Nonlinear trend of COVID-19 infection time series
Fuente:
arXiv
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916539345666048 |
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| author | Ishiyama, Fumihiko |
| author_facet | Ishiyama, Fumihiko |
| contents | We have developed a nonlinear method of time series analysis that allows us to obtain multiple nonlinear trends without harmonics from a given set of numerical data. We propose to apply the method to recognize the ongoing status of COVID-19 infection with an analytical equation for nonlinear trends. We found that there is only a single nonlinear trend, and this result justifies the use of a week-based infection growth rate. In addition, the fit with the obtained analytical equation for the nonlinear trend holds for a duration of more than three months for the Delta variant infection time series. The fitting also visualizes the transition to the Omicron variant. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_00866 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Nonlinear trend of COVID-19 infection time series Ishiyama, Fumihiko Physics and Society Numerical Analysis Populations and Evolution We have developed a nonlinear method of time series analysis that allows us to obtain multiple nonlinear trends without harmonics from a given set of numerical data. We propose to apply the method to recognize the ongoing status of COVID-19 infection with an analytical equation for nonlinear trends. We found that there is only a single nonlinear trend, and this result justifies the use of a week-based infection growth rate. In addition, the fit with the obtained analytical equation for the nonlinear trend holds for a duration of more than three months for the Delta variant infection time series. The fitting also visualizes the transition to the Omicron variant. |
| title | Nonlinear trend of COVID-19 infection time series |
| topic | Physics and Society Numerical Analysis Populations and Evolution |
| url | https://arxiv.org/abs/2404.00866 |