EWMA Control Chart Integrated with Time Series Models for COVID-19 Surveillance
The global outbreak of coronavirus disease 2019 (COVID-19) has posed a severe threat to public health and caused widespread socioeconomic disruptions in the past several years. While the pandemic has subsided, it is essential to explore effective disease surveillance tools to aid in controlling futu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/1/115 |
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| author | Chen-Rui Hsu Hsiuying Wang |
| author_facet | Chen-Rui Hsu Hsiuying Wang |
| author_sort | Chen-Rui Hsu |
| collection | DOAJ |
| description | The global outbreak of coronavirus disease 2019 (COVID-19) has posed a severe threat to public health and caused widespread socioeconomic disruptions in the past several years. While the pandemic has subsided, it is essential to explore effective disease surveillance tools to aid in controlling future pandemics. Several studies have proposed methods to capture the epidemic trend and forecast new daily confirmed cases. In this study, we propose the use of exponentially weighted moving average (EWMA) control charts integrated with time series models to monitor the number of daily new confirmed cases of COVID-19. The conventional EWMA control chart directly monitors the number of daily new confirmed cases. The proposed methods, however, monitor the residuals of time series models fitted to these data. In this study, two time series models—the auto-regressive integrated moving average (ARIMA) model and the vector auto-regressive moving average (VARMA) model—are considered. The results are compared with those of the conventional EWMA control chart using three datasets from India, Malaysia, and Thailand. The findings demonstrate that the proposed method can detect disease outbreak signals earlier than conventional control charts. |
| format | Article |
| id | doaj-art-73e17e08399543c1887262a8d7d07fa9 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-73e17e08399543c1887262a8d7d07fa92025-01-10T13:18:17ZengMDPI AGMathematics2227-73902024-12-0113111510.3390/math13010115EWMA Control Chart Integrated with Time Series Models for COVID-19 SurveillanceChen-Rui Hsu0Hsiuying Wang1Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu 300093, TaiwanInstitute of Statistics, National Yang Ming Chiao Tung University, Hsinchu 300093, TaiwanThe global outbreak of coronavirus disease 2019 (COVID-19) has posed a severe threat to public health and caused widespread socioeconomic disruptions in the past several years. While the pandemic has subsided, it is essential to explore effective disease surveillance tools to aid in controlling future pandemics. Several studies have proposed methods to capture the epidemic trend and forecast new daily confirmed cases. In this study, we propose the use of exponentially weighted moving average (EWMA) control charts integrated with time series models to monitor the number of daily new confirmed cases of COVID-19. The conventional EWMA control chart directly monitors the number of daily new confirmed cases. The proposed methods, however, monitor the residuals of time series models fitted to these data. In this study, two time series models—the auto-regressive integrated moving average (ARIMA) model and the vector auto-regressive moving average (VARMA) model—are considered. The results are compared with those of the conventional EWMA control chart using three datasets from India, Malaysia, and Thailand. The findings demonstrate that the proposed method can detect disease outbreak signals earlier than conventional control charts.https://www.mdpi.com/2227-7390/13/1/115average run lengthCOVID-19control chartsresidualstime series model |
| spellingShingle | Chen-Rui Hsu Hsiuying Wang EWMA Control Chart Integrated with Time Series Models for COVID-19 Surveillance Mathematics average run length COVID-19 control charts residuals time series model |
| title | EWMA Control Chart Integrated with Time Series Models for COVID-19 Surveillance |
| title_full | EWMA Control Chart Integrated with Time Series Models for COVID-19 Surveillance |
| title_fullStr | EWMA Control Chart Integrated with Time Series Models for COVID-19 Surveillance |
| title_full_unstemmed | EWMA Control Chart Integrated with Time Series Models for COVID-19 Surveillance |
| title_short | EWMA Control Chart Integrated with Time Series Models for COVID-19 Surveillance |
| title_sort | ewma control chart integrated with time series models for covid 19 surveillance |
| topic | average run length COVID-19 control charts residuals time series model |
| url | https://www.mdpi.com/2227-7390/13/1/115 |
| work_keys_str_mv | AT chenruihsu ewmacontrolchartintegratedwithtimeseriesmodelsforcovid19surveillance AT hsiuyingwang ewmacontrolchartintegratedwithtimeseriesmodelsforcovid19surveillance |