Detecting COVID-19 spreading phases with time series analysis

Yuanguo Kuang; Jingyao Zheng; Haiyan Song

doi:10.1117/12.2671650

21 March 2023 Detecting COVID-19 spreading phases with time series analysis

Yuanguo Kuang, Jingyao Zheng, Haiyan Song

Proceedings Volume 12609, International Conference on Computer Application and Information Security (ICCAIS 2022); 126091Z (2023) https://doi.org/10.1117/12.2671650
Event: International Conference on Computer Application and Information Security (ICCAIS 2022), 2022, ONLINE, ONLINE

Abstract

Piecewise linear regression (PLR) method is applied to study cumulative cases of COVID-19 evolving everyday in England up to 6th February 2022 just before travel restrictions are removed and people started not to get tested anymore in the UK and factors e.g. the lockdowns behind the spread COVID-19 are also investigated. It is clear that different periods exhibit distinct patterns depending on variants and government-imposed restriction. Therefore, the effectiveness of lockdown measures is evaluated by comparing the rate of increase after a certain period (delay effect of measures) and that of time before as well as how new variants take over as a dominant variant. In addition, autoregression function is studied to show strong effect of cases in the past on today’s cases since the disease is highly infectious. Most of work is carried out thorough python built-in libraries such as pandas for preprocessing data and matplotlib which allows us to gain more insight and better visualization into the real scenario. Visualization is conducted by Geoda showing the regional level of infections.

Citation Download Citation

Yuanguo Kuang, Jingyao Zheng, and Haiyan Song "Detecting COVID-19 spreading phases with time series analysis", Proc. SPIE 12609, International Conference on Computer Application and Information Security (ICCAIS 2022), 126091Z (21 March 2023); https://doi.org/10.1117/12.2671650

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