about
The Nigerian health care system: Need for integrating adequate medical intelligence and surveillance systemsTime series modeling for syndromic surveillanceModeling emergency department visit patterns for infectious disease complaints: results and application to disease surveillanceA software tool for creating simulated outbreaks to benchmark surveillance systems.Early detection of tuberculosis outbreaks among the San Francisco homeless: trade-offs between spatial resolution and temporal scaleAn epidemiological network model for disease outbreak detectionA Bayesian dynamic model for influenza surveillance.Medication sales and syndromic surveillance, France.Syndromic surveillance using veterinary laboratory data: data pre-processing and algorithm performance evaluation.Syndromic surveillance using veterinary laboratory data: algorithm combination and customization of alertsCan syndromic surveillance data detect local outbreaks of communicable disease? A model using a historical cryptosporidiosis outbreak.Lumbar puncture ordering and results in the pediatric population: a promising data source for surveillance systems.Integrating syndromic surveillance data across multiple locations: effects on outbreak detection performance.Detection of outbreaks from time series data using wavelet transformUsing GIS to create synthetic disease outbreaksAEGIS: a robust and scalable real-time public health surveillance systemThe tell-tale heart: population-based surveillance reveals an association of rofecoxib and celecoxib with myocardial infarctionModeling and detection of respiratory-related outbreak signatures.Use of population health data to refine diagnostic decision-making for pertussisSurveillance of febrile patients in a district and evaluation of their spatiotemporal associations: a pilot study.Identifying pediatric age groups for influenza vaccination using a real-time regional surveillance system.Implementing syndromic surveillance: a practical guide informed by the early experience.Epidemic features affecting the performance of outbreak detection algorithmsBayesian information fusion networks for biosurveillance applicationsDetection of disease outbreaks by the use of oral manifestations.Evaluating detection of an inhalational anthrax outbreak.Modeling the optimum duration of antibiotic prophylaxis in an anthrax outbreakA simulation study to evaluate the performance of five statistical monitoring methods when applied to different time-series components in the context of control programs for endemic diseasesHigh-fidelity injection detectability experiments: a tool for evaluating syndromic surveillance systems.Integrating spatial epidemiology into a decision model for evaluation of facial palsy in children.Assessing the utility of public health surveillance using specificity, sensitivity, and lives saved.Understanding detection performance in public health surveillance: modeling aberrancy-detection algorithms.A susceptible-infected model of early detection of respiratory infection outbreaks on a background of influenzaDecision theoretic analysis of improving epidemic detection.Electronic medical record (EMR) utilization for public health surveillance.Predicting outbreak detection in public health surveillance: quantitative analysis to enable evidence-based method selection.Building test data from real outbreaks for evaluating detection algorithms.Recombinant temporal aberration detection algorithms for enhanced biosurveillance.Template-driven spatial-temporal outbreak simulation for outbreak detection evaluation.The perspective of syndromic surveillance systems on public health threats: a paradigm of the Athens 2004 Olympic Games.
P2860
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P2860
description
2003 nî lūn-bûn
@nan
2003 թուականի Փետրուարին հրատարակուած գիտական յօդուած
@hyw
2003 թվականի փետրվարին հրատարակված գիտական հոդված
@hy
2003年の論文
@ja
2003年論文
@yue
2003年論文
@zh-hant
2003年論文
@zh-hk
2003年論文
@zh-mo
2003年論文
@zh-tw
2003年论文
@wuu
name
Using temporal context to improve biosurveillance.
@ast
Using temporal context to improve biosurveillance.
@en
Using temporal context to improve biosurveillance.
@nl
type
label
Using temporal context to improve biosurveillance.
@ast
Using temporal context to improve biosurveillance.
@en
Using temporal context to improve biosurveillance.
@nl
prefLabel
Using temporal context to improve biosurveillance.
@ast
Using temporal context to improve biosurveillance.
@en
Using temporal context to improve biosurveillance.
@nl
P2093
P2860
P356
P1476
Using temporal context to improve biosurveillance.
@en
P2093
Ben Y Reis
Kenneth D Mandl
Marcello Pagano
P2860
P304
P356
10.1073/PNAS.0335026100
P407
P577
2003-02-06T00:00:00Z