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Nonlinear time series analysis of state-wise COVID-19 in Malaysia using wavelet and persistent homology

The nonlinear progression of COVID-19 positive cases, their fluctuations, the correlations in amplitudes and phases across different regions, along with seasonality or periodicity, pose challenges to thoroughly examining the data for revealing similarities or detecting anomalous trajectories. To add...

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Main Authors: Pang, Piau, Carey Ling, Yu Fan, Liew, Siaw Hong, Fatimah, Abdul Razak, Benchawan, Wiwatanapataphee
Format: Article
Language:English
Published: Springer Nature Limited 2024
Subjects:
Q Science (General)
Online Access:http://ir.unimas.my/id/eprint/46596/1/%282024%20Phang%20et%20al%29%20Sci%20Reps%20WoS%20Q1%20IF%203_8.pdf
http://ir.unimas.my/id/eprint/46596/
https://www.nature.com/articles/s41598-024-79002-0
https://doi.org/10.1038/s41598-024-79002-0
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id my.unimas.ir-46596
record_format eprints
spelling my.unimas.ir-465962024-11-12T06:59:47Z http://ir.unimas.my/id/eprint/46596/ Nonlinear time series analysis of state-wise COVID-19 in Malaysia using wavelet and persistent homology Pang, Piau Carey Ling, Yu Fan Liew, Siaw Hong Fatimah, Abdul Razak Benchawan, Wiwatanapataphee Q Science (General) The nonlinear progression of COVID-19 positive cases, their fluctuations, the correlations in amplitudes and phases across different regions, along with seasonality or periodicity, pose challenges to thoroughly examining the data for revealing similarities or detecting anomalous trajectories. To address this, we conducted a nonlinear time series analysis combining wavelet and persistent homology to detect the qualitative properties underlying COVID-19 daily infection numbers at the state level from the pandemic’s onset to June 2024 in Malaysia. The first phase involved investigating the evolution of daily confirmed cases by state in the time-frequency domain using wavelets. Subsequently, a topological feature-based time series clustering is performed by reconstructing a higher-dimensional phase space through a delay embedding method. Our findings reveal a prominent 7-day periodicity in case numbers from mid-2021 to the end of 2022. The state-wise daily cases are moderately correlated in both amplitudes and phases during the Delta and Omicron waves. Biweekly averaged data significantly enhances the detection of topological loops associated with these waves. Selangor demonstrates unique case trajectories, while Pahang shows the highest similarity with other states. This methodological framework provides a more detailed understanding of epidemiological time series data, offering valuable insights for preparing for future public health crises. Springer Nature Limited 2024-11-11 Article PeerReviewed text en http://ir.unimas.my/id/eprint/46596/1/%282024%20Phang%20et%20al%29%20Sci%20Reps%20WoS%20Q1%20IF%203_8.pdf Pang, Piau and Carey Ling, Yu Fan and Liew, Siaw Hong and Fatimah, Abdul Razak and Benchawan, Wiwatanapataphee (2024) Nonlinear time series analysis of state-wise COVID-19 in Malaysia using wavelet and persistent homology. Scientific Reports, 14 (27562). pp. 1-13. ISSN 2045-2322 https://www.nature.com/articles/s41598-024-79002-0 https://doi.org/10.1038/s41598-024-79002-0
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic Q Science (General)
spellingShingle Q Science (General)
Pang, Piau
Carey Ling, Yu Fan
Liew, Siaw Hong
Fatimah, Abdul Razak
Benchawan, Wiwatanapataphee
Nonlinear time series analysis of state-wise COVID-19 in Malaysia using wavelet and persistent homology
description The nonlinear progression of COVID-19 positive cases, their fluctuations, the correlations in amplitudes and phases across different regions, along with seasonality or periodicity, pose challenges to thoroughly examining the data for revealing similarities or detecting anomalous trajectories. To address this, we conducted a nonlinear time series analysis combining wavelet and persistent homology to detect the qualitative properties underlying COVID-19 daily infection numbers at the state level from the pandemic’s onset to June 2024 in Malaysia. The first phase involved investigating the evolution of daily confirmed cases by state in the time-frequency domain using wavelets. Subsequently, a topological feature-based time series clustering is performed by reconstructing a higher-dimensional phase space through a delay embedding method. Our findings reveal a prominent 7-day periodicity in case numbers from mid-2021 to the end of 2022. The state-wise daily cases are moderately correlated in both amplitudes and phases during the Delta and Omicron waves. Biweekly averaged data significantly enhances the detection of topological loops associated with these waves. Selangor demonstrates unique case trajectories, while Pahang shows the highest similarity with other states. This methodological framework provides a more detailed understanding of epidemiological time series data, offering valuable insights for preparing for future public health crises.
format Article
author Pang, Piau
Carey Ling, Yu Fan
Liew, Siaw Hong
Fatimah, Abdul Razak
Benchawan, Wiwatanapataphee
author_facet Pang, Piau
Carey Ling, Yu Fan
Liew, Siaw Hong
Fatimah, Abdul Razak
Benchawan, Wiwatanapataphee
author_sort Pang, Piau
title Nonlinear time series analysis of state-wise COVID-19 in Malaysia using wavelet and persistent homology
title_short Nonlinear time series analysis of state-wise COVID-19 in Malaysia using wavelet and persistent homology
title_full Nonlinear time series analysis of state-wise COVID-19 in Malaysia using wavelet and persistent homology
title_fullStr Nonlinear time series analysis of state-wise COVID-19 in Malaysia using wavelet and persistent homology
title_full_unstemmed Nonlinear time series analysis of state-wise COVID-19 in Malaysia using wavelet and persistent homology
title_sort nonlinear time series analysis of state-wise covid-19 in malaysia using wavelet and persistent homology
publisher Springer Nature Limited
publishDate 2024
url http://ir.unimas.my/id/eprint/46596/1/%282024%20Phang%20et%20al%29%20Sci%20Reps%20WoS%20Q1%20IF%203_8.pdf
http://ir.unimas.my/id/eprint/46596/
https://www.nature.com/articles/s41598-024-79002-0
https://doi.org/10.1038/s41598-024-79002-0
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score 13.223943

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