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Complex Contact Network of Patients at the Beginning of an Epidemic Outbreak: An Analysis Based on 1218 COVID-19 Cases in China

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  • Zhangbo Yang

    (School of Humanities and Social Science, Xi’an Jiaotong University, Xi’an 710049, China
    Institute for Empirical Social Science Research, Xi’an Jiaotong University, Xi’an 710049, China)

  • Jiahao Zhang

    (School of Social Development and Public Policy, Fudan University, Shanghai 200433, China)

  • Shanxing Gao

    (School of Management, Xi’an Jiaotong University, Xi’an 710049, China)

  • Hui Wang

    (School of Management, Xi’an Jiaotong University, Xi’an 710049, China)

Abstract

The spread of viruses essentially occurs through the interaction and contact between people, which is closely related to the network of interpersonal relationships. Based on the epidemiological investigations of 1218 COVID-19 cases in eight areas of China, we use text analysis, social network analysis and visualization methods to construct a dynamic contact network of the epidemic. We analyze the corresponding demographic characteristics, network indicators, and structural characteristics of this network. We found that more than 65% of cases are likely to be infected by a strong relationship, and nearly 40% of cases have family members infected at the same time. The overall connectivity of the contact network is low, but there are still some clustered infections. In terms of the degree distribution, most cases’ degrees are concentrated between 0 and 2, which is relatively low, and only a few ones have a higher degree value. The degree distribution also conforms to the power law distribution, indicating the network is a scale-free network. There are 17 cases with a degree greater than 10, and these cluster infections are usually caused by local transmission. The first implication of this research is we find that the COVID-19 spread is closely related to social structures by applying computational sociological methods for infectious disease studies; the second implication is to confirm that text analysis can quickly visualize the spread trajectory at the beginning of an epidemic.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:2:p:689-:d:720226
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    as
    1. Elena Calvo Gallardo & Juan Carlos Fernandez de Arroyabe & Nieves Arranz, 2020. "Preventing Internal COVID-19 Outbreaks within Businesses and Institutions: A Methodology Based on Social Networks Analysis for Supporting Occupational Health and Safety Services Decision Making," Sustainability, MDPI, vol. 12(11), pages 1-16, June.
    2. Christian M. Hafner, 2020. "The Spread of the Covid-19 Pandemic in Time and Space," IJERPH, MDPI, vol. 17(11), pages 1-13, May.
    3. Yongdong Shi & Rongsheng Huang & Hanwen Cui, 2021. "Prediction and Analysis of Tourist Management Strategy Based on the SEIR Model during the COVID-19 Period," IJERPH, MDPI, vol. 18(19), pages 1-12, October.
    4. Lingling Xia & Guoping Jiang & Yurong Song & Bo Song, 2017. "An improved local immunization strategy for scale-free networks with a high degree of clustering," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 90(1), pages 1-7, January.
    5. Yingfei Zhang & Zheng Feei Ma, 2020. "Impact of the COVID-19 Pandemic on Mental Health and Quality of Life among Local Residents in Liaoning Province, China: A Cross-Sectional Study," IJERPH, MDPI, vol. 17(7), pages 1-12, March.
    6. Abel Brodeur & David Gray & Anik Islam & Suraiya Bhuiyan, 2021. "A literature review of the economics of COVID‐19," Journal of Economic Surveys, Wiley Blackwell, vol. 35(4), pages 1007-1044, September.
    7. 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.
    8. Matouk, A.E., 2020. "Complex dynamics in susceptible-infected models for COVID-19 with multi-drug resistance," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    9. Xiaoyan Mu & Anthony Gar-On Yeh & Xiaohu Zhang, 2021. "The interplay of spatial spread of COVID-19 and human mobility in the urban system of China during the Chinese New Year," Environment and Planning B, , vol. 48(7), pages 1955-1971, September.
    10. Duncan J. Watts & Steven H. Strogatz, 1998. "Collective dynamics of ‘small-world’ networks," Nature, Nature, vol. 393(6684), pages 440-442, June.
    11. Xiaowei Chen & Jing Li & Chen Xiao & Peilin Yang, 2021. "Numerical solution and parameter estimation for uncertain SIR model with application to COVID-19," Fuzzy Optimization and Decision Making, Springer, vol. 20(2), pages 189-208, June.
    12. Delphine Planas & David Veyer & Artem Baidaliuk & Isabelle Staropoli & Florence Guivel-Benhassine & Maaran Michael Rajah & Cyril Planchais & Françoise Porrot & Nicolas Robillard & Julien Puech & Matth, 2021. "Reduced sensitivity of SARS-CoV-2 variant Delta to antibody neutralization," Nature, Nature, vol. 596(7871), pages 276-280, August.
    13. Jayson S. Jia & Xin Lu & Yun Yuan & Ge Xu & Jianmin Jia & Nicholas A. Christakis, 2020. "Population flow drives spatio-temporal distribution of COVID-19 in China," Nature, Nature, vol. 582(7812), pages 389-394, June.
    14. Yang Xiao & Yanjie Bian & Lei Zhang, 2020. "Mental Health of Chinese Online Networkers under COVID-19: A Sociological Analysis of Survey Data," IJERPH, MDPI, vol. 17(23), pages 1-17, November.
    15. Stephen Eubank & Hasan Guclu & V. S. Anil Kumar & Madhav V. Marathe & Aravind Srinivasan & Zoltán Toroczkai & Nan Wang, 2004. "Modelling disease outbreaks in realistic urban social networks," Nature, Nature, vol. 429(6988), pages 180-184, May.
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    1. Jianing Li & Jie Fan & Ling Zhu & Xiaohua Wu & Chunyu Luo & Wei Wang, 2023. "Construction of vaccination network and influencing factors: a case study of Chongqing, China," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.
    2. Khan, Junaid Iqbal & Ullah, Farman & Lee, Sungchang, 2022. "Attention based parameter estimation and states forecasting of COVID-19 pandemic using modified SIQRD Model," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).

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