ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2003
An analytic framework fo space-time aberrancy detection in public health surveillance data.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2003. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Outbreak detection algorithms based on generalized linear model: a review with new practical examples.BMC medical research methodology · 2023Review
- Latitude-based approach for detecting aberrations of hand, foot, and mouth disease epidemics.BMC medical informatics and decision making · 2015Article
- Improving the performance of outbreak detection algorithms by classifying the levels of disease incidence.PloS one · 2013Article
- Understanding detection performance in public health surveillance: modeling aberrancy-detection algorithms.Journal of the American Medical Informatics Association : JAMIAArticle
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Authors and funding
4 authors.
Funding
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Abstract
Public health surveillance is changing in response to concerns about bioterrorism, which have increased the pressure for early detection of epidemics. Rapid detection necessitates following multiple non-specific indicators and accounting for spatial structure. No single analytic method can meet all of these requirements for all data sources and all surveillance goals. Analytic methods must be selected and configured to meet a surveillance goal, but there are no uniform criteria to guide the selection and configuration process. In this paper, we describe work towards the development of an analytic framework for space-time aberrancy detection in public health surveillance data. The framework decomposes surveillance analysis into sub-tasks and identifies knowledge that can facilitate selection of methods to accomplish sub-tasks.
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14728146PMC1479968What OpenQuestion holds
Registered trials
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