Applying Aberration Detection Algorithms to Live Public Health Data: Lessons from National Syphilis Case Surveillance Data

Martin, Erika; Angles, John; Pondo, Tracy; Pagaoa, Melissa; Torrone, Elizabeth

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Applying Aberration Detection Algorithms to Live Public Health Data: Lessons from National Syphilis Case Surveillance Data

9/30/2024
By Martin, Erika ; Angles, John ; Pondo, Tracy ; ...

[PDF-1.02 MB]

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Personal Author:

Martin, Erika ; Angles, John ; Pondo, Tracy ; Pagaoa, Melissa ; Torrone, Elizabeth

Martin, Erika ; Angles, John ; Pondo, Tracy ; Pagaoa, Melissa ; Torrone, Elizabeth Less -
Corporate Authors:

Centers for Disease Control and Prevention (U.S.). National Center for HIV, STD, and TB Prevention (U.S.)
Description:

This report provides public health programs with strategies for implementing data aberration detection algorithms to identify meaningful changes in syphilis case surveillance data that indicate potential outbreaks or actionable data quality issues.

White paper supported by the Centers for Disease Control and Prevention, National Center for HIV, Viral Hepatitis, STD, and TB Prevention Epidemiological and Economic Modeling Agreement (no. 5U38PS004650).
Document Type:

Report
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21 numbered pages
Collection(s):

Stephen B. Thacker CDC Library collection
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urn:sha-512:df54fb9fab548db01233abcf5165632ffe67feab02b14624a43b3a0bebb38684db38af7539a2a74b5a7491e0584b6804791a201552c9e9518aeb9b4abac35960
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https://stacks.cdc.gov/view/cdc/164155/cdc_164155_DS1.pdf
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Applying Aberration Detection Algorithms to Live Public Health Data: Lessons from National Syphilis Case Surveillance Data

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