CHANGEPOINTS DETECTION OF PANDEMIC WAVE IN REAL-TIME: APPLICATIONS TO THE TRANSMISSION OF COVID-19

Authors

  • Faihatuz Zuhairoh STKIP YPUP Makassar
  • Muhammad Rais Ridwan STKIP YPUP Makassar

DOI:

https://doi.org/10.25077/jmua.13.4.230-243.2024

Keywords:

Changepoint detection method, multiple wave pandemic, Richards model

Abstract

The COVID-19 pandemic has spread throughout the world. Most countries experienced the pandemic in multiple waves. The Richards model predicts when a pandemic will peak and end in a particular area. However, this model can only be used in the single-wave case. The research aims to identify a changepoint detection method capable of delineating pandemic wave boundaries, thus enabling the resolution of multiple wave cases using the Richards model. This article uses two methods to detect changepoints: the Pruned Exact Linear Time (PELT) and the interpolation method. PELT method determines the changepoint based on changes in the statistical properties of the sequence of observations which can be in the form of differences in the mean or variance of each set of observations. In contrast, the linear interpolation method determines the changepoint based on the slope of a data pattern. The two methods complement each other, where the interpolation method is used to determine whether the pandemic is still in a single wave or has multiple waves, followed by determining wave boundaries using the PELT method. Richards model parameter estimation is carried out after the wave boundaries are obtained, and initial data is taken from the last wave using the PELT method. The prediction results show the peak of the pandemic in a particular region and when it will end, which can be used to inform medium-term strategies for the government to overcome the ongoing pandemic. This information helps prevent a resurgence of infections, which would negatively affect the COVID-19 mortality rate and the area's economic situation.

Downloads

Download data is not yet available.

References

D. L. Roberts, J. S. Rossman, and I. Jarić, “Dating first cases of COVID-19,†PLoS Pathogens, vol. 17, no. 6, p. e1009620, 2021, doi: 10.1371/journal.ppat.1009620.

R. Pung et al., “Investigation of three clusters of COVID-19 in Singapore: implications for surveillance and response measures,†The Lancet, vol. 395, no. 10229, pp. 1039–1046, 2020, doi: 10.1016/S0140-6736(20)30528-6.

U. Kollamparambil and A. Oyenubi, “Behavioural response to the Covid-19 pandemic in South Africa,†PLoS One, vol. 16, no. 4, p. e0250269, 2021, doi: 10.1371/journal.pone.0250269.

K. Meier et al., “Public perspectives on protective measures during the COVID-19 pandemic in the Netherlands, Germany and Italy: A survey study,†PLoS One, vol. 15, no. 8, p. e0236917, 2020, doi: 10.1371/journal.pone.0236917.

Z. Y. He, A. Abbes, H. Jahanshahi, N. D. Alotaibi, and Y. Wang, “Fractional-Order Discrete-Time SIR Epidemic Model with Vaccination: Chaos and Complexity,†Mathematics, vol. 10, no. 2, 2022, doi: 10.3390/math10020165.

X. Luo, H. Duan, and K. Xu, “A novel grey model based on traditional Richards model and its application in COVID-19,†Chaos, Solitons, and Fractals, vol. 142, 2021, doi: 10.1016/j.chaos.2020.110480.

A. E. Laher et al., “Development and internal validation of the HIV In-hospital Mortality Prediction (HIV-IMP) risk score,†HIV Medicine, vol. 23, no. 1, 2022, doi: 10.1111/hiv.13159.

Y. H. Hsieh, “Dengue outbreaks in Taiwan, 1998-2017: Importation, serotype and temporal pattern,†Asian Pacific Journal of Tropical Medicine, vol. 11, no. 8. 2018. doi: 10.4103/1995-7645.240081.

E. Shim, A. Tariq, W. Choi, Y. Lee, and G. Chowell, “Transmission potential and severity of COVID-19 in South Korea,†International Journal of Infectious Diseases, vol. 93, 2020, doi: 10.1016/j.ijid.2020.03.031.

M. Aghaali, G. Kolifarhood, R. Nikbakht, H. M. Saadati, and S. S. Hashemi Nazari, “Estimation of the serial interval and basic reproduction number of COVID-19 in Qom, Iran, and three other countries: A data-driven analysis in the early phase of the outbreak,†Transboundary and Emerging Diseases, vol. 67, no. 6, 2020, doi: 10.1111/tbed.13656.

R. Nikbakht, M. R. Baneshi, and A. Bahrampour, “Estimation of the basic reproduction number and vaccination coverage of influenza in the United States (2017-18),†Journal of Research in Health Sciences, vol. 18, no. 4, pp. 1–6, 2018, doi: 10.4103/jrms.JRMS_888_18.

X. Shi, C. Gallagher, R. Lund, and R. Killick, “A comparison of single and multiple changepoint techniques for time series data,†Computational Statistics and Data Analysis, vol. 170, 2022, doi: 10.1016/j.csda.2022.107433.

R. Baranowski, Y. Chen, and P. Fryzlewicz, “Narrowest over threshold detection of multiple changepoints and changepoint like features,†Journal of the Royal Statistical Society. Series B: Statistical Methodology, vol. 81, no. 3, 2019, doi: 10.1111/rssb.12322.

L. Ma, A. J. Grant, and G. Sofronov, “Multiple changepoint detection and validation in autoregressive time series data,†Statistical Papers, vol. 61, no. 4. pp. 1507–1528, 2020. doi: 10.1007/s00362-020-01198-w.

F. Zuhairoh and D. Rosadi, "Real-time forecasting of the COVID-19 epidemic using the richards model in South Sulawesi, Indonesia," Indonesian Journal of Science and Technology, vol. 5, no. 3, pp. 456–462, 2020, doi: 10.17509/ijost.v5i3.26139.

F. Zuhairoh and D. Rosadi, "Data-driven Analysis and Prediction of COVID-19 Infection in Southeast Asia using A Phenomenological Model," Pakistan Journal of Statistics and Operation Research, vol. 18, no. 1, pp. 59–69, 2022, doi: 10.18187/pjsor.v18i1.3714.

F. Zuhairoh and D. Rosadi, "Real-time prediction for multi-wave COVID-19 outbreaks," Communications for Statistical Applications and Methods, vol. 29, no. 5, pp. 499–512, 2022, doi: 10.29220/CSAM.2022.29.5.499.

F. Zuhairoh, D. Rosadi, and A. R. Effendie, "Prediction of Disease Outbreaks with the SIR Model and Richards Model in Multi-wave Cases," in AIP Conference Proceedings, American Institute of Physics Inc., Dec. 2023, pp. 1–8. doi: 10.1063/5.0181069.

P. Fearnhead and G. Rigaill, "Changepoint Detection in the Presence of Outliers," Journal of the American Statistical Association, vol. 114, no. 525, 2019, doi: 10.1080/01621459.2017.1385466.

S. Wang and F. Fan, "Analysis of the response of long-term vegetation dynamics to climate variability using the pruned exact linear time (Pelt) method and disturbance lag model (dlm) based on remote sensing data: A case study in guangdong province (china)," Remote Sensing, vol. 13, no. 10, 2021, doi: 10.3390/rs13101873.

M. Aqil, A. Jbari, and A. Bourouhou, "Comparison of ECG Baseline Wander Removal Techniques and Improvement Based on Moving Average of Wavelet Approximation Coefficients," International Journal Bioautomation, vol. 25, no. 2, 2021, doi: 10.7546/ijba.2021.25.2.000770.

M. Maleki, M. R. Mahmoudi, D. Wraith, and K. H. Pho, "Time series modelling to forecast the confirmed and recovered cases of COVID-19," Travel Medicine and Infectious Disease, vol. 37, p. 101742, 2020, doi: 10.1016/j.tmaid.2020.101742.

K. Roosa et al., vShort-term Forecasts of the COVID-19 Epidemic in Guangdong and Zhejiang, China: February 13–23, 2020," Journal of Clinical Medicine, vol. 9, no. 2, pp. 596–604, 2020, doi: 10.3390/jcm9020596.

U. Avila-Ponce de León, Ã. G. C. Pérez, and E. Avila-Vales, "An SEIARD epidemic model for COVID-19 in Mexico: Mathematical analysis and state-level forecast," Chaos, Solitons, and Fractals, vol. 140, p. 110165, Nov. 2020, doi: 10.1016/j.chaos.2020.110165.

K. C. Santosh, "COVID-19 Prediction Models and Unexploited Data," Journal of Medical Systems, vol. 44, no. 170, pp. 1–4, 2020, doi: 10.1007/s10916-020-01645-z.

Downloads

Additional Files

Published

31-10-2024

How to Cite

Zuhairoh, F., & Ridwan, M. R. (2024). CHANGEPOINTS DETECTION OF PANDEMIC WAVE IN REAL-TIME: APPLICATIONS TO THE TRANSMISSION OF COVID-19. Jurnal Matematika UNAND, 13(4), 230–243. https://doi.org/10.25077/jmua.13.4.230-243.2024

Issue

Vol. 13 No. 4 (2024)

Section

Articles

License

All articles published in Jurnal Matematika UNAND (JMUA) are open access and licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). This license allows published content to be freely accessed, shared, redistributed, and adapted for any purpose, including commercial use, provided that the applicable license conditions are followed.

Copyright Holder

The copyright of articles published in Jurnal Matematika UNAND is held by the Departemen Matematika dan Sains Data, Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA), Universitas Andalas (UNAND), in accordance with the journal's stated copyright policy.

Author Rights

Under the CC BY-SA 4.0 license, authors may share, distribute, and reuse their published articles, subject to the applicable license conditions. These rights include:

  • Self-Archiving: Authors may deposit their published articles in institutional repositories, personal websites, or academic platforms.

  • Reuse: Authors may reuse their published work in subsequent publications, research, teaching materials, or other lawful purposes, provided that appropriate attribution is maintained.

  • Distribution: Authors may share and distribute their published articles without requiring additional permission under the terms of the license.

Creative Commons License (CC BY-SA 4.0)

Under the CC BY-SA 4.0 license, users are free to:

  • Share: Copy and redistribute the material in any medium or format.

  • Adapt: Remix, transform, and build upon the material for any purpose, including commercial purposes.

These permissions are subject to the following conditions:

  • Attribution: Users must give appropriate credit to the original author(s), retain relevant copyright notices, provide a link to the license, and indicate if changes were made. Attribution must not imply endorsement by the author(s), copyright holder, or journal.

  • ShareAlike: If users remix, transform, or build upon the material, they must distribute their contributions under the same CC BY-SA 4.0 license or a compatible license.

  • No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from exercising the rights granted by the license.

For more information about the CC BY-SA 4.0 license, please visit:

https://creativecommons.org/licenses/by-sa/4.0/

Third-Party Content

Third-party materials, including figures, tables, photographs, and illustrations, are covered by the article's CC BY-SA 4.0 license only when their inclusion under that license is authorized.

Materials excluded from the license must be clearly identified with appropriate copyright notices and attribution. Authors are responsible for obtaining any necessary permissions, while users must comply with the applicable licensing terms or permissions when reusing excluded materials.

Copyright and Licensing Information

Copyright and licensing information should be clearly displayed on each published article's landing page and included in the full-text versions (HTML and PDF), where applicable, to ensure transparency regarding copyright ownership and permitted reuse.