Surveillance Strategies for Detecting Changepoint in Incidence Rate Based on Exponentially Weighted Moving Average Methods

Abstract

Surveillance is a major issue in the today's world. The need to develop adequate surveillance strategies is genuine in many spheres of human activity. In this article we focus on a specific problem of surveillance and discuss some related statistical issues. This specific problem deals with a possible change in the incidence rate of an event (to a higher value) when a system is studied at discrete time points. We apply the exponentially weighted moving average methods for detecting an increased incidence rate per exposure unit of an event. Different measures of evaluation, suitable in different types of applications, such as the expected delay, out-of-control average run length, and probability of successful detection, are studied. Analytical bounds are provided for those measures of evaluation. Results from intensive simulations indicate that the analytical bounds perform fairly well when the weight parameter is large.