Predicting vaccine hesitancy from area-level indicators: A machine learning approach.
Vincenzo Carrieri1,2,3, Raffele Lagravinese4, Giuliano Resce5
1Department of Law, Economics and Sociology, Magna Graecia University, Catanzaro, Italy.
Health Economics
|September 15, 2021
Summary
Machine learning models can predict communities at high risk of vaccine hesitancy (VH). This approach improves accuracy by 24%, identifying waste recycling and employment rates as key predictors for targeted public health interventions.
Area of Science:
- Public Health
- Epidemiology
- Health Policy
Background:
- Vaccine hesitancy (VH) poses a significant challenge to public health initiatives, potentially undermining mass immunization campaigns like those for COVID-19.
- Predicting and understanding the drivers of VH at a community level is crucial for developing effective public health strategies.
- Existing methods for identifying at-risk communities often lack the predictive power needed for timely intervention.
Purpose of the Study:
- To develop and evaluate machine learning models for predicting areas with high vaccine hesitancy.
- To identify key area-level indicators that are most strongly associated with vaccine hesitancy.
- To provide policymakers with a tool to target public health interventions and awareness campaigns effectively.
Main Methods:
- Utilized machine learning algorithms to analyze data from child immunization campaigns for seven non-mandatory vaccines across 6062 Italian municipalities in 2016.
- Compared the predictive performance of various machine learning models using the area under the receiver operating characteristics curve (AUC).
- Identified significant area-level predictors of vaccine hesitancy from a range of socioeconomic and environmental indicators.
Main Results:
- The Random Forest algorithm demonstrated the highest predictive accuracy for identifying areas at high risk of VH.
- The model achieved a 24% improvement in accuracy compared to the baseline unpredictable level.
- The proportion of waste recycling and the employment rate were identified as the most potent predictors of high vaccine hesitancy.
Conclusions:
- Machine learning offers a powerful approach to predict community-level vaccine hesitancy using readily available data.
- Area-level indicators, such as waste recycling and employment rates, can serve as valuable proxies for identifying populations at risk of VH.
- These findings can inform the strategic deployment of public health resources and targeted pro-vaccine awareness campaigns to mitigate vaccine hesitancy.
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