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

In: AI and Analytics for Public Health

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yuxuan Xiu & Wai Kin Victor Chan, 2022. "Towards Understanding the Dynamics of COVID-19: An Approach Based on Polynomial Regression with Adaptive Sliding Windows," Springer Proceedings in Business and Economics, in: Hui Yang & Robin Qiu & Weiwei Chen (ed.), AI and Analytics for Public Health, pages 89-100, Springer.
    • Handle: RePEc:spr:prbchp:978-3-030-75166-1_4
    DOI: 10.1007/978-3-030-75166-1_4
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