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A case-association cluster detection and visualisation tool with an application to Legionnaires' disease
P Sansom1, V R Copley, F C Naik
1Microbial Risk Assessment, Emergency Response Department, Health Protection Agency, Porton Down, Salisbury, Wiltshire, SP4 0JG, U.K.
Statistics in Medicine
|March 14, 2013
Summary
This study introduces a new model to measure the association between disease cases in space and time. This tool helps identify disease source links and potential outbreaks during investigations.
Area of Science:
- Epidemiology
- Biostatistics
- Public Health
Background:
- Current spatio-temporal disease surveillance methods identify case clusters but lack measures of case association.
- Quantifying the association between cases is crucial for grouping them and investigating shared disease sources.
- Existing methods do not fully support the assignment of cases to common etiologic groups.
Purpose of the Study:
- To present a model-based approach for measuring the strength of association between disease cases in space and time.
- To develop a tool for designating and visualizing likely groupings of disease cases.
- To enhance prospective disease surveillance and outbreak investigation capabilities.
Main Methods:
- A novel model-based statistical approach utilizing available location data.
- Development of a quantitative measure for case-to-case association in spatio-temporal dimensions.
- Application of the method to a historical case series of Legionnaires' disease in England and Wales.
Main Results:
- The developed method provides a quantitative measure of case association, aiding in group designation.
- The approach effectively visualizes potential groupings of disease cases based on spatio-temporal proximity and association.
- Demonstrated utility in analyzing historical disease data for outbreak investigation insights.
Conclusions:
- The model-based approach offers a valuable measure of case association, improving spatio-temporal disease surveillance.
- This method facilitates the identification of shared sources and enhances outbreak investigation efficiency.
- The tool can be applied prospectively for early outbreak detection and retrospectively for investigation.
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