 0JG, U.K.