Detection of aberrations in the occurrence of notifiable diseases surveillance data

Stroup, Donna F.; Williamson, G. David; Herndon, Joy L.; Karon, John M.

doi:10.1002/sim.4780080312

Cited by 123 publications

(90 citation statements)

References 6 publications

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“…Aberration Detection: A standard aberration detection algorithm was used to calculate the running mean of historical cases for the time period 2006-2010 [27][28][29]. Current cases were grouped in 4 week intervals to be compared to five years of historical data for the same 4 week intervals.…”

Section: Methodsmentioning

confidence: 99%

Determining Risk Factors of a Non-Point Source Outbreak of Campylobacter Cases Using Case-Case and Case-Control Studies

Pogreba-Brown¹,

Ernst²

2015

Epidemiology (Sunnyvale)

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Background: Investigating foodborne outbreaks is a resource and time-intensive process using traditional casecontrol methodology. The use of case-case studies in outbreak investigations is not well studied, although they require fewer resources to conduct and limit selection and recall bias. In this study we investigated a cluster of Campylobacter infections using almost simultaneous case-control and case-case studies to compare results from the two methodologies.

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Section: Methodsmentioning

confidence: 99%

Determining Risk Factors of a Non-Point Source Outbreak of Campylobacter Cases Using Case-Case and Case-Control Studies

Pogreba-Brown¹,

Ernst²

2015

Epidemiology (Sunnyvale)

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“…Starting on 1 June 2009, the expected ILI incidence was calculated for each week as the average of weekly ILI incidences reported in the preceding, current and following weeks in the period 1985 to 2008 [4]. A 90% confidence interval was derived from the 5th and 95th percentiles of these values (Q 5 and Q 95 , respectively) for each week.…”

Section: Expected and Excess Ili Casesmentioning

confidence: 99%

Early estimates of 2009 pandemic influenza A(H1N1) virus activity in general practice in France: incidence of influenza-like illness and age distribution of reported cases

et al. 2009

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In the end of August 2009, an unusually elevated level of influenza-like illness (ILI) activity was reported to the French Sentinel Network. We quantified the observed excess in ILI cases in France during summer 2009 and characterised age patterns in reported cases. An excess of cases has been observed since 5 July, with a time increasing trend. The cumulated estimated excess number of ILI cases was 269,935 [179,585; 316,512], corresponding to 0.5% French population over the period. Compared to the same period in the past years, relative cumulated incidence was greater among young subjects and lower among subjects over 65 years-old. Compared to past epidemics, the relative cumulated incidence was greater in children less than five years-old. This excess of cases may reflect the current spread of the A(H1N1) virus in France, subject to the following limitations: estimates were based on clinical cases consulting a GP; large media coverage may have led to a non specific increase in consultation rates.

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“…Le Straat (2005) [11] comprehensively reviews to that date the statistical methods that have been applied for detecting or monitoring outbreaks and monitoring trends of diseases. Some of them have been implemented in the R-package surveillance, in particular, those by Stroup et al (1989) [24], Farrington et al (1996) [2] and Höhle and Riebler (2005) [10]. The list of contributions in surveillance is still growing (see, for instance, recent papers by Held et al (2005Held et al ( , 2006 [8,9]).…”

mentioning

confidence: 99%

Bayesian Markov switching models for the early detection of influenza epidemics

Martínez-Beneito

Conesa

López‐Quílez

et al. 2008

Statistics in Medicine

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The early detection of the outbreaks of diseases is one of the most challenging objectives of epidemiological surveillance systems. In this paper, a Markov switching model is introduced to determine the epidemic and non-epidemic periods from influenza surveillance data: the process of differenced incidence rates is modelled either with a first-order autoregressive process or with a Gaussian white noise process depending on whether the system is in an epidemic or a nonepidemic phase. The transition between phases of the disease is modelled as a Markovian process. Bayesian inference is carried out on the former model to detect influenza epidemics at the very moment of their onset. Moreover, the proposal provides the probability of being in an epidemic state at any given moment. The methodology is evaluated on influenza illness data obtained from the Sanitary Sentinel Network of the Comunitat Valenciana, one of the 17 autonomous regions in Spain.

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Detection of aberrations in the occurrence of notifiable diseases surveillance data

Cited by 123 publications

References 6 publications

Determining Risk Factors of a Non-Point Source Outbreak of Campylobacter Cases Using Case-Case and Case-Control Studies

Determining Risk Factors of a Non-Point Source Outbreak of Campylobacter Cases Using Case-Case and Case-Control Studies

Early estimates of 2009 pandemic influenza A(H1N1) virus activity in general practice in France: incidence of influenza-like illness and age distribution of reported cases

Bayesian Markov switching models for the early detection of influenza epidemics

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