Towards Understanding the Dynamics of COVID-19: An Approach Based on Polynomial Regression with Adaptive Sliding Windows

Yuxuan Xiu and Wai Kin Victor Chan ()
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Yuxuan Xiu: Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University
Wai Kin Victor Chan: Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University

A chapter in AI and Analytics for Public Health, 2022, pp 89-100 from Springer

Abstract: Abstract Intuitively, the time series of each wave of COVID-19 can be partitioned into several stages with different dynamics, such as the early stage, the raising stage and the fading stage. However, it is still needed to define a mathematical standard to quantitatively distinguish one stage from another, instead of performing the segmentation subjectively. This paper adopts an approach based on polynomial regression with adaptive sliding windows to partition the COVID-19 time series into segments with different dynamics, which provides a mathematical standard to distinguish between stages. Experimental results on 15 representative countries demonstrate that the segmentation results are highly intuitively interpretable. Besides, significant similarities are observed between the same stages of different waves in different countries. In addition, some evidences suggest that the dynamics of the previous segment could provide information on the subsequent development of the epidemic.

Keywords: COVID-19; Dynamics; Polynomial regression; Complex network analysis (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-030-75166-1_4

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DOI: 10.1007/978-3-030-75166-1_4

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