16926216

Source:http://linkedlifedata.com/resource/pubmed/id/16926216

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Predicate	Object
rdf:type	pubmed:Citation
lifeskim:mentions	umls-concept:C0021400, umls-concept:C0025663, umls-concept:C0150369, umls-concept:C0733511, umls-concept:C1511726
pubmed:issue	5
pubmed:dateCreated	2006-10-20
pubmed:abstractText	A variety of Serfling-type statistical algorithms requiring long series of historical data, exclusively from temperate climate zones, have been proposed for automated monitoring of influenza sentinel surveillance data. We evaluated three alternative statistical approaches where alert thresholds are based on recent data in both temperate and subtropical regions.
pubmed:language	eng
pubmed:journal	http://linkedlifedata.com/resource/pubmed/journal/7802871
pubmed:citationSubset	IM
pubmed:status	MEDLINE
pubmed:month	Oct
pubmed:issn	0300-5771
pubmed:author	pubmed-author:CowlingBenjamin JBJ, pubmed-author:HoLai-MingLM, pubmed-author:LeungGabriel MGM, pubmed-author:RileyStevenS, pubmed-author:WongIrene O LIO
pubmed:issnType	Print
pubmed:volume	35
pubmed:owner	NLM
pubmed:authorsComplete	Y
pubmed:pagination	1314-21
pubmed:meshHeading	pubmed-meshheading:16926216-Algorithms, pubmed-meshheading:16926216-Disease Outbreaks, pubmed-meshheading:16926216-Hong Kong, pubmed-meshheading:16926216-Humans, pubmed-meshheading:16926216-Influenza, Human, pubmed-meshheading:16926216-Linear Models, pubmed-meshheading:16926216-Public Health, pubmed-meshheading:16926216-Seasons, pubmed-meshheading:16926216-Sentinel Surveillance, pubmed-meshheading:16926216-United States
pubmed:year	2006
pubmed:articleTitle	Methods for monitoring influenza surveillance data.
pubmed:affiliation	Department of Community Medicine and School of Public Health, The University of Hong Kong, Pokfulam, Hong Kong. bcowling@hku.hk
pubmed:publicationType	Journal Article, Research Support, Non-U.S. Gov't