TY - GEN
T1 - Early outbreak detection using an automated data feed of test orders from a veterinary diagnostic laboratory
AU - Shaffer, Loren
AU - Funk, Julie
AU - Rajala-Schultz, Päivi
AU - Wallstrom, Garrick
AU - Wittum, Thomas
AU - Wagner, Michael
AU - Saville, William
PY - 2007
Y1 - 2007
N2 - Disease surveillance in animals remains inadequate to detect outbreaks resulting from novel pathogens and potential bioweapons. Mostly relying on confirmed diagnoses, another shortcoming of these systems is their ability to detect outbreaks in a timely manner. We investigated the feasibility of using veterinary laboratory test orders in a prospective system to detect outbreaks of disease earlier compared to traditional reporting methods. IDEXX Laboratories, Inc. automatically transferred daily records of laboratory test orders submitted from veterinary providers in Ohio via a secure file transfer protocol. Test products were classified to appropriate syndromic category using their unique identifying number. Counts of each category by county were analyzed to identify unexpected increases using a cumulative sums method. The results indicated that disease events can be detected through the prospective analysis of laboratory test orders and may provide indications of similar disease events in humans before traditional disease reporting.
AB - Disease surveillance in animals remains inadequate to detect outbreaks resulting from novel pathogens and potential bioweapons. Mostly relying on confirmed diagnoses, another shortcoming of these systems is their ability to detect outbreaks in a timely manner. We investigated the feasibility of using veterinary laboratory test orders in a prospective system to detect outbreaks of disease earlier compared to traditional reporting methods. IDEXX Laboratories, Inc. automatically transferred daily records of laboratory test orders submitted from veterinary providers in Ohio via a secure file transfer protocol. Test products were classified to appropriate syndromic category using their unique identifying number. Counts of each category by county were analyzed to identify unexpected increases using a cumulative sums method. The results indicated that disease events can be detected through the prospective analysis of laboratory test orders and may provide indications of similar disease events in humans before traditional disease reporting.
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U2 - 10.1007/978-3-540-72608-1_1
DO - 10.1007/978-3-540-72608-1_1
M3 - Conference contribution
AN - SCOPUS:37249070808
SN - 9783540726074
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1
EP - 10
BT - Intelligence and Security Informatics
PB - Springer-Verlag
T2 - 2nd NSF BioSurveillance Workshop, BioSurveillance 2007
Y2 - 22 May 2007 through 22 May 2007
ER -