About: Exploring a Proposed WHO Method to Determine Thresholds for Seasonal Influenza Surveillance

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Exploring a Proposed WHO Method to Determine Thresholds for Seasonal Influenza Surveillance
Creator	Kelly, Heath Grant, Kristina Kirk, Martyn Mounts, Anthony Tay, Ee
Source	Medline; PMC
abstract	INTRODUCTION: Health authorities find thresholds useful to gauge the start and severity of influenza seasons. We explored a method for deriving thresholds proposed in an influenza surveillance manual published by the World Health Organization (WHO). METHODS: For 2002-2011, we analysed two routine influenza-like-illness (ILI) datasets, general practice sentinel surveillance and a locum medical service sentinel surveillance, plus laboratory data and hospital admissions for influenza. For each sentinel dataset, we created two composite variables from the product of weekly ILI data and the relevant laboratory data, indicating the proportion of tested specimens that were positive. For all datasets, including the composite datasets, we aligned data on the median week of peak influenza or ILI activity and assigned three threshold levels: seasonal threshold, determined by inspection; and two intensity thresholds termed average and alert thresholds, determined by calculations of means, medians, confidence intervals (CI) and percentiles. From the thresholds, we compared the seasonal onset, end and intensity across all datasets from 2002-2011. Correlation between datasets was assessed using the mean correlation coefficient. RESULTS: The median week of peak activity was week 34 for all datasets, except hospital data (week 35). Means and medians were comparable and the 90% upper CIs were similar to the 95(th) percentiles. Comparison of thresholds revealed variations in defining the start of a season but good agreement in describing the end and intensity of influenza seasons, except in hospital admissions data after the pandemic year of 2009. The composite variables improved the agreements between the ILI and other datasets. Datasets were well correlated, with mean correlation coefficients of >0.75 for a range of combinations. CONCLUSIONS: Thresholds for influenza surveillance are easily derived from historical surveillance and laboratory data using the approach proposed by WHO. Use of composite variables is helpful for describing influenza season characteristics.
has issue date	2013-10-11(xsd:dateTime)
bibo:doi	10.1371/journal.pone.0077244
bibo:pmid	24146973
has license	cc-by
sha1sum (hex)	5143bcbf109f693de355d2512a5810ed93a3072a
schema:url	https://doi.org/10.1371/journal.pone.0077244
resource representing a document's title	Exploring a Proposed WHO Method to Determine Thresholds for Seasonal Influenza Surveillance
has PubMed Central identifier	PMC3795663
has PubMed identifier	24146973
schema:publication	PLoS One
resource representing a document's body	covid:5143bcbf109f693de355d2512a5810ed93a3072a#body_text
is schema:about of	named entity 'authorities' named entity 'INFLUENZA SURVEILLANCE' named entity 'AUTHORITIES' named entity 'GAUGE' named entity 'START' named entity 'INFLUENZA SURVEILLANCE' named entity 'PUBLISHED' named entity 'HEALTH' named entity 'DERIVING' named entity 'SEVERITY' named entity 'METHOD' named entity 'USEFUL' named entity 'SEASONS' named entity 'DETERMINE' named entity 'PROPOSED' named entity 'WORLD HEALTH ORGANIZATION' named entity 'EXPLORING' named entity 'INFLUENZA' named entity 'THRESHOLDS' named entity 'SEASONAL INFLUENZA' named entity 'PROPOSED' named entity 'THRESHOLDS' named entity 'MANUAL' named entity 'WHO METHOD' covid:arg/5143bcbf109f693de355d2512a5810ed93a3072a named entity 'gauge' named entity 'start' named entity 'WHO' named entity 'influenza' named entity 'influenza' named entity 'CuSum' named entity 'MMDS' named entity 'Victoria' named entity 'count data' named entity 'ILI' named entity 'ILI' named entity 'MMDS' named entity 'influenza' named entity 'Melbourne' named entity 'syndromic surveillance' named entity 'WHO' named entity 'Victoria' named entity 'GPSS' named entity 'influenza' named entity 'older age' named entity 'influenza' named entity 'historical data' named entity 'WHO' named entity 'ILI' named entity 'WHO' named entity 'influenza' named entity 'influenza' named entity 'United Kingdom' named entity 'ILI' named entity 'anti-viral' named entity 'influenza season' named entity 'GPSS' named entity 'health care' named entity 'influenza' named entity 'cough' named entity 'Europe' named entity 'MMDS' named entity 'Normal distribution' named entity 'count data' named entity 'CDC' named entity 'seasonal influenza' named entity 'early spring' named entity 'ICD-10-AM' named entity 'temperate climate' named entity 'influenza virus' named entity 'WHO' named entity 'Normal distribution' named entity 'influenza' named entity 'influenza-like illness' named entity 'ILI' named entity 'MMDS' named entity 'proxy indicator'

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