Proactive advising: a machine learning driven approach to vaccine hesitancy

Bell, Andrew; Rich, Alexander; Teng, Melisande; Orešković, Tin; Brás, Nuno B.; Mestrinho, Lenia; Golubovic, Srdan; Pristaš, Ivan; Zejnilović, Leid

doi:10.1109/ichi.2019.8904616

Cited by 13 publications

(17 citation statements)

References 14 publications

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“…Compared with the unpredictable baseline level, the accuracy with Random Forest improves by 24%. Remarkably, our best model shows performances in line with recent studies that make use of patient level indicators with a higher granularity (see Bell et al 2019). Among the area-level indicators, the share of waste recycling and the employment rate are found to be the most powerful predictors.…”

Section: Introductionsupporting

confidence: 89%

“…The main strength of administrative data is their immediate availability, at zero additional cost for analysts. Our approach also suggests that this does not come at costs in terms of accuracy, being the performances of our model in line with models using patient-level indicators to predict VH (see Bell et al 2019, Oreskovic et al 2020). The administrative data already exist and does not require an ad-hoc collection.…”

Section: Discussionsupporting

confidence: 69%

“…A few papers also employed machine learning algorithms to predict VH using patient-level data. For instance, Bell et al (2019) use machine learning approach to identify children at risk of not being vaccinated against Measles, Mumps and Rubella in WHO countries. Oreskovic et al (2020) adopt Proactive machine-learning-based approaches to VH for a potential SARS-Cov-2 using LASSO logistic regression on a low number of attributes of the child and his or her family and community.…”

Section: Introductionmentioning

confidence: 99%

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

Carrieri

Lagravinese

Resce

2021

Preprint

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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.

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Section: Introductionsupporting

confidence: 89%

Section: Discussionsupporting

confidence: 69%

Section: Introductionmentioning

confidence: 99%

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

Carrieri

Lagravinese

Resce

2021

Preprint

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“…Some researchers also look at the impact of newspapers on the vaccination decision (Okuhara et al, 2019b). Other researchers examine social media as a tool to promote on-time vaccinations for children (Chandir et al, 2018;Bell et al, 2019).…”

Section: Theme: Public Sentiment About Vaccinesmentioning

confidence: 99%

Big Data Applications in Vaccinology

Kasten

2021

International Journal of Big Data and Analytics in Healthcare

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The development of vaccines has been one of the most important medical and pharmacological breakthroughs in the history of the world. Besides saving untold lives, they have enabled the human race to live and thrive in conditions thought far too dangerous only a few centuries ago. In recent times, the development of the COVID-19 vaccine has captured the world’s attention as the primary tool to defeat the current pandemic. The tools used to develop these vaccines have changed dramatically over time, with the use of big data technologies becoming standard in many instances. This study performs a structured literature review centered on the development, distribution, and evaluation of vaccines and the role played by big data tools such as data analytics, datamining, and machine learning. Through this review, the paper identifies where these technologies have made important contributions and in what areas further research is likely to be useful.

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“…To evaluate influencing factors that place individuals at a higher risk of measles [22] utilized ML techniques and found that contact with measles patients, age, rhinorrhea, vaccination, male sex, cough, conjunctivitis, ethnicity, and fever were the crucial factors that were associated with measles disease. The authors in [23] adopted the LASSO (Least Absolute Shrinkage and Selection Operator) logistic regression model on the electronic health record to identify message vaccine-resistant families and obtained 72.0 % precision. They attributed 25 features based on the history of the child and from their family members.…”

Section: Introductionmentioning

confidence: 99%

Associating Measles Vaccine Uptake Classification and its Underlying Factors Using an Ensemble of Machine Learning Models

et al. 2021

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Measles is one of the significant public health issues responsible for the high mortality rate around the globe, especially for developing countries. Using nationally representative demographic and health survey data, measles vaccine utilization has been classified, and its underlying factors are identified through an ensemble Machine Learning (ML) approach. Firstly, missing values are imputed employing various approaches, and then several feature selection techniques have been applied to identify the crucial attributes for predicting measles vaccination. A grid search hyperparameter optimization technique has been applied for tuning the critical hyperparameters of different ML models, such as Naive Bayes, random forest, decision tree, XGboost, and lightgbm. The categorization performance of the individual optimized ML model as all as their ensembles have been reported utilizing our proposed BDHS dataset. Individually, the optimized lightgbm provides the highest precision and AUC of 79.90 % and 77.80 %, respectively. This result improved when the optimized lightgbm is ensembled with XGboost, providing the precision and AUC of 84.60 % and 80.0 %, respectively. Our result reveals that the statistical median imputation technique with the XGboost-based attribute selection method and the lightgbm classifier provides the best individual result. The performance has been improved when the proposed weighted ensemble of the XGboost and lightgbm approach has been adapted with the same preprocessing and recommended for measles vaccine utilization. The significance of our proposed approach is that it utilizes minimum attributes collected from the child and their family members and yielded 80.0 % accuracy, making it easily explainable by caregivers and healthcare personnel. Finally, our predictive model provides an early detection procedure to help national policymakers enforce new policies with specific rules and regulations. The data and source codes that support the findings of this study are available at https://github.com/kamruleee51/measles_vaccine_uptake. INDEX TERMSAttribute selection, Measles vaccine uptake classification, Measles BDHS data, Missing value imputation, Weighted ensemble ML model.

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Proactive advising: a machine learning driven approach to vaccine hesitancy

Cited by 13 publications

References 14 publications

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

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

Big Data Applications in Vaccinology

Associating Measles Vaccine Uptake Classification and its Underlying Factors Using an Ensemble of Machine Learning Models

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