IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i13p4872-d381114.html
   My bibliography  Save this article

Cluster-Based Analysis of Infectious Disease Occurrences Using Tensor Decomposition: A Case Study of South Korea

Author

Listed:
  • Seungwon Jung

    (School of Electrical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea)

  • Jaeuk Moon

    (School of Electrical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea)

  • Eenjun Hwang

    (School of Electrical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea)

Abstract

For a long time, various epidemics, such as lower respiratory infections and diarrheal diseases, have caused serious social losses and costs. Various methods for analyzing infectious disease occurrences have been proposed for effective prevention and proactive response to reduce such losses and costs. However, the results of the occurrence analyses were limited because numerous factors affect the outbreak of infectious diseases and there are complex interactions between these factors. To alleviate this limitation, we propose a cluster-based analysis scheme of infectious disease occurrences that can discover commonalities or differences between clusters by grouping elements with similar occurrence patterns. To do this, we collect and preprocess infectious disease occurrence data according to time, region, and disease. Then, we construct a tensor for the data and apply Tucker decomposition to extract latent features in the dimensions of time, region, and disease. Based on these latent features, we conduct k-means clustering and analyze the results for each dimension. To demonstrate the effectiveness of this scheme, we conduct a case study on data from South Korea and report some of the results.

Suggested Citation

  • Seungwon Jung & Jaeuk Moon & Eenjun Hwang, 2020. "Cluster-Based Analysis of Infectious Disease Occurrences Using Tensor Decomposition: A Case Study of South Korea," IJERPH, MDPI, vol. 17(13), pages 1-19, July.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:13:p:4872-:d:381114
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/13/4872/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/13/4872/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yu Zhang & Jiayu Wu & Chunyao Zhou & Qingyu Zhang, 2019. "Installation Planning in Regional Thermal Power Industry for Emissions Reduction Based on an Emissions Inventory," IJERPH, MDPI, vol. 16(6), pages 1-13, March.
    2. Yordan P Raykov & Alexis Boukouvalas & Fahd Baig & Max A Little, 2016. "What to Do When K-Means Clustering Fails: A Simple yet Principled Alternative Algorithm," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-28, September.
    3. Nan Wang & Shanwu Sun & Dantong OuYang, 2018. "Business Process Modeling Abstraction Based on Semi-Supervised Clustering Analysis," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 60(6), pages 525-542, December.
    4. Lin Wang & Joseph T. Wu, 2018. "Characterizing the dynamics underlying global spread of epidemics," Nature Communications, Nature, vol. 9(1), pages 1-11, December.
    5. Amy Wesolowski & Elisabeth zu Erbach-Schoenberg & Andrew J. Tatem & Christopher Lourenço & Cecile Viboud & Vivek Charu & Nathan Eagle & Kenth Engø-Monsen & Taimur Qureshi & Caroline O. Buckee & C. J. , 2017. "Multinational patterns of seasonal asymmetry in human movement influence infectious disease dynamics," Nature Communications, Nature, vol. 8(1), pages 1-9, December.
    6. Xavier Rodó & Mercedes Pascual & Francisco Doblas-Reyes & Alexander Gershunov & Dáithí Stone & Filippo Giorgi & Peter Hudson & James Kinter & Miquel-Àngel Rodríguez-Arias & Nils Stenseth & David Alons, 2013. "Climate change and infectious diseases: Can we meet the needs for better prediction?," Climatic Change, Springer, vol. 118(3), pages 625-640, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Ning & Zhu, Xuzhen & Chen, Yanyan, 2019. "Information spreading on complex networks with general group distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 671-676.
    2. Xueli Wang & Moqin Zhou & Jinzhu Jia & Zhi Geng & Gexin Xiao, 2018. "A Bayesian Approach to Real-Time Monitoring and Forecasting of Chinese Foodborne Diseases," IJERPH, MDPI, vol. 15(8), pages 1-13, August.
