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arXiv · 2404.00866

Nonlinear trend of COVID-19 infection time series

Fumihiko Ishiyama
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Abstract

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.

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BibTeXRIS

Fumihiko Ishiyama. 2024-04-01. Nonlinear trend of COVID-19 infection time series. https://doi.org/10.1587/nolta.14.165

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