IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i5p2461-d509239.html
   My bibliography  Save this article

Using Social Network Analysis to Identify Spatiotemporal Spread Patterns of COVID-19 around the World: Online Dashboard Development

Author

Listed:
  • Kyent-Yon Yie

    (Department of Gastrointestinal Hepatobiliary, Chi Mei Jiali Hospital, Tainan 700, Taiwan)

  • Tsair-Wei Chien

    (Department of Medical Research, Chi-Mei Hospital, Tainan 700, Taiwan)

  • Yu-Tsen Yeh

    (Medical School, St. George’s University of London, London SW17 0RE, UK)

  • Willy Chou

    (Department of Physical Medicine and Rehabilitation, Chi Mei Medical Center, Tainan 700, Taiwan)

  • Shih-Bin Su

    (Department of Occupational Medicine, Chi Mei Medical Center, Tainan 700, Taiwan)

Abstract

The COVID-19 pandemic has spread widely around the world. Many mathematical models have been proposed to investigate the inflection point (IP) and the spread pattern of COVID-19. However, no researchers have applied social network analysis (SNA) to cluster their characteristics. We aimed to illustrate the use of SNA to identify the spread clusters of COVID-19. Cumulative numbers of infected cases (CNICs) in countries/regions were downloaded from GitHub. The CNIC patterns were extracted from SNA based on CNICs between countries/regions. The item response model (IRT) was applied to create a general predictive model for each country/region. The IP days were obtained from the IRT model. The location parameters in continents, China, and the United States were compared. The results showed that (1) three clusters (255, n = 51, 130, and 74 in patterns from Eastern Asia and Europe to America) were separated using SNA, (2) China had a shorter mean IP and smaller mean location parameter than other counterparts, and (3) an online dashboard was used to display the clusters along with IP days for each country/region. Spatiotemporal spread patterns can be clustered using SNA and correlation coefficients (CCs). A dashboard with spread clusters and IP days is recommended to epidemiologists and researchers and is not limited to the COVID-19 pandemic.

Suggested Citation

  • Kyent-Yon Yie & Tsair-Wei Chien & Yu-Tsen Yeh & Willy Chou & Shih-Bin Su, 2021. "Using Social Network Analysis to Identify Spatiotemporal Spread Patterns of COVID-19 around the World: Online Dashboard Development," IJERPH, MDPI, vol. 18(5), pages 1-15, March.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:5:p:2461-:d:509239
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/5/2461/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/5/2461/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lin-Yen Wang & Tsair-Wei Chien & Willy Chou, 2021. "Using the IPcase Index with Inflection Points and the Corresponding Case Numbers to Identify the Impact Hit by COVID-19 in China: An Observation Study," IJERPH, MDPI, vol. 18(4), pages 1-16, February.
    2. Huiqin Chen & Lei Chen, 2017. "Support Vector Machine Classification of Drunk Driving Behaviour," IJERPH, MDPI, vol. 14(1), pages 1-14, January.
    3. Thomas Warm, 1989. "Weighted likelihood estimation of ability in item response theory," Psychometrika, Springer;The Psychometric Society, vol. 54(3), pages 427-450, September.
    4. Jinling Quan, 2019. "Multi-Temporal Effects of Urban Forms and Functions on Urban Heat Islands Based on Local Climate Zone Classification," IJERPH, MDPI, vol. 16(12), pages 1-35, June.
    5. JinSoo Park & Sungroul Kim, 2020. "Machine Learning-Based Activity Pattern Classification Using Personal PM 2.5 Exposure Information," IJERPH, MDPI, vol. 17(18), pages 1-11, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Po-Hsin Chou & Tsair-Wei Chien & Ting-Ya Yang & Yu-Tsen Yeh & Willy Chou & Chao-Hung Yeh, 2021. "Predicting Active NBA Players Most Likely to Be Inducted into the Basketball Hall of Famers Using Artificial Neural Networks in Microsoft Excel: Development and Usability Study," IJERPH, MDPI, vol. 18(8), pages 1-18, April.
    2. Po-Hsin Chou & Jui-Chung John Lin & Tsair-Wei Chien, 2023. "Using text mining and forest plots to identify similarities and differences between two spine-related journals based on medical subject headings (MeSH terms) and author-specified keywords in 100 top-c," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 1-17, January.
    3. Zhangbo Yang & Jiahao Zhang & Shanxing Gao & Hui Wang, 2022. "Complex Contact Network of Patients at the Beginning of an Epidemic Outbreak: An Analysis Based on 1218 COVID-19 Cases in China," IJERPH, MDPI, vol. 19(2), pages 1-17, January.
    4. Ekaterina Ignatenko & Manuel Ribeiro & Mónica D. Oliveira, 2022. "Informing the Design of Data Visualization Tools to Monitor the COVID-19 Pandemic in Portugal: A Web-Delphi Participatory Approach," IJERPH, MDPI, vol. 19(17), pages 1-18, September.

