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Statistical challenges in biosurveillance. (English) Zbl 1345.92007
Chowell, Gerardo (ed.) et al., Mathematical and statistical estimation approaches in epidemiology. Dordrecht: Springer (ISBN 978-90-481-2312-4/hbk; 978-90-481-2313-1/ebook). 163-187 (2009).
Summary: One goal in biosurveillance is to detect patterns in disease rates, such as temporal and/or geographic clustering. Traditionally, disease rates are available by geographic unit over weekly, monthly, or yearly time bins, and covariates such as age, gender, and socio-economic status can be used to adjust predicted rates prior to testing for clustering. Recently, more timely pre-diagnostic data including emergency department visits have been used in “syndromic surveillance” in order to more rapidly detect either natural or bioterrorist-related outbreaks. Typically, such data are categorized by chief complaint into one of several syndromes such as gastro-intestinal or respiratory.
This chapter describes outbreak detection using either traditional diagnosed case rates or syndromic surveillance data. Outbreak detection involves many issues; our focus is the associated statistical challenges, including: (1) approaches to characterizing the natural background; (2) algorithms for detecting abnormal increases above background disease rates, (3) methods for adjusting for covariates such as gender, age, etc.; (4) detecting spatial-temporal clusters, and (5) methods for protecting data confidentiality.
For the entire collection see [Zbl 1166.92002].
MSC:
92B15 General biostatistics
62P10 Applications of statistics to biology and medical sciences; meta analysis
62H30 Classification and discrimination; cluster analysis (statistical aspects)
Software:
BayesDA; R
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