Abstract
Automated public health records provide the necessary data for rapid outbreak detection. An adaptive exponentially weighted moving average (EWMA) plan is developed for signalling unusually high incidence when monitoring a time series of nonhomogeneous daily disease counts. A Poisson transitional regression model is used to fit background/expected trend in counts and provides “one-day-ahead” forecasts of the next day's count. Departures of counts from their forecasts are monitored. The paper outlines an approach for improving early outbreak data signals by dynamically adjusting the exponential weights to be efficient at signalling local persistent high side changes. We emphasise outbreak signals in steady-state situations; that is, changes that occur after the EWMA statistic had run through several in-control counts.
Highlights
Detection of disease outbreaks is essential for the efficient control of acute public health risks
We offer a practical way of dynamically changing the exponential weights, giving the plan a better chance of detecting predictable yet relatively small unusual epidemic footprints
The dynamic Poisson adaptive EWMA plan has an advantage over the Poisson adaptive EWMA plan with λ 0.1 in this example
Summary
Detection of disease outbreaks is essential for the efficient control of acute public health risks. Weighted moving average EWMA control charts have been useful for monitoring industrial production processes e.g., see Lucas and Saccucci 3 More recently they have been used in public health surveillance see review article by Woodall 1 and for monitoring network counts see Lambert and Liu 4. Unlike Grigg and Spiegelhalter 8 a primary aim of our work is to improve the early detection of outbreak data signals; a major goal of automated real-time public health surveillance. The average time to signal for a step change of δ in mean denoted by ATSδ is used to measure a plan s early detection performance. A simulated example is used to demonstrate the efficiency of the recommended improvement strategy and demonstrate that this plan is significantly better than using the “optimal” adaptive CUSUM of pseudo residuals in terms of early signals for the same false alarm rate.
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