Author: Carrieri, V.; Lagravinese, R.; Resce, G.
Title: Predicting vaccine hesitancy from area-level indicators: A machine learning approach Cord-id: pbxevpv9 Document date: 2021_3_9
ID: pbxevpv9
Snippet: 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 non-mandatory vaccines carried out in 6408 Italian municipalities in 2016. A battery of machine learning models is compared in terms of area under the Receiver Operating
Document: 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 non-mandatory vaccines carried out in 6408 Italian municipalities in 2016. A battery of machine learning models is compared in terms of area under the Receiver Operating Characteristics (ROC) 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 policy makers to target area-level provaccine awareness campaigns.
Search related documents:
Co phrase search for related documents- absolute value and logistic regression: 1, 2, 3, 4, 5, 6, 7, 8, 9
- absolute value and low income: 1
- absolute value and machine learning: 1, 2, 3, 4, 5
- absolute value and machine learning model: 1
- acceptable target and logistic regression: 1
- acceptable target and low income: 1
- acceptable target and low number: 1
- acceptance rate and logistic regression: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17
- acceptance rate and low acceptance rate: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11
- acceptance rate and low income: 1, 2
- acceptance rate and low number: 1
- acceptance rate and machine learning: 1, 2
- acceptance rate and machine learning model: 1
- accuracy term and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9
- accuracy term and machine learning approach: 1, 2
- accuracy term and machine learning model: 1, 2, 3
Co phrase search for related documents, hyperlinks ordered by date