Trump vs. the GOP: Political Determinants of COVID-19 Vaccine Hesitancy

Jung, Yeonha; Lee, Seungduck

doi:10.2139/ssrn.3966799

Cited by 7 publications

(5 citation statements)

References 21 publications

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“…Moreover, a recent analysis identified a specific "Trump effect" such that even conditional on the Republican vote share, support for Trump was additionally and causally associated with a lower vaccination rate (Jung & Lee, 2023).…”

Section: The Political Landscape Of Disinformationmentioning

confidence: 99%

Liars Know They Are Lying: Differentiating Disinformation from Disagreement

Lewandowsky,

Ecker,

Cook

et al. 2024

Preprint

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Section: The Political Landscape Of Disinformationmentioning

confidence: 99%

Liars Know They Are Lying: Differentiating Disinformation from Disagreement

Lewandowsky,

Ecker,

Cook

et al. 2024

Preprint

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“…Formally, Vaccine hesitancy according to the Strategic Advisory Group of Experts on Immunization (SAGE) Working Group [1,2] is defined as a delay in accepting or refusing vaccination [3,4]. Upon the release of COVID-19 vaccines, it was essential that a substantial percentage of the population need to get vaccinated [3] [7] [5] to achieve herd immunity. For the decision-makers it becomes crucial to comprehend and recognise a person’s vaccination-related behavior.…”

Section: Introductionmentioning

confidence: 99%

“…Artificial intelligence (AI) techniques, which involve processing large amounts of data, identifying patterns and trends, could be helpful in making predictions that might guide the understanding and the implementation of efficient vaccination strategies. Several researches have shown that AI-based techniques may be used to identify important vaccination uptake factors and predict vaccine uptake behavior at the individual level [9][10][11][12][13]. However, models developed in previous literature [8][9][10][11][12] are based on a local scale (for a specific region or country), and none of them studied whether or not vaccination policies can be implemented on a global scale.…”

Section: Introductionmentioning

confidence: 99%

“…Several researches have shown that AI-based techniques may be used to identify important vaccination uptake factors and predict vaccine uptake behavior at the individual level [9][10][11][12][13]. However, models developed in previous literature [8][9][10][11][12] are based on a local scale (for a specific region or country), and none of them studied whether or not vaccination policies can be implemented on a global scale. The success of vaccination programmes may vary between geographical location and socio-demographic factors of a specific population [7].…”

Section: Introductionmentioning

confidence: 99%

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Bayesian network approach to develop generalisable predictive model for COVID-19 vaccine uptake

Awasthi¹,

Nagori²,

Nasri

2023

Preprint

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ObjectiveTo discover global determinants of vaccine uptake behavior and to develop a generalizable machine learning model to predict the vulnerability of vaccine uptake behavior at the individual level.Methodology23135 Respondents across the 23 countries were interviewed for the survey questionnaire, after preprocessing and cleaning data, we performed Bayesian networks and generalized linear models to identify the key determinants of vaccine uptake. Markov Blankets obtained from the Bayesian networks were used to estimate the important predictors of the vaccine uptake. These variables were then used to build the models. To build generalizable models, we used country-wise data splitting. Model evaluation is assessed for the prediction performance on the new countries. We also developed income specific models cross validated within the income group.ResultsWe found 16 important predictors of vaccine uptake using the Bayesian network and Markov Blanket approach. We found that the trust of the central government (Log-Odds 0.55[0.25, 0.84] (p= 0.0002)), Vaccination restriction for national and international travel (Log-Odds 0.4[0.14, 0.65] (p= 0.0034)) as the key determinants of Vaccine uptake. Our Generalized mixed effects model approach achieved an AUC of 89%, Precision 90% and Recall of 82% on the prediction task on new countries, thus, generalizing to new countries.

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“…Arti cial intelligence (AI) techniques, which involve processing large amounts of data, identifying patterns and trends, could be helpful in making predictions that might guide the understanding and the implementation of e cient vaccination strategies. Several researches have shown that AI-based techniques may be used to identify important vaccination uptake factors and predict vaccine uptake behavior at the individual level [9][10][11][12][13]. However, models developed in previous literature [8 -12] are based on a local scale (for a speci c region or country), and none of them studied whether or not vaccination policies can be implemented on a global scale.…”

Section: Introductionmentioning

confidence: 99%

Bayesian network approach to develop generalisable predictive model for COVID-19 vaccine uptake

Awasthi¹,

Nagori²,

Nasri

2023

Preprint

View full text Add to dashboard Cite

The effectiveness of a vaccine depends on vaccine uptake, which is influenced by various factors, including vaccine hesitancy. Vaccine hesitancy is a complex socio-behavioral issue, influenced by misinformation, distrust in healthcare providers and government organizations, fear of side effects, and cultural or religious beliefs. To address this problem, AI models have been developed, but their global generalizability remains unclear. Therefore, this study aimed to identify global determinants of vaccine uptake and develop a generalizable machine learning model to predict individual-level vaccine uptake. The study used publicly available survey data from 23 countries and employed Bayesian networks and generalized mixed effects models to identify key determinants of vaccine uptake. The results showed that trust in the central government and vaccination restrictions for national and international travel were key determinants of vaccine uptake. A generalized mixed effects model achieved an AUC of 89% (SD = 1%)% (SD = 1%), precision of 90%(SD = 4%), and recall of 82% (SD = 2%) on unseen testing data from new countries, demonstrating the model's generalizability. The findings of this study can inform targeted interventions to improve vaccine uptake globally.

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Trump vs. the GOP: Political Determinants of COVID-19 Vaccine Hesitancy

Cited by 7 publications

References 21 publications

Liars Know They Are Lying: Differentiating Disinformation from Disagreement

Liars Know They Are Lying: Differentiating Disinformation from Disagreement

Bayesian network approach to develop generalisable predictive model for COVID-19 vaccine uptake

Bayesian network approach to develop generalisable predictive model for COVID-19 vaccine uptake

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