Predicting vaccine hesitancy from area-level indicators: A machine learning approach
| Autor: | Vincenzo Carrieri, Giuliano Resce, Raffaele Lagravinese |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
2019-20 coronavirus outbreak
Coronavirus disease 2019 (COVID-19) Computer science area-level indicators machine learning vaccine hesitancy Child Humans Machine Learning SARS-CoV-2 Vaccination COVID-19 Vaccines Baseline level Machine learning computer.software_genre Waste recycling Receiver operating characteristic business.industry Health Policy Random forest Child immunization Mass immunization Artificial intelligence business computer |
| Zdroj: | Health economicsREFERENCES. 30(12) |
| ISSN: | 1099-1050 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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