New theoretical ISM-K2 Bayesian network model for evaluating vaccination effectiveness

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.

Keywords: Bayesian network; Interpretive structural modeling; Vaccine effectiveness.

Fig. 1

Framework and flowchart of the paper

Fig. 2

Approval of China’s influenza vaccine market. Data Sources: https://www.nifdc.org.cn/nifdc/

Fig. 3

Distribution of influenza vaccines from origins to Hunan Province

Fig. 4

Outlier data detection based on a boxplot

Fig. 5

Providing the relationship between reference variables

Fig. 6

Risk of vaccination: a ISM Model for VE Risk, b BN Structure for VE Risk

Fig. 7

BN for VE Risk

Fig. 8

These are the two figures of the confusion matrix: a confusion matrix for temperature record (d2) of inoculation point storage room; b confusion matrix for VE (e)

Fig. 9

Comparison of Single Structure BN for VE Risks: a description of BNISM; b description of BNGA-K2

Fig. 10

Accuracy gap comparison of VE prediction