Predicting vaccine hesitancy from area‐level indicators: A machine learning approach

Abstract

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.