A Review of Healthcare, Public Health, and Syndromic Surveillance

Tsui, Kwok Leung; Chiu, Wenchi; Gierlich, Peter; Goldsman, David; Liu, Xuyuan; Maschek, Thomas

doi:10.1080/08982110802334138

Cited by 74 publications

(32 citation statements)

References 59 publications

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“…Jackson et al 37 compared the performance of EMWA, Shewhart chart, and a general linear model method based on the data set having day-of-the-week effects. Because of the spatiotemporal characteristics of biosurveillance data, modifications of the multivariate SPC techniques such as MCUSUM and MEWMA have been suggested by Joner et al 35 and Fricker et al 38 Comprehensive review of the applications of SPC methods to health surveillance can be found in Woodall 1 and Tsui et al 4 .…”

Section: Introductionmentioning

confidence: 98%

A comparison of CUSUM, EWMA, and temporal scan statistics for detection of increases in poisson rates

Han

Tsui

Ariyajunya

et al. 2009

Quality & Reliability Eng

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Section: Introductionmentioning

confidence: 98%

A comparison of CUSUM, EWMA, and temporal scan statistics for detection of increases in poisson rates

Han

Tsui

Ariyajunya

et al. 2009

Quality & Reliability Eng

Self Cite

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“…Woodall [6] provides detailed discussions of these methods for healthcare applications. Tsui et al [7] provides detailed reviews on public health surveillance.…”

Section: Healthcare and Public Health Surveillancementioning

confidence: 99%

“…Tsui et aI., [7] highlight the importance of investigating and comparing the performance measures of surveillance methods under various scenarios. Recent research has been undertaken to fill in the existing research gap.…”

Section: Temporal Surveillancementioning

confidence: 99%

Recent research in public health surveillance and health management

Tsui¹,

Goldsman²,

Jiang³

et al. 2010

2010 Prognostics and System Health Management Conference

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While the challenges of the next pandemic outbreak are overwhelming, either from swine flu, other infectious disease, bioterrorism, timely detection of disease outbreaks is most important for public health surveillance and society safety and stability. In public health surveillance, the objective is to systematically collect, analyze, and interpret public health data (chronic or infectious diseases) in order to understand trends, to detect changes in disease incidence and death rates, and to plan, implement, and evaluate public health practice. Recently much research has been conducted to develop methods and algorithms for health surveillance and disease detection. This paper presents an overview and reviews the recent research methods on temporal and spatiotemporal surveillance. Specific research challenges and future research directions are discussed. A real life example is used to compare the performance of three currently used surveillance methods, scan, EWMA, and CUSUM.

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“…In addition to traditional methods including the multivariate cumulative sum and exponentially weighted moving average approaches, Rolka et al (2007) considered the spatiotemporal aspect of biosurveillance data and the use of spatial scan statistics as well as Bayesian network models to combine multiple data streams. Tsui et al (2008) also considered public health and disease surveillance, giving an overview of the most popular methods, and discussing research opportunities in spatiotemporal surveillance. Fraker et al (2008) and Megahed et al (2012) discussed the advantages and disadvantages of several performance metrics applied to health surveillance, recommending metrics based on time-to-signal properties.…”

Section: Multistream Datamentioning

confidence: 99%

Statistical Perspectives on “Big Data”

Megahed

Jones‐Farmer

2015

Frontiers in Statistical Quality Control 11

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As our information infrastructure evolves, our ability to store, extract, and analyze data is rapidly changing. Big data is a popular term that is used to describe the large, diverse, complex and/or longitudinal datasets generated from a variety of instruments, sensors and/or computer-based transactions. The term big data refers not only to the size or volume of data, but also to the variety of data and the velocity or speed of data accrual. As the volume, variety, and velocity of data increase, our existing analytical methodologies are stretched to new limits. These changes pose new opportunities for researchers in statistical methodology, including those interested in surveillance and statistical process control methods. Although it is well documented that harnessing big data to make better decisions can serve as a basis for innovative solutions in industry, healthcare, and science, these solutions can be found more easily with sound statistical methodologies. In this paper, we discuss several big data applications to highlight the opportunities and challenges for applied statisticians interested in surveillance and statistical process control. Our goal is to bring the research issues into better focus and encourage methodological developments for big data analysis in these areas.

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A Review of Healthcare, Public Health, and Syndromic Surveillance

Cited by 74 publications

References 59 publications

A comparison of CUSUM, EWMA, and temporal scan statistics for detection of increases in poisson rates

A comparison of CUSUM, EWMA, and temporal scan statistics for detection of increases in poisson rates

Recent research in public health surveillance and health management

Statistical Perspectives on “Big Data”

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