Exponentially weighted moving average plans for detecting unusual negative binomial counts

Abstract

Exponentially Weighted Moving Average (EWMA) plans for negative binomial counts with a non-homogeneous (time-varying) mean are developed for monitoring disease counts. These plans are used to identify unusual disease outbreaks or unusual epidemics. Time-varying means are typical for disease counts. The recommended surveillance plan in this article differs from the traditional approach of using standardized forecast errors based on the normality assumption, which suffers assumption concerns. The article demonstrates that the proposed EWMA plan has efficient detection properties for signaling unusually large outbreaks. These plans may be a useful tool for epidemiologists.

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