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Construction of vaccination network and influencing factors: a case study of Chongqing, China

Author

Listed:
  • Jianing Li

    (Chongqing Medical University)

  • Jie Fan

    (Chongqing Medical University
    Nanan District Center for Disease Control and Prevention)

  • Ling Zhu

    (Chongqing Medical University
    Nanan District Center for Disease Control and Prevention)

  • Xiaohua Wu

    (Nanan District Center for Disease Control and Prevention)

  • Chunyu Luo

    (Nanan District Center for Disease Control and Prevention)

  • Wei Wang

    (Chongqing Medical University)

Abstract

Vaccination is a fundamental tool in preventing infectious diseases. However, due to the wide array of vaccines available, comprehending the entire vaccine landscape can be a daunting task. To tackle this complexity, this study employs advanced network analysis methods capable of capturing the intricate relationships within multivariate datasets. The objective is to investigate how the vaccination landscape has evolved both before and after the COVID-19 pandemic. This study examined vaccination data in the Nanan District of Chongqing, China, spanning from 2016 to 2022. Additionally, the network topological characteristics were computed and scrutinized across 2326 sliding windows. The investigation focused on assessing alterations in the topological structure of the vaccination network before and after the COVID-19 pandemic, encompassing analyses at both macro and mesoscale levels. Furthermore, at the micro level, this study delved into the correlation degrees of selected vaccine nodes within the vaccination network. The analysis unveiled that the correlation and activity within the vaccination network showed a noticeable enhancement in strength in the wake of the COVID-19 pandemic, especially during the spring and winter months. However, the community structure and the average interactions between vaccines displayed a diminishing trend post-pandemic. Among the analyzed vaccines, the HepA vaccine emerged as the one with the highest average node-degree centrality rank. A closer examination of the node-degree centrality ranking chart within the vaccination network disclosed fluctuations in the rankings of various vaccine types across different time periods and seasons. In contrast, vaccines incorporated into NIP exhibited a remarkable degree of consistency, which is attributed to children adhering to a fixed vaccination schedule, rendering NIP vaccines less susceptible to disruptions and enhancing their stability within the vaccination network. This study offers valuable insights into the dynamics of the vaccination network, shedding light on the impact of the COVID-19 pandemic, seasonal variations, and the ever-shifting correlation patterns among different vaccine types. These discoveries enrich our comprehension of vaccination trends and have the potential to guide forthcoming endeavors aimed at refining vaccination strategies and enhancing public health outcomes, not only in the Nanan District but also in analogous settings.

Suggested Citation

  • 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.
    • Handle: RePEc:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-02387-2
    DOI: 10.1057/s41599-023-02387-2
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    References listed on IDEAS

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    1. 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.
    2. Anna Maria D’Arcangelis & Giulia Rotundo, 2016. "Complex Networks in Finance," Lecture Notes in Economics and Mathematical Systems, in: Pasquale Commendatore & Mariano Matilla-García & Luis M. Varela & Jose S. Cánovas (ed.), Complex Networks and Dynamics, pages 209-235, Springer.
    3. Vasiliki Plerou & Parameswaran Gopikrishnan & Bernd Rosenow & Luis A. Nunes Amaral & H. Eugene Stanley, 1999. "Universal and non-universal properties of cross-correlations in financial time series," Papers cond-mat/9902283, arXiv.org.
    4. C.-L. Tang & W.-X. Wang & X. Wu & B.-H. Wang, 2006. "Effects of average degree on cooperation in networked evolutionary game," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 53(3), pages 411-415, October.
    5. Rui Ding & Norsidah Ujang & Hussain Bin Hamid & Mohd Shahrudin Abd Manan & Rong Li & Safwan Subhi Mousa Albadareen & Ashkan Nochian & Jianjun Wu, 2019. "Application of Complex Networks Theory in Urban Traffic Network Researches," Networks and Spatial Economics, Springer, vol. 19(4), pages 1281-1317, December.
    6. Wen, Xing-Zhang & Zheng, Yue & Du, Wen-Li & Ren, Zhuo-Ming, 2023. "Regulating clustering and assortativity affects node centrality in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    7. Katarzyna Czabanowska & Ellen Kuhlmann, 2021. "Public health competences through the lens of the COVID‐19 pandemic: what matters for health workforce preparedness for global health emergencies," International Journal of Health Planning and Management, Wiley Blackwell, vol. 36(S1), pages 14-19, May.
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