Predicting vaccine hesitancy from area‐level indicators: A machine learning approach
利用机器学习算法，基于区域层面的指标（如废物回收率和就业率）预测疫苗犹豫高风险社区，准确率比随机基线提高24%，可帮助政策制定者精准开展疫苗宣传。
Vaccine hesitancy (VH) might represent a serious threat to the next COVID-19 mass immunization campaign. We use machine learning algorithms to predict communities at a high risk of VH relying on area-level indicators easily available to policymakers. We illustrate our approach on data from child immunization campaigns for seven nonmandatory vaccines carried out in 6062 Italian municipalities in 2016. A battery of machine learning models is compared in terms of area under the receiver operating characteristics curve. We find that the Random Forest algorithm best predicts areas with a high risk of VH improving the unpredictable baseline level by 24% in terms of accuracy. Among the area-level indicators, the proportion of waste recycling and the employment rate are found to be the most powerful predictors of high VH. This can support policymakers to target area-level provaccine awareness campaigns.