    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. repec:zbw:rwidps:0002 is not listed on IDEAS
    2. Dahmann, Sarah C., 2017. "How does education improve cognitive skills? Instructional time versus timing of instruction," Labour Economics, Elsevier, vol. 47(C), pages 35-47.
    3. Torberg Falch & Justina AV Fischer, 2008. "Does a generous welfare state crowd out student achievement? Panel data evidence from international student tests," TWI Research Paper Series 31, Thurgauer Wirtschaftsinstitut, Universität Konstanz.
    4. Steger, Diana & Schroeders, Ulrich & Wilhelm, Oliver, 2019. "On the dimensionality of crystallized intelligence: A smartphone-based assessment," Intelligence, Elsevier, vol. 72(C), pages 76-85.
    5. Janna Niens & Lisa Richter-Beuschel & Tobias C. Stubbe & Susanne Bögeholz, 2021. "Procedural Knowledge of Primary School Teachers in Madagascar for Teaching and Learning towards Land-Use- and Health-Related Sustainable Development Goals," Sustainability, MDPI, vol. 13(16), pages 1-36, August.
    6. Michela Battauz & Ruggero Bellio, 2011. "Structural Modeling of Measurement Error in Generalized Linear Models with Rasch Measures as Covariates," Psychometrika, Springer;The Psychometric Society, vol. 76(1), pages 40-56, January.
    7. Xiang Liu & James Yang & Hui Soo Chae & Gary Natriello, 2020. "Power Divergence Family of Statistics for Person Parameters in IRT Models," Psychometrika, Springer;The Psychometric Society, vol. 85(2), pages 502-525, June.
    8. Chun Wang, 2015. "On Latent Trait Estimation in Multidimensional Compensatory Item Response Models," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 428-449, June.
    9. Marko Böhm & Jan Barkmann & Sabina Eggert & Claus H. Carstensen & Susanne Bögeholz, 2020. "Quantitative Modelling and Perspective Taking: Two Competencies of Decision Making for Sustainable Development," Sustainability, MDPI, vol. 12(17), pages 1-32, August.
    10. repec:zbw:rwidps:0023 is not listed on IDEAS
    11. Hammon, Angelina & Zinn, Sabine, 2020. "Multiple imputation of binary multilevel missing not at random data," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 69(3), pages 547-564.
    12. Schmidt, Christoph & Fertig, Michael, 2002. "The Role of Background Factors for Reading Literacy: Straight National scores in the Pisa 2000 Study," CEPR Discussion Papers 3544, C.E.P.R. Discussion Papers.
    13. Yang Liu & Jan Hannig & Abhishek Pal Majumder, 2019. "Second-Order Probability Matching Priors for the Person Parameter in Unidimensional IRT Models," Psychometrika, Springer;The Psychometric Society, vol. 84(3), pages 701-718, September.
    14. Robitzsch, Alexander, 2020. "About Still Nonignorable Consequences of (Partially) Ignoring Missing Item Responses in Large-scale Assessment," OSF Preprints hmy45, Center for Open Science.
    15. Haruhiko Ogasawara, 2013. "Asymptotic properties of the Bayes modal estimators of item parameters in item response theory," Computational Statistics, Springer, vol. 28(6), pages 2559-2583, December.
    16. Fertig, Michael, 2003. "Educational Production, Endogenous Peer Group Formation and Class Composition - Evidence From the PISA 2000 Study," RWI Discussion Papers 2, RWI - Leibniz-Institut für Wirtschaftsforschung.
    17. Elina Tsigeman & Sebastian Silas & Klaus Frieler & Maxim Likhanov & Rebecca Gelding & Yulia Kovas & Daniel Müllensiefen, 2022. "The Jack and Jill Adaptive Working Memory Task: Construction, Calibration and Validation," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-29, January.
    18. Seonghoon Kim, 2012. "A Note on the Reliability Coefficients for Item Response Model-Based Ability Estimates," Psychometrika, Springer;The Psychometric Society, vol. 77(1), pages 153-162, January.
    19. Falch, Torberg & Justina, Fischer, 2016. "Welfare state generosity and student performance: Evidence from international student tests 1980-2003," MPRA Paper 74553, University Library of Munich, Germany.
    20. Xiao Li & Hanchen Xu & Jinming Zhang & Hua-hua Chang, 2023. "Deep Reinforcement Learning for Adaptive Learning Systems," Journal of Educational and Behavioral Statistics, , vol. 48(2), pages 220-243, April.
    21. Martin Biehler & Heinz Holling & Philipp Doebler, 2015. "Saddlepoint Approximations of the Distribution of the Person Parameter in the Two Parameter Logistic Model," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 665-688, September.
    22. Piero Veronese & Eugenio Melilli, 2021. "Confidence Distribution for the Ability Parameter of the Rasch Model," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 131-166, March.

    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:18:y:2021:i:5:p:2461-:d:509239. 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.