Control charts for threshold correlated count data in disease infection number monitoring

Abstract

AbstractTimely monitoring is essential for effective disease control and public health management. Early outbreak detection and swift responses inform optimal resource allocation for effective public health protection. Disease incidence datasets often consist of time series counts that exhibit threshold characteristics. Currently, there is limited research on the monitoring of such data in the field of statistical process control. In this paper, we propose a new threshold‐based control chart for threshold autoregressive models, which has been proven to be effective. Several existing efficient control charts are also employed for comparison. We conduct an extensive simulation study using the Monte Carlo method. Finally, the method is applied to the meningitis data that motivated this investigation.