1. COVID-19 vaccine willingness and hesitancy among residents in Qatar: a quantitative analysis based on machine learning.
- Author
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Hafizh, Muhammad, Badri, Yousif, Mahmud, Sakib, Hafez, Amir, and Choe, Pilsung
- Subjects
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VACCINATION , *ANALYSIS of variance , *CONFIDENCE intervals , *COVID-19 vaccines , *ATTITUDE (Psychology) , *ACTIVITIES of daily living , *MACHINE learning , *RACE , *VACCINE hesitancy , *QUESTIONNAIRES , *DESCRIPTIVE statistics , *SCALE analysis (Psychology) , *HYPOTHESIS , *RESEARCH funding , *DATA analysis software , *ETHNIC groups , *SENSITIVITY & specificity (Statistics) , *LOGISTIC regression analysis , *RECEIