A Study of the Average Run Length Characteristics of the National Notifiable Diseases Surveillance System

Lewis VanBrackle, Kennesaw State University
G. David Williamson, Center for Disease Control


This study examines the statistical properties (that is, false positive and negative signals) in detecting unusual patterns of reported cases of diseases from the Centers for Disease Control and Prevention's National Notifiable Diseases Surveillance System. Control charts are applied to the residuals of one-step ahead forecasts based on Box-Jenkins models of reported cases of disease. Simulation and analytical techniques are used to study the average run length characteristics of these control charts for various types of changes in the levels of the series, including spike, trend and step changes. The average run lengths for the highly correlated disease series are much longer than for the usual independent data case. This increase in the average run lengths is strongly influenced by the type of change in the level of the series and by the type of control chart. Understanding the average run length characteristics of the control charts can lead to timely detection of changes in the levels of disease series, and subsequent timely public hearth actions to decrease unnecessary morbidity and mortality.