Monitoring of the COVID-19 Cases by EWMA Control Chart

Kumari, Pulak; Priyadarshi, Anurag; Gupta, Amit Kumar; Prasad, Sanjeev Kumar

doi:10.1007/978-981-16-2597-8_46

Cited by 6 publications

(2 citation statements)

References 6 publications

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“…The majority of previous publications using control charts in an epidemiological setting has focused on infection control and hospital epidemiology, 26‐30 but there is less work addressing the timely detection of unusual patterns in public health data 31‐33 . With respect to coronavirus studies using statistical control charts, there are the following papers where quantitative variables are considered: daily COVID‐19 reported infections, 34‐37 daily COVID‐19 reported deaths, 34,36,38‐41 daily COVID‐19 reported recoveries, 36 every second week COVID‐19 reported death rate, 36 every second week COVID‐19 reported recovery rate; 36 mathematical models for the definition of an epidemiological curve are implemented (generally with three growth phases, that is, pre‐growth/growth/post‐growth, considering numbers of events 34,37,38,40,41 ) or of a symmetric logistic growth curve (mostly with three growth phases, that is, exponential growth/linear regime/exponential saturation, considering cumulative numbers of events 35,38 ); epidemic processes are monitored by means of control charts using the Shewhart process model: hybrid Shewhart chart as a combination of

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charts with a log‐regression slope, 34,37,41 adapted Shewhart chart with underlying moving average model, 38 EWMA chart with Shewhart control limits and various quantile functions as alert levels, 35 Gamma Shewhart chart with inner and outer control limits using generalized multiple dependent state (GMDS) sampling, 39 EWMA chart, 36

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chart and EWMA chart with Shewhart control limits; 40 epidemic processes are investigated for specific countries: Ireland, 34 Italy, 34,41 Spain, 41 UK, 34,41 California (USA), 34,37 Hong Kong (China), 35 Wuhan (China), 39 India, 36 Pakistan,…”

Section: Introductionmentioning

confidence: 99%

“…However, monitoring of epidemic processes in the above mentioned papers has limitations regarding the availability of accurate, consistent and reliable daily collected data; consideration of the numbers of infections/deaths/recoveries but not the rates (except for recovery and death rate in Reference 36); monitoring of comparatively short periods (on average one month) and exceptionally of longer periods (at most 16 months in Reference 38); unavoidable switching between various charts and re‐calculation of model parameters related to different epochs of an epidemiological curve with non‐stationary and stationary patterns 34,37,40,41 …”

Section: Introductionmentioning

confidence: 99%

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Monitoring epidemic processes under political measures

Chukhrova,

Plate,

Johannssen

2024

Statistics in Medicine

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Statistical modeling of epidemiological curves to capture the course of epidemic processes and to implement a signaling system for detecting significant changes in the process is a challenging task, especially when the process is affected by political measures. As previous monitoring approaches are subject to various problems, we develop a practical and flexible tool that is well suited for monitoring epidemic processes under political measures. This tool enables monitoring across different epochs using a single statistical model that constantly adapts to the underlying process, and therefore allows both retrospective and on‐line monitoring of epidemic processes. It is able to detect essential shifts and to identify anomaly conditions in the epidemic process, and it provides decision‐makers a reliable method for rapidly learning from trends in the epidemiological curves. Moreover, it is a tool to evaluate the effectivity of political measures and to detect the transition from pandemic to endemic. This research is based on a comprehensive COVID‐19 study on infection rates under political measures in line with the reporting of the Robert Koch Institute covering the entire period of the pandemic in Germany.

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