Real-time monitoring of events applied to syndromic surveillance. Issue Volume 31:Issues 1(2019) (2nd January 2019)

• Record Type: Journal Article
• Authors: Sparks, Ross; Jin, Brian; Karimi, Sarvnaz; Paris, Cecile; MacIntyre, C. R.
• Is Part Of: Quality engineering. Volume 31:Issues 1(2019)
• Page Start: 73
• Page End: 90
• Publication Date: 2019-01-02
• Subjects: counting processes -- EWMA -- quantiles -- statistical process control -- time between events; Quality control -- Periodicals; Production management -- Quality control -- Periodicals; 658.5
• DOI: 10.1080/08982112.2018.1537443
• Languages: English
• ISSNs: 0898-2112

Abstract

Abstract: This article focuses on monitor plans aimed at the early detection of the increase in the frequency of events. The literature recommends either monitoring the time between events (TBE) if events are rare or counting the number of events per unit non-overlapping time intervals otherwise. Some authors advocate using the Bernoulli model for rare events, applying presence or absence of events within non-overlapping and exhaustive time intervals. This Bernoulli model does improve the real-time monitoring assessment of these events compared to counting events over a larger interval, making them less rare. However this approach became inefficient if more than one event starts occurring within the intervals. Monitoring TBE is the real-time option for outbreak detection, because outbreak information is accumulated when an event occurs. This is preferred to waiting for the end of a period to count events. If the TBE reduces significantly, then the incidence of these events increases significantly. This article explores this TBE option relative to using the monitoring of counts when the TBEs are either Exponentially, Gamma or Weibull distributed for moderately low count scenarios. The article will discuss and compare the approaches of using an Exponentially Weighted Moving Average (EWMA) statistic for the TBEs to the EWMA of counts. Several robust options will be considered when the future change in event frequency is unknown. Our goal is to have a robust monitoring plan which is able to efficiently detect many different levels of shifts. These robust plans are compared to the more traditional event monitoring plans for both small and large changes in the event frequency.