    3. Yan Ma & Arvid Bring & Zahra Kalantari & Georgia Destouni, 2019. "Potential for Hydroclimatically Driven Shifts in Infectious Disease Outbreaks: The Case of Tularemia in High-Latitude Regions," IJERPH, MDPI, vol. 16(19), pages 1-11, October.
    4. Maksymilian Mądziel, 2024. "Modeling Exhaust Emissions in Older Vehicles in the Era of New Technologies," Energies, MDPI, vol. 17(19), pages 1-18, October.
    5. Zhang, Gui-Qing & Baró, Jordi & Cheng, Fang-Yin & Huang, He & Wang, Lin, 2019. "Avalanche dynamics of a generalized earthquake model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1463-1471.
    6. Shu, Panpan & Wang, Wei & Eugene Stanley, H. & Braunstein, Lidia A., 2018. "A general social contagion dynamic in interconnected lattices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 511(C), pages 272-279.
    7. Omer Ajmal & Shahzad Mumtaz & Humaira Arshad & Abdullah Soomro & Tariq Hussain & Razaz Waheeb Attar & Ahmed Alhomoud, 2024. "Enhanced Parameter Estimation of DENsity CLUstEring (DENCLUE) Using Differential Evolution," Mathematics, MDPI, vol. 12(17), pages 1-46, September.
    8. Lu, Peng & Nie, Shizhao, 2019. "The strength distribution and combined duration prediction of online collective actions: Big data analysis and BP neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    9. Joaquín Pérez-Ortega & Nelva Nely Almanza-Ortega & David Romero, 2018. "Balancing effort and benefit of K-means clustering algorithms in Big Data realms," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-19, September.
    10. Zhang, Shuhua & Zhang, Zhipeng & Wu, Yu’e & Yan, Ming & Xie, Yunya, 2018. "Tolerance-based punishment and cooperation in spatial public goods game," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 267-272.
    11. Tan, Daniel & Suvarna, Manu & Shee Tan, Yee & Li, Jie & Wang, Xiaonan, 2021. "A three-step machine learning framework for energy profiling, activity state prediction and production estimation in smart process manufacturing," Applied Energy, Elsevier, vol. 291(C).
    12. Pueyo, Salvador, 2020. "Jevons' paradox and a tax on aviation to prevent the next pandemic," SocArXiv vb5q3_v1, Center for Open Science.
    13. Sánchez, Allan G.S. & Posadas–Castillo, C. & Garza–González, E., 2021. "Determining efficiency of small-world algorithms: A comparative approach," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 187(C), pages 687-699.
    14. Mayra Z Rodriguez & Cesar H Comin & Dalcimar Casanova & Odemir M Bruno & Diego R Amancio & Luciano da F Costa & Francisco A Rodrigues, 2019. "Clustering algorithms: A comparative approach," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-34, January.
    15. Liu, Xiang-Chun & Zhu, Xu-Zhen & Tian, Hui & Zhang, Zeng-Ping & Wang, Wei, 2019. "Identifying localized influential spreaders of information spreading," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 92-97.
    16. Yan Ma & Guillaume Vigouroux & Zahra Kalantari & Romain Goldenberg & Georgia Destouni, 2020. "Implications of Projected Hydroclimatic Change for Tularemia Outbreaks in High-Risk Areas across Sweden," IJERPH, MDPI, vol. 17(18), pages 1-13, September.
    17. Deng, Zheng-Hong & Qiao, Hong-Hai & Song, Qun & Gao, Li, 2019. "A complex network community detection algorithm based on label propagation and fuzzy C-means," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 217-226.
    18. Wang, Qingqing & Du, Chunpeng & Geng, Yini & Shi, Lei, 2020. "Historical payoff can not overcome the vaccination dilemma on Barabási–Albert scale-free networks," Chaos, Solitons & Fractals, Elsevier, vol. 130(C).
    19. Richard Bluhm & Maxim Pinkovskiy, 2021. "The spread of COVID-19 and the BCG vaccine: A natural experiment in reunified Germany," The Econometrics Journal, Royal Economic Society, vol. 24(3), pages 353-376.
    20. Zou, Yang & Xiong, Zhongyang & Zhang, Pu & Wang, Wei, 2018. "Social contagions on multiplex networks with different reliability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 728-735.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:17:y:2020:i:13:p:4872-:d:381114. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.