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Personal and social patterns predict influenza vaccination decision
Adir Shaham1, Gabriel Chodick2, Varda Shalev2
1Department of Industrial Engineering, Tel Aviv University, 55 Haim Levanon St, Tel Aviv, Israel.
BMC Public Health
|February 14, 2020
Summary
Predicting influenza vaccination uptake is possible using personal and social factors. Machine learning models accurately forecast individual vaccination decisions, paving the way for personalized public health campaigns.
Area of Science:
- Public Health
- Epidemiology
- Health Informatics
Background:
- Seasonal influenza vaccination coverage remains suboptimal globally.
- Individual decision-making is key to vaccination adherence.
- Understanding behavioral patterns can improve vaccine uptake.
Purpose of the Study:
- To identify personal and social behavioral patterns influencing influenza vaccination.
- To develop a predictive model for individual vaccination decisions.
Main Methods:
- Retrospective cohort study of 250,000 individuals (2007-2017).
- Analysis of electronic medical records from 137 clinics.
- Application of machine-learning models to predict vaccination uptake.
Main Results:
- Vaccination decisions are influenced by personal factors (e.g., past behavior, health consumption) and social factors (e.g., family vaccination).
- Age, socioeconomic status, and location significantly impact uptake.
- An XGBoost model achieved 0.91 ROC-AUC, 90% accuracy, and recall, using historical vaccination, demographics, and health indicators.
Conclusions:
- Accurate prediction of vaccination decisions enables personalized campaigns.
- This approach can enhance the next generation of targeted vaccination strategies.
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