skip to main content
10.1145/3605423.3605458acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicctaConference Proceedingsconference-collections
research-article
Open access

Forecasting the Spread of COVID-19 in Asia: A Clustering and Seasonal Analysis

Published: 20 August 2023 Publication History

Abstract

The objective of this research is to analyze COVID-19 outbreak data in Asia using k-means and Seasonal Patterns techniques. The data was gathered from various organizations worldwide, available on GitHub from February 24, 2020, to May 16, 2022. The population density in Asia was clustered into Clusters_0 (high density), Clusters_1 (low density), and Clusters_2 (medium density) using k-means clustering. Moreover, forecasting was performed using the seasonal variation technique to create a map of the COVID-19 spread in Asia. Based on the available actual data, the spread of COVID-19 is expected to exhibit a decreasing trend in transmission over the next 30 days with a total margin of reliability rate of 81.21%. This research can be useful for policymakers, organizations, and public health officials to make informed decisions about future COVID-19 response plans.

References

[1]
Anastasiya Doroshenko. 2020. Analysis of the Distribution of COVID-19 in Italy Using Clustering Algorithms. In Proceedings of the 2020 IEEE third International Conference on Data Stream Mining & Processing (DSMP), IEEE, Lviv, Ukraine, 325-328. https://doi.org/10.1109/DSMP47368.2020.9204202
[2]
R.A. Indraputra and Rina Fitriana. 2020. K-means Clustering Data COVID-19. J. Jurnal Teknik Industri 10, 3 (November 2020), 275-282. https://doi.org/10.25105/jti.v10i3.8428
[3]
Guanjin Wang and Stephen Wai Hang Kwok. 2021. Using K-means Clustering Method with Doc2Vec to Understand the Twitter Users’ Opinions on COVID-19 Vaccination. In Proceedings of the 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), IEEE, Athens, Greece, 1-4. https://doi.org/10.1109/BHI50953.2021.9508578
[4]
Patrick GT Walker, 2020. The impact of COVID-19 and strategies for mitigation and suppression in low- and middle-income countries. J. Science 369, 6502, 413-422. https://doi.org/10.1126/science.abc00
[5]
Vukašin Crnogorac, Milana Grbić, Marko Đukanović, and Dragan Matić. 2021. Clustering of European countries and territories based on cumulative relative number of COVID 19 patients in 2020. In Proceedings of the 20th International Symposium INFOTEH-JAHORINA (INFOTEH), IEEE, East Sarajevo, Bosnia and Herzegovina, 1-6. https://doi.org/10.1109/INFOTEH51037.2021.9400670
[6]
Douglas Steinley. 2006. K‐means clustering: a half‐century synthesis. British Journal of Mathematical and Statistical Psychology 59, 1, 1-34. https://doi.org/10.1348/000711005X48266
[7]
Rui Máximo Esteves, Thomas Hacker, and Chunming Rong. 2013. Competitive K-means, a New Accurate and Distributed K-means Algorithm for Large Datasets. In Proceedings of the 2013 IEEE 5th International Conference on Cloud Computing Technology and Science, IEEE, Bristol, UK, 17-24. https://doi.org/10.1109/CloudCom.2013.89
[8]
Xv Lan, Qian Li and Yi Zheng. 2015. Density K-means: A new algorithm for centers initialization for K-means. In Proceedings of the 2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS), IEEE, Beijing, China, 958-961. https://doi.org/10.1109/ICSESS.2015.7339213
[9]
Jan G. De Gooijer and Rob J. Hyndman. 2006. 25 years of time series forecasting. International journal of forecasting 22, 3, 443-473. https://doi.org/10.1016/j.ijforecast.2006.01.001
[10]
Ma Wen. 2016. Time Series Analysis of Receipt of Fire Alarms Based on Seasonal Adjustment Method. In Proceedings of the 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), IEEE, Hangzhou, China, 81-84. https://doi.org/10.1109/IHMSC.2016.208
[11]
Rob J. Hyndman. 2004. The interaction between trend and seasonality. International Journal of Forecasting 20, 4, 561-563, 2004. https://doi.org/10.1016/j.ijforecast.2004.03.005
[12]
Doan Ngoc Bao, Ngo Duy Khanh Vy, and Duong Tuan Anh. 2019. A hybrid method for forecasting trend and seasonal time series. In Proceedings of the 2013 RIVF International Conference on Computing & Communication Technologies - Research, Innovation, and Vision for Future (RIVF), IEEE, Hanoi, Vietnam, 203-208. https://doi.org/10.1109/RIVF.2013.6719894
[13]
Nitinai Rungjindarat and Sarun Thatsakaniwet. 2018. Time Series Forecasting with Classical Decomposition Method: Jasmine Rice Exportation of Thailand. Dusit Thani College Journal 13, 2 (May – August 2019), 283-293.
[14]
The Johns Hopkins Coronavirus Resource Center. 2019. COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. Retrieved May 27, 2019, from https://github.com/CSSEGISandData/COVID-19

Index Terms

  1. Forecasting the Spread of COVID-19 in Asia: A Clustering and Seasonal Analysis

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    ICCTA '23: Proceedings of the 2023 9th International Conference on Computer Technology Applications
    May 2023
    270 pages
    ISBN:9781450399579
    DOI:10.1145/3605423
    Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 20 August 2023

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. COVID-19
    2. Forecast
    3. K-Means
    4. Seasonal Patterns

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    ICCTA 2023

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 121
      Total Downloads
    • Downloads (Last 12 months)95
    • Downloads (Last 6 weeks)25
    Reflects downloads up to 18 Feb 2025

    Other Metrics

    Citations

    View Options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Login options

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media