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A case-association cluster detection and visualisation tool with an application to Legionnaires' disease.

Sansom P, Copley VR, Naik FC, Leach S, Hall IM - Stat Med (2013)

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.

View Article: PubMed Central - PubMed

Affiliation: Microbial Risk Assessment, Emergency Response Department, Health Protection Agency, Porton Down, Salisbury, Wiltshire, SP4 0JG, U.K.

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Single groupings of more than 11 cases identified by model at 0.15 distance level from complete data series; excludes Barrow-in-Furness outbreak.
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fig03: Single groupings of more than 11 cases identified by model at 0.15 distance level from complete data series; excludes Barrow-in-Furness outbreak.

Mentions: When it is run on the complete data series, our method identifies seven groups at the 0.15 distance level, which consist of more than eleven cases. One of these relates to the Barrow outbreak 25. We show the remaining six groups as dendrograms in Figure 3. Each leaf in a dendrogram corresponds to a case and is numbered with the modelled group to which it belongs. We use a leaf label of zero in Figure 3 to identify a sporadic community-acquired case, whereas non-zero labels indicate that the case belongs to an empirically defined outbreak.


A case-association cluster detection and visualisation tool with an application to Legionnaires' disease.

Sansom P, Copley VR, Naik FC, Leach S, Hall IM - Stat Med (2013)

Single groupings of more than 11 cases identified by model at 0.15 distance level from complete data series; excludes Barrow-in-Furness outbreak.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3842591&req=5

fig03: Single groupings of more than 11 cases identified by model at 0.15 distance level from complete data series; excludes Barrow-in-Furness outbreak.
Mentions: When it is run on the complete data series, our method identifies seven groups at the 0.15 distance level, which consist of more than eleven cases. One of these relates to the Barrow outbreak 25. We show the remaining six groups as dendrograms in Figure 3. Each leaf in a dendrogram corresponds to a case and is numbered with the modelled group to which it belongs. We use a leaf label of zero in Figure 3 to identify a sporadic community-acquired case, whereas non-zero labels indicate that the case belongs to an empirically defined outbreak.

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.

View Article: PubMed Central - PubMed

Affiliation: Microbial Risk Assessment, Emergency Response Department, Health Protection Agency, Porton Down, Salisbury, Wiltshire, SP4 0JG, U.K.

Show MeSH
Related in: MedlinePlus