Applying cusum-based methods for the detection of outbreaks of Ross River virus disease in Western Australia

Watkins, Rochelle; Eagleson, Serryn; Veenendaal, Bert; Wright, Graeme; Plant, Aileen J.

doi:10.1186/1472-6947-8-37

Cited by 53 publications

(53 citation statements)

References 17 publications

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“…Our experimental results were compared with 2 other existing methods on the same dataset, namely the MAV [16] and the CUSUM [17,18], which were discussed in related works. Long et al applied the MAV to the dengue data and then compared their results with CUSUM, which is a statistical technique [16].…”

Section: Resultsmentioning

confidence: 99%

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Negative selection algorithm for dengue outbreak detection

Mousavi

Bakar

Zainudin

et al. 2013

Turk J Elec Eng & Comp Sci

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Abstract:Dengue is a critical communicable and vector-borne disease and is becoming a serious concern in Malaysia. It is important to have an early detection system that could provide immediate action, such as the control of dengue transmission at a specific location. However, the available strategy and action may give long-term effects to the community since inaccurate decision making or prediction may lead to other circumstances. Moreover, the need to have a system that can detect the outbreak in a reasonable amount of time is critical. In this study, a nature-inspired computing technique, the artificial immune system (AIS), is used for dengue outbreak detection. One of the variants of the AIS algorithms, called the negative selection algorithm (NSA), has been widely applied in anomaly detection and fault detection. This study aims to employ the NSA for dengue outbreak detection.

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Section: Resultsmentioning

confidence: 99%

“…The CUSUM is often used in the detection of small changes in a process parameter that are important. The CUSUM and regression are frequently used as tools in detecting outbreaks within a dataset [17,18]. In this study, MAV and CUSUM will be used as comparative methods.…”

Section: Related Studiesmentioning

confidence: 99%

Negative selection algorithm for dengue outbreak detection

Mousavi

Bakar

Zainudin

et al. 2013

Turk J Elec Eng & Comp Sci

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“…Some researchers have treated special cases in the EF CUSUM family, including Hawkins and Olwell (1997) on detecting known location and shape change in inverse gamma distribution, Hawkins and Zamba (2005) on change point detection in unknown mean and variance for normal distribution, Watkins et al (2008) used negative binomial CUSUM to study outbreaks of Ross River virus disease and compared it to Early Aberration Reporting System CUSUM algorithms, Wu et al (2008) studied large shifts in fraction non-conforming, and Lucas (1985) improved the Poisson CUSUM with fast initial response (FIR) and introduced the two-in-a-row rule to robust CUSUM. Healy (1987) discussed shift in mean and covariance for multivariate normal distribution using CUSUM, Alwan (2000) proposed transformation to normality to deal with EF CUSUM chart, Severo and Gama (2010) discussed using Kalman filter and CUSUM to detect residual mean and variance in the regression model, and Qiu and Hawkins (2001) used a rank-based CUSUM procedure to deal with multivariate measurements without normality assumption.…”

Section: Literature Reviewmentioning

confidence: 99%

Instability and change detection in exponential families and generalized linear models, with a study of Atlantic tropical storms

Chatterjee

2014

Nonlin. Processes Geophys.

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Abstract. Exponential family statistical distributions, including the well-known normal, binomial, Poisson, and exponential distributions, are overwhelmingly used in data analysis. In the presence of covariates, an exponential family distributional assumption for the response random variables results in a generalized linear model. However, it is rarely ensured that the parameters of the assumed distributions are stable through the entire duration of the data collection process. A failure of stability leads to nonsmoothness and nonlinearity in the physical processes that result in the data. In this paper, we propose testing for stability of parameters of exponential family distributions and generalized linear models. A rejection of the hypothesis of stable parameters leads to change detection. We derive the related likelihood ratio test statistic. We compare the performance of this test statistic to the popular normal distributional assumption dependent cumulative sum (Gaussian CUSUM) statistic in change detection problems. We study Atlantic tropical storms using the techniques developed here, so to understand whether the nature of these tropical storms has remained stable over the last few decades.

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“…Ein Verfahren stützt sich auf die Ausreißerana-lyse auf der Basis zurückliegender Vorjahresdaten [14,15,16,17], während das zweite Verfahren auf Entwicklungen zugreift, die kurzzeitig auftretende Verände-rungen der Dynamik von Entwicklungsgeschehen berücksichtigen [18,19]. Jeder der zugrunde liegenden Algorithmen besitzt gleichermaßen Vor-und Nachteile [20,21,22].…”

Section: Das Fish-modul: Zeitlich Orientierte Ausreißeranalyseunclassified

Integriertes Bulletin zur automatisierten Surveillance meldepflichtiger Infektionserkrankungen in Schleswig-Holstein (IBISSH)

Läubrich

Bocter

Fickenscher

et al. 2011

Bundesgesundheitsbl.

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The program package "Integrated Bulletin for Infectious disease Surveillance for Schleswig-Holstein" (IBIS(SH)) was introduced in 2008 for the automated data analysis of notifiable infectious diseases in Schleswig-Holstein, Germany. The Java-based IBIS(SH) software supports access to the national SurvNet@RKI reporting data via Access and MS SQL. The aim of the IBIS(SH) system is early warning and interpretation of clusters and monitoring of trends. One module of the system permits the analysis of temporal aberration by comparison of data from previous years. The interpretation system is based on the weekly median and on percentile values from previous years. The extent of an aberration is assessed by a five-step score magnitude scale. Another module permits the detection of regional clusters by the weekly assessment of a population-based risk analysis. IBIS(SH) automatically generates tables and graphs for the weekly bulletin and their allocation in the Internet. Data for the most relevant pathogens in Schleswig-Holstein are presented for the year 2009. The performance of the automated temporal and the regional detection systems are compared to outbreak detection by local health authorities.

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Applying cusum-based methods for the detection of outbreaks of Ross River virus disease in Western Australia

Cited by 53 publications

References 17 publications

Negative selection algorithm for dengue outbreak detection

Negative selection algorithm for dengue outbreak detection

Instability and change detection in exponential families and generalized linear models, with a study of Atlantic tropical storms

Integriertes Bulletin zur automatisierten Surveillance meldepflichtiger Infektionserkrankungen in Schleswig-Holstein (IBISSH)

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