Abstract
Aiming at the difficulty in obtaining a complete Bayesian network (BN) structure directly through search-scoring algorithms, authors attempted to incorporate expert judgment and historical data to construct an interpretive structural model with an ISM-K2 algorithm for evaluating vaccination effectiveness (VE). By analyzing the influenza vaccine data provided by Hunan Provincial Center for Disease Control and Prevention, risk factors influencing VE in each link in the process of “Transportation—Storage—Distribution—Inoculation” were systematically investigated. Subsequently, an evaluation index system of VE and an ISM-K2 BN model were developed. Findings include: (1) The comprehensive quality of the staff handling vaccines has a significant impact on VE; (2) Predictive inference and diagnostic reasoning through the ISM-K2 BN model are stable, effective, and highly interpretable, and consequently, the post-production supervision of vaccines is enhanced. The study provides a theoretical basis for evaluating VE and a scientific tool for tracking the responsibility of adverse events of ineffective vaccines, which has the value of promotion in improving VE and reducing the transmission rate of infectious diseases.
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26 July 2022
A Correction to this paper has been published: https://doi.org/10.1007/s12652-022-04337-3
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Acknowledgements
This work was supported by the National Social Science Foundation of China under Grants 19BTJ011 and funded by the Natural Science Foundation of Hunan Provice, China under Grants 2022JJ30673.
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Xie, X., Xie, B., Xiong, D. et al. New theoretical ISM-K2 Bayesian network model for evaluating vaccination effectiveness. J Ambient Intell Human Comput 14, 12789–12805 (2023). https://doi.org/10.1007/s12652-022-04199-9
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DOI: https://doi.org/10.1007/s12652-022-04199-9