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Comparison of various statistical methods for detecting disease outbreaks

Authors
Choi, Byeong YeobKim, HoGo, Un YeongJeong, Jong-HyeonLee, Jae Won
Issue Date
12월-2010
Publisher
SPRINGER HEIDELBERG
Keywords
Outbreak; Infectious disease; Sensitivity; Specificity; Positive predictive value; Time lag; Missing rate
Citation
COMPUTATIONAL STATISTICS, v.25, no.4, pp.603 - 617
Indexed
SCIE
SCOPUS
Journal Title
COMPUTATIONAL STATISTICS
Volume
25
Number
4
Start Page
603
End Page
617
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/115299
DOI
10.1007/s00180-010-0191-7
ISSN
0943-4062
Abstract
In this article, we compared seven statistical methods for detecting outbreaks of infectious disease; Historical limits, English model, SPOTv2, CuSums, Bayesian predictive model, RKI method and Serfling model. We used simulated data and real data to compare those seven methods. Simulated data have parameters such as trend, seasonality, mean and standard deviation. Among these methods, SPOTv2 shows the best performance with a balance between sensitivity and positive predictive value and short time lag. But in datasets having strong trends, Bayesian predictive model, English model and Serfling model perform better than SPOTv2. These methods are also compared through real numerical example.
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