Statistical Challenges in BioSurveillance

Tom Burr (), Sarah Michalak () and Rick Picard ()
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Tom Burr: Statistical Sciences Group, Los Alamos National Laboratory
Sarah Michalak: Statistical Sciences Group, Los Alamos National Laboratory
Rick Picard: Statistical Sciences Group, Los Alamos National Laboratory

A chapter in Mathematical and Statistical Estimation Approaches in Epidemiology, 2009, pp 163-187 from Springer

Abstract: Abstract 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.

Keywords: Disease Rate; Reference Distribution; Statistical Challenge; Syndromic Surveillance; Outbreak Detection (search for similar items in EconPapers)
Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-90-481-2313-1_8

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DOI: 10.1007/978-90-481-2313-1_8

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