About: Spatial and Temporal Algorithm Evaluation for Detecting Over-The-Counter Thermometer Sale Increases during 2009 H1N1 Pandemic

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About: Spatial and Temporal Algorithm Evaluation for Detecting Over-The-Counter Thermometer Sale Increases during 2009 H1N1 Pandemic Goto Sponge NotDistinct Permalink

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Spatial and Temporal Algorithm Evaluation for Detecting Over-The-Counter Thermometer Sale Increases during 2009 H1N1 Pandemic
Creator	Tsui, Fu-Chiang Que, Jialan
Source	Medline; PMC
abstract	BACKGROUND: Spatial outbreak detection algorithms using routinely collected healthcare data have been developed since the late 90s to identify and locate disease outbreaks. However, current well-received spatial algorithms assume only one outbreak cluster present at the same point of time which may not be valid during a pandemic when several clusters of geographic areas concurrently occur. Based on a retrospective evaluation on time-series and spatial algorithms, this paper suggests that time series analysis in detection of pandemics is still a desirable process, which may achieve more sensitive performance with better timeliness. METHODS: In this paper, we first prove in theory that two existing spatial models, the likelihood ratio and the Bayesian spatial scan statistics, are not useful if multiple clusters occur at the same point of time in different geographic regions. Then we conduct a comparison between a spatial algorithm, the Bayesian Spatial Scan Statistic (BSS), and a time series algorithm, the wavelet anomaly detector (WAD), on the performance of detecting the increase of the over-the-counter (OTC) medicine sales during 2009 H1N1 pandemic. RESULTS: The experiments demonstrated that the Bayesian spatial algorithm responded to the increase of thermometer sales about 3 days later than the time series algorithm. CONCLUSION: Time-series algorithms demonstrated an advantage for early outbreak detection, especially when multiple clusters occur at the same time in different geographic regions. Given spatial-temporal algorithms for outbreak detection are widely used, this paper suggests that epidemiologists or public health officials would benefit by applying time series algorithms as a complement to spatial algorithms for public health surveillance.
has issue date	2012-05-17(xsd:dateTime)
bibo:doi	10.5210/ojphi.v4i1.3915
bibo:pmid	23569624
has license	no-cc
sha1sum (hex)	14180d7522cd39919afbfe4a6725e1cfc51229bc
schema:url	https://doi.org/10.5210/ojphi.v4i1.3915
resource representing a document's title	Spatial and Temporal Algorithm Evaluation for Detecting Over-The-Counter Thermometer Sale Increases during 2009 H1N1 Pandemic
has PubMed Central identifier	PMC3615801
has PubMed identifier	23569624
schema:publication	Online J Public Health Inform
resource representing a document's body	covid:14180d7522cd39919afbfe4a6725e1cfc51229bc#body_text
is schema:about of	named entity 'EVALUATION' named entity 'PANDEMIC' named entity 'Temporal' named entity 'Spatial' named entity 'H1N1' named entity 'INCREASING' named entity 'THERMOMETER' named entity 'BACKGROUND' named entity 'ALGORITHM' named entity 'SALE' named entity 'OVER-THE-COUNTER' named entity 'DETECTING' named entity 'TEMPORAL' named entity 'SPATIAL' named entity 'INCREASES' covid:arg/14180d7522cd39919afbfe4a6725e1cfc51229bc named entity 'statistical test' named entity 'algorithm' named entity 'time series' named entity 'randomization test' named entity 'Dallas' named entity 'public health surveillance' named entity 'ISDS' named entity 'time series' named entity 'spatial analysis' named entity 'correlation' named entity 'data sets' named entity 'frequentist approach' named entity 'algorithm' named entity 'rank-based' named entity 'time series' named entity 'spatial models' named entity 'false negatives' named entity 'El Paso' named entity 'H1N1' named entity 'algorithm' named entity 'U.S.' named entity 'ZIP code' named entity 'H1N1' named entity 'ZIP code' named entity 'algorithm' named entity 'time series' named entity 'Wavelet' named entity 'likelihood ratio' named entity 'Mexico' named entity 'ZIP code' named entity 'diarrhea' named entity 'posterior probabilities' named entity 'likelihood ratio' named entity 'ZIP code' named entity 'real-world' named entity 'ZIP code' named entity 'algorithm' named entity 'Bayesian models' named entity 'Spatial' named entity '2009 H1N1' named entity 'time series' named entity 'May 7' named entity 'posterior probabilities' named entity 'OTC' named entity 'Gamma distribution' named entity 'April 27' named entity 'infectious disease outbreaks' named entity 'thermometer' named entity 'Texas' named entity 'Texas' named entity 'algorithm' named entity 'significance levels' named entity 'Texas' named entity 'posterior probability' named entity 'wavelet' named entity 'standard deviations' named entity 'likelihood ratio' named entity 'medical treatment' named entity 'moving average' named entity 'Texas' named entity 'posterior probability' named entity 'swine flu' named entity 'flu' named entity 'H1N1'

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