Timeliness of Data Sources Used for Influenza Surveillance

Dailey, Lynne; Watkins, Rochelle; Plant, Aileen J.

doi:10.1197/jamia.m2328

Cited by 46 publications

(49 citation statements)

References 15 publications

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“…This means that each syndrome (number of visits to pharmacies, calls to "hot lines", sales of a particular product, visits of particular web sites, etc [5], [12]) is a linear function of the number of infected people. The observation model is then…”

Section: Model Of Syndromic Observationsmentioning

confidence: 99%

“…The ongoing epidemics of AIDS, tuberculosis and the recent outbreaks of SARS and H1N1 (swine flu) provide some revealing examples. In the absence of a cure against many diseases, the best approach to mitigate an epidemic outbreak (malicious or natural) resides in the development of capability for its early detection and for prediction of its further development [6], [12], [3]. Such a capability would allow c Commonwealth of Australia making any countermeasures (quarantine, vaccination, medical treatment) much more effective and less costly [25].…”

Section: Introductionmentioning

confidence: 99%

“…One of the promising methods for early detection of epidemics, which has recently attracted significant attention by scientists and practitioners, is the so-called method of syndromic surveillance (specific patterns of symptomatic behavior), for more details see [12] and [5]. The main idea of this method rests on the reasonable assumption (in some cases validated by field studies) that an increase in the number of infected people in the community is usually associated with the changes of some other indicators, which can be easily measured/estimated by "non-medical" means (e.g.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting an epidemic based on syndromic surveillance

Skvortsov

Ristić

Woodruff

2010

2010 13th International Conference on Information Fusion

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-Early detection and prediction of the size and the peak time of an epidemic outbreak (malicious or natural) is of crucial importance for a timely medical response (quarantine, vaccination, etc). A conventional approach to this problem is based on large scale agent-based computer simulations. This paper proposes an alternative framework formulated in the context of stochastic nonlinear filtering. The framework is based on the stochastic SIR epidemiological model of infection dynamics, with syndromic (often non-medical) observations of the number of infected people (e.g. visits to pharmacies, sale of certain products, absenteeism from work/study etc.). The unknown parameters of the SIR epidemic model are estimated via the sequential Monte Carlo method, with the prediction based on the dynamic model. The numerical results indicate that the proposed framework can provide useful early prediction of the epidemic peak if the uncertainty in prior knowledge of model parameters is not excessive.

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Section: Model Of Syndromic Observationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting an epidemic based on syndromic surveillance

Skvortsov

Ristić

Woodruff

2010

2010 13th International Conference on Information Fusion

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“…Our study is motivated by the fact that two types of surveillance data, particularly in terms of their timeliness, are available to the estimation task (Dailey et al 2007). For an epidemic flu, a traditional source of information for the current epidemic state is Influenza-Like-Illness (ILI) data from a government health agency such as Centers for Disease Control and Prevention (CDC) (FluView 2013; Influenza Weekly Report 2013).…”

mentioning

confidence: 99%

Combining syndromic surveillance and ILI data using particle filter for epidemic state estimation

Lee

Shin

2014

Flex Serv Manuf J

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Designing effective mitigation strategies against influenza outbreak requires an accurate prediction of a disease's future course of spreading. Real time information such as syndromic surveillance data and influenza-like-illness (ILI) reports by clinicians can be used to generate estimates of the current state of spreading of a disease. Syndromic surveillance data are immediately available, in contrast to ILI reports that require data collection and processing. On the other hand, they are less credible than ILI data because they are essentially behavioral responses from a community. In this paper, we present a method to combine immediatelyavailable-but-less-reliable syndromic surveillance data with reliable-but-timedelayed ILI data. This problem is formulated as a non-linear stochastic filtering problem, and solved by a particle filtering method. Our experimental results from hypothetical pandemic scenarios show that state estimation is improved by utilizing both sets of data compared to when using only one set. However, the amount of improvement depends on the relative credibility and length of delay in ILI data. An analysis for a linear, Gaussian case is presented to support the results observed in the experiments.

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“…Methods to identify epidemics include obtaining data from over-the-counter drug sales, sentinel practices, absenteeism records, telephone triage centers, home visits by general practitioners, health advice calls, and emergency departments [2][3][4][5][6][7][8][9][10][11][12][13]. More recently, identification of epidemics based on data from the web has been reported with promising results in studies using search engines [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Increased emergency department chief complaints of fever identified the influenza (H1N1) pandemic before outpatient symptom surveillance

Shimoni

Rodrig

Dusseldorp

et al. 2011

Environ Health Prev Med

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Objective To determine whether a sentinel clinic network or an emergency department (ED) was more timely in identifying the 2009 influenza A (H1N1) pandemic. Methods All reasons for presenting to the adult regional medical ED were coded online by admission secretaries, without the aid of medical personnel. Increased influenza activity defined by weekly chief complaints of fever was compared with activity defined by the Israel Center for Disease Control (viral surveillance as well as a large sentinel clinic network). Results Influenza activity during the pandemic increased in the ED 2 weeks before outpatient sentinel clinics. During the pandemic, maximal ED activity was much higher than in previous seasons. Maximal activity during the past 5 years correlated with the timeliness of the chief complaint of fever in identifying the onset of epidemics.Conclusion Chief complaint of fever in the ED can be a sensitive marker of increased influenza activity and might replace the use of sentinel clinics.

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Timeliness of Data Sources Used for Influenza Surveillance

Cited by 46 publications

References 15 publications

Predicting an epidemic based on syndromic surveillance

Predicting an epidemic based on syndromic surveillance

Combining syndromic surveillance and ILI data using particle filter for epidemic state estimation

Increased emergency department chief complaints of fever identified the influenza (H1N1) pandemic before outpatient symptom surveillance

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