Using the Laney p’ Control Chart for Monitoring COVID-19 Cases in Jordan

Abstract

In this research, we examine the use of the Laney p’ control chart and the application of test rules to assess governmental interventions throughout the COVID-19 pandemic and understand how certain activities and events that took place affected the infection rate. Data for the infection rate (IR) were collected between October 31, 2020, and March 19, 2022. The IR was calculated by dividing the number of confirmed cases by the number of PCR (polymerase chain reaction) tests performed. The IR data were subsequently plotted on the Laney p’ control charts using the Minitab software. The charts thereby allowed us to study the effects on infection rates of the government’s moves to restrict the movements and activities of the population, as well as the results of easing these restrictions. The restrictive measures proved to be effective in decreasing the infection rate, whereas relaxing these measures had the opposite effect. Typically, test signals are considered as an indication of a change in a process, although in some situations we have observed that slight changes are not accompanied by a signal. Regardless, the analysis shows cases where using test rules rapidly detected patterns and changes in IR, and allowing remedial action to be taken without delay. In this study, we use the Laney p’ control chart to monitor the COVID-19 IR and compare its performance with that of the EWMA control chart. In addition, we analyze the performance of various test rules in detecting IR changes. Comparing the Laney p’ control chart with the EWMA control chart, the data showed that in most cases, the Laney p’ control chart was able to identify the change of IR faster. Comparing the performance of different tests in detecting changes in the IR, one can see that no particular test outperformed the others in all cases. We also recommend analyzing the data points in both single-stage and multistage analyses in accordance with this new perspective rather than the traditional one used in process improvement projects. Accordingly, the single-stage analysis gives a complete picture of how the infection rate is changing overall, whereas the multistage analysis is more sensitive to small changes.

Using Quality Control Charts for Monitoring COVID-19 Daily Cases and Deaths in Türkiye

“…The centerline (CL) and upper and lower limits (UCL, LCL) of the pcontrol chart are computed as follows. Here, Di shows the nonconforming items in sample i, ni (sample size) is the number of units per sample i, and m shows the number of samples [6].…”

“…Arafah [6] inspected the usage of Laney p' control chart and the practice of test rules for evaluating governmental intercessions during the COVID-19 pandemic and understanding how specific actions and circumstances that happened affected the infection proportion. Laney ( 2002) has warned about the potential for overdispersion when employing the p-control chart, particularly for scenarios with large sample sizes, which could result in control limits too closely spaced.…”

A Study on the Laney p′ Control Chart with Parameters Estimated from Phase I Data: Performance Evaluation and Applications

“…Arafah [24] utilized the Laney p control chart to monitor the variations in the COVID-19 cases in Jordan. The study defined the infection rate (IR) as the number of confirmed cases divided by the number of polymerase chain reaction (PCR) tests conducted.…”