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Optimal exponentially weighted moving average (EWMA) plans for detecting seasonal epidemics when faced with non-homogeneous negative binomial counts

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  • R. S. Sparks
  • T. Keighley
  • D. Muscatello

Abstract

Exponentially weighted moving average (EWMA) plans for non-homogeneous negative binomial counts are developed for detecting the onset of seasonal disease outbreaks in public health surveillance. These plans are robust to changes in the in-control mean and over-dispersion parameter of the negative binomial distribution, and therefore are referred to as adaptive plans. They differ from the traditional approach of using standardized forecast errors based on the normality assumption. Plans are investigated in terms of early signal properties for seasonal epidemics. The paper demonstrates that the proposed EWMA plan has efficient early detection properties that can be useful to epidemiologists for communicable and other disease control and is compared with the CUSUM plan.

Suggested Citation

  • R. S. Sparks & T. Keighley & D. Muscatello, 2011. "Optimal exponentially weighted moving average (EWMA) plans for detecting seasonal epidemics when faced with non-homogeneous negative binomial counts," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(10), pages 2165-2181.
    • Handle: RePEc:taf:japsta:v:38:y:2011:i:10:p:2165-2181
    DOI: 10.1080/02664763.2010.545184
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    References listed on IDEAS

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    Cited by:

    1. Yunhan Huang & Quanyan Zhu, 2022. "Game-Theoretic Frameworks for Epidemic Spreading and Human Decision-Making: A Review," Dynamic Games and Applications, Springer, vol. 12(1), pages 7-48, March.

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