Modeling and Analysis of Monkeypox Outbreak Using a New Time Series Ensemble Technique
The coronavirus pandemic has raised concerns about the emergence of other viral infections, such as monkeypox, which has become a significant hazard to public health. Thus, this work proposes a novel time series ensemble technique for analyzing and forecasting the spread of monkeypox in the four hig...
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| Formato: | Artículo |
| Lenguaje: | inglés |
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2024
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| _version_ | 1881508904606629888 |
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| author | Meza Cuba, Wilfredo Huaman Alfaro, Juan Carlos Iftikhar, Hasnain López Gonzales, Javier Linkolk |
| author_browse | Huaman Alfaro, Juan Carlos Iftikhar, Hasnain López Gonzales, Javier Linkolk Meza Cuba, Wilfredo |
| author_facet | Meza Cuba, Wilfredo Huaman Alfaro, Juan Carlos Iftikhar, Hasnain López Gonzales, Javier Linkolk |
| author_sort | Meza Cuba, Wilfredo |
| collection | Producción científica |
| description | The coronavirus pandemic has raised concerns about the emergence of other viral infections, such as monkeypox, which has become a significant hazard to public health. Thus, this work proposes a novel time series ensemble technique for analyzing and forecasting the spread of monkeypox in the four highly infected countries with the monkeypox virus. This approach involved processing the first cumulative confirmed case time series to address variance stabilization, normalization, stationarity, and a nonlinear secular trend component. After that, five single time series models and three proposed ensemble models are used to estimate the filtered confirmed case time series. The accuracy of the models is evaluated using typical accuracy mean errors, graphical evaluation, and an equal forecasting accuracy statistical test. Based on the results, it is found that the proposed time series ensemble forecasting approach is an efficient and accurate way to forecast the cumulative confirmed cases for the top four countries in the world and the entire world. Using the best ensemble model, a forecast is made for the next 28 days (four weeks), which will help understand the spread of the disease and the associated risks. This information can prevent further spread and enable timely and effective treatment. Furthermore, the developed novel time series ensemble approach can be used to forecast other diseases in the future. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/414 |
| institution | UPeU |
| language | English |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/4142026-10-30T16:59:54ZModeling and Analysis of Monkeypox Outbreak Using a New Time Series Ensemble TechniqueMeza Cuba, WilfredoHuaman Alfaro, Juan CarlosIftikhar, HasnainLópez Gonzales, Javier Linkolkmonkeypox outbreak tracinginfectious diseasestatistical modelingtime series modelsensemble modelshealth statisticsThe coronavirus pandemic has raised concerns about the emergence of other viral infections, such as monkeypox, which has become a significant hazard to public health. Thus, this work proposes a novel time series ensemble technique for analyzing and forecasting the spread of monkeypox in the four highly infected countries with the monkeypox virus. This approach involved processing the first cumulative confirmed case time series to address variance stabilization, normalization, stationarity, and a nonlinear secular trend component. After that, five single time series models and three proposed ensemble models are used to estimate the filtered confirmed case time series. The accuracy of the models is evaluated using typical accuracy mean errors, graphical evaluation, and an equal forecasting accuracy statistical test. Based on the results, it is found that the proposed time series ensemble forecasting approach is an efficient and accurate way to forecast the cumulative confirmed cases for the top four countries in the world and the entire world. Using the best ensemble model, a forecast is made for the next 28 days (four weeks), which will help understand the spread of the disease and the associated risks. This information can prevent further spread and enable timely and effective treatment. Furthermore, the developed novel time series ensemble approach can be used to forecast other diseases in the future.Articlehttps://www.mdpi.com/2075-1680/13/8/554/pdf?version=1723628048https://doi.org/10.3390/axioms1308055410.3390/axioms13080554https://hdl.handle.net/20.500.15037/414W4401583152enAxioms2075-16802024-08-142026-06-13T08:13:03Z2026-06-13T08:13:03Z |
| spellingShingle | monkeypox outbreak tracing infectious disease statistical modeling time series models ensemble models health statistics Meza Cuba, Wilfredo Huaman Alfaro, Juan Carlos Iftikhar, Hasnain López Gonzales, Javier Linkolk Modeling and Analysis of Monkeypox Outbreak Using a New Time Series Ensemble Technique |
| title | Modeling and Analysis of Monkeypox Outbreak Using a New Time Series Ensemble Technique |
| title_full | Modeling and Analysis of Monkeypox Outbreak Using a New Time Series Ensemble Technique |
| title_fullStr | Modeling and Analysis of Monkeypox Outbreak Using a New Time Series Ensemble Technique |
| title_full_unstemmed | Modeling and Analysis of Monkeypox Outbreak Using a New Time Series Ensemble Technique |
| title_short | Modeling and Analysis of Monkeypox Outbreak Using a New Time Series Ensemble Technique |
| title_sort | modeling and analysis of monkeypox outbreak using a new time series ensemble technique |
| topic | monkeypox outbreak tracing infectious disease statistical modeling time series models ensemble models health statistics |
| topic_facet | monkeypox outbreak tracing infectious disease statistical modeling time series models ensemble models health statistics |
| url | https://www.mdpi.com/2075-1680/13/8/554/pdf?version=1723628048 https://doi.org/10.3390/axioms13080554 https://hdl.handle.net/20.500.15037/414 |
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