EWMA control chart based on its first hitting time and coronavirus alert levels for monitoring symmetric COVID-19 cases

Abstract

Objective: To define the alert levels for the total number of COVID-19 cases derived by using quantile functions to monitor COVID-19 outbreaks via an exponentially weighted moving-average (EWMA) control chart based on the first hitting time of the total number of COVID-19 cases following a symmetric logistic growth curve.Methods: The cumulative distribution function of the time for the total number of COVID-19 cases was used to construct a quantile function for classifying COVID-19 alert levels. The EWMA control chart control limits for monitoring a COVID-19 outbreak were formulated by applying the delta method and the sample mean and variance method. Samples were selected from countries and region including Thailand, Singapore, Vietnam, and Hong Kong to generate the total number of COVID-19 cases from February 15, 2020 to December 16, 2020, all of which followed symmetric patterns. A comparison of the two methods was made by applying them to a EWMA control chart based on the first hitting time for monitoring the COVID-19 outbreak in the sampled countries and region.Results: The optimal first hitting times for the EWMA control chart for monitoring COVID-19 outbreaks in Thailand, Singapore, Vietnam, and Hong Kong were approximately 280, 208, 286, and 298 days, respectively.Conclusions: The findings show that the sample mean and variance method can detect the first hitting time better than the delta method. Moreover, the COVID-19 alert levels can be defined into four stages for monitoring COVID-19 situation, which help the authorities to enact policies that monitor, control, and protect the population from a COVID-19 outbreak.