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

Carrieri, Vincenzo; Lagravinese, Raffaele; Resce, Giuliano

doi:10.1101/2021.03.08.21253109

Cited by 7 publications

(4 citation statements)

References 36 publications

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“…The sole contextual factor suggested by our model was the economic level of the country where the students lived and studied. In agreement with this finding, Carrieri et al 2021 synthesized a machine-learning model for vaccine hesitancy among Italian municipalities' inhabitants, which indicated that socioeconomic indicators, e.g., the proportion of waste recycling and the employment rate, were the most powerful predictors of vaccine hesitancy at an area-level [69]. The household income was found to be a significant determinant for COVID-19 vaccine hesitancy among South African, Italian, and Portuguese adult populations during the pre-vaccination stage and the early stages of mass vaccination [70][71][72].…”

Section: Discussionmentioning

confidence: 84%

Universal Predictors of Dental Students’ Attitudes towards COVID-19 Vaccination: Machine Learning-Based Approach

Riad

Huang

Abdulqader³

et al. 2021

Vaccines

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Background: young adults represent a critical target for mass-vaccination strategies of COVID-19 that aim to achieve herd immunity. Healthcare students, including dental students, are perceived as the upper echelon of health literacy; therefore, their health-related beliefs, attitudes and behaviors influence their peers and communities. The main aim of this study was to synthesize a data-driven model for the predictors of COVID-19 vaccine willingness among dental students. Methods: a secondary analysis of data extracted from a recently conducted multi-center and multi-national cross-sectional study of dental students’ attitudes towards COVID-19 vaccination in 22 countries was carried out utilizing decision tree and regression analyses. Based on previous literature, a proposed conceptual model was developed and tested through a machine learning approach to elicit factors related to dental students’ willingness to get the COVID-19 vaccine. Results: machine learning analysis suggested five important predictors of COVID-19 vaccination willingness among dental students globally, i.e., the economic level of the country where the student lives and studies, the individual’s trust of the pharmaceutical industry, the individual’s misconception of natural immunity, the individual’s belief of vaccines risk-benefit-ratio, and the individual’s attitudes toward novel vaccines. Conclusions: according to the socio-ecological theory, the country’s economic level was the only contextual predictor, while the rest were individual predictors. Future research is recommended to be designed in a longitudinal fashion to facilitate evaluating the proposed model. The interventions of controlling vaccine hesitancy among the youth population may benefit from improving their views of the risk-benefit ratio of COVID-19 vaccines. Moreover, healthcare students, including dental students, will likely benefit from increasing their awareness of immunization and infectious diseases through curricular amendments.

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Section: Discussionmentioning

confidence: 84%

Universal Predictors of Dental Students’ Attitudes towards COVID-19 Vaccination: Machine Learning-Based Approach

Riad

Huang

Abdulqader³

et al. 2021

Vaccines

View full text Add to dashboard Cite

show abstract

“…The World Health Organization recognizes that vaccine hesitancy remains one of the most significant health threats [12]. Scholars also argue that vaccine hesitancy represents a threat to the COVID-19 immunization campaigns [13], suggesting that public confidence in COVID-19 vaccine needs to be promoted. More efforts are being invested in studying vaccination intent as many anticipate that an effective roll-out of the vaccine will end the pandemic [14].…”

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confidence: 99%

Social Bonding and Public Trust/Distrust in COVID-19 Vaccines

Ramkissoon

2021

Sustainability

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COVID-19 vaccine hesitancy has been a growing concern. The pandemic has proved to be very complicated with the mutated virus. The Delta variant is contributing to a surge of cases across the globe. Vaccine hesitancy can be socially contagious, requiring more stringent efforts from policy makers and health professionals in promoting vaccine uptake. Some evidence shows that vaccine acceptance appears to have played an integral role in successfully controlling the pandemic. Vaccination acceptance, however, demands that the public has a good understanding of the vaccine’s benefits in promoting healthier societies and people’s quality of life. Unclear COVID-19 vaccine information can lead to distrust in vaccines and vaccine hesitancy. It is of paramount importance to communicate clear and unbiased vaccine information to the public to encourage vaccine uptake. Word of mouth communication remains important to further promote COVID-19 vaccine uptake in the community. This short paper discusses the role of social bonds and public trust/distrust and word of mouth communication in vaccine decision making.

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“…One study used a supervised machine-learning algorithm with multivariate ordinary least squares (OLS) regression to explore the predictive power of both non-clinical personal attitudes toward scientific information and the clinical experience of severe respiratory disease on the influenza vaccination rate; accordingly, the area under the receiver operating characteristic curve (AUC) for this mixed clinical and non-clinical prediction method was 85%. 29 Carrieri et al 30 implemented the supervised random-forest machine-learning algorithm on area-level indicators of institutional and socioeconomic backgrounds to predict the vaccine hesitancy rate for Italian local authorities, thus helping public-health practitioners run targeted awareness campaigns. Their findings suggested that non-clinical features had the highest predictive powers in the random-forest algorithm, with an AUC of 0.836.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the COVID-19 vaccine uptake rate: an infodemiological study in the US

Zhou

2022

Human Vaccines & Immunotherapeutics

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A year following the initial COVID-19 outbreak in China, many countries have approved emergency vaccines. Public-health practitioners and policymakers must understand the predicted populational willingness for vaccines and implement relevant stimulation measures. This study developed a framework for predicting vaccination uptake rate based on traditional clinical data – involving an autoregressive model with autoregressive integrated moving average (ARIMA) – and innovative web search queries – involving a linear regression with ordinary least squares/least absolute shrinkage and selection operator, and machine-learning with boost and random forest. For accuracy, we implemented a stacking regression for the clinical data and web search queries. The stacked regression of ARIMA (1,0,8) for clinical data and boost with support vector machine for web data formed the best model for forecasting vaccination speed in the US. The stacked regression provided a more accurate forecast. These results can help governments and policymakers predict vaccine demand and finance relevant programs.

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

Cited by 7 publications

References 36 publications

Universal Predictors of Dental Students’ Attitudes towards COVID-19 Vaccination: Machine Learning-Based Approach

Universal Predictors of Dental Students’ Attitudes towards COVID-19 Vaccination: Machine Learning-Based Approach

Social Bonding and Public Trust/Distrust in COVID-19 Vaccines

Forecasting the COVID-19 vaccine uptake rate: an infodemiological study in the US

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