Prefiltered component‐based greedy (PreCoG) scan method

The spatial distribution of disease cases can provide important insights into disease spread and its potential risk factors. Identifying disease clusters correctly can help us discover new risk factors and inform interventions to control and prevent the spread of disease as quickly as possible. In t...

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Veröffentlicht in:Statistics in medicine Jg. 43; H. 21; S. 4113 - 4130
Hauptverfasser: French, Joshua P., Meysami, Mohammad, Lipner, Ettie M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken, USA John Wiley & Sons, Inc 20.09.2024
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Schlagworte:
Algorithms
candidate zones
Cluster Analysis
Computer Simulation
disease cluster identification
disease clusters
Epidemiology
Health surveillance
Humans
Models, Statistical
public health
Risk Factors
Sensitivity analysis
Spatial analysis
spatial scan method
Statistical methods
disease clusters
disease cluster identification
spatial scan method
candidate zones
public health
ISSN:0277-6715, 1097-0258, 1097-0258
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Zusammenfassung:The spatial distribution of disease cases can provide important insights into disease spread and its potential risk factors. Identifying disease clusters correctly can help us discover new risk factors and inform interventions to control and prevent the spread of disease as quickly as possible. In this study, we propose a novel scan method, the Prefiltered Component‐based Greedy (PreCoG) scan method, which efficiently and accurately detects irregularly shaped clusters using a prefiltered component‐based algorithm. The PreCoG scan method's flexibility allows it to perform well in detecting both regularly and irregularly‐shaped clusters. Additionally, it is fast to apply while providing high power, sensitivity, and positive predictive value for the detected clusters compared to other scan methods. To confirm the effectiveness of the PreCoG method, we compare its performance to many other scan methods. Additionally, we have implemented this method in the smerc R package to make it publicly available to other researchers. Our proposed PreCoG scan method presents a unique and innovative process for detecting disease clusters and can improve the accuracy of disease surveillance systems.
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ISSN:0277-6715
1097-0258
1097-0258
DOI:10.1002/sim.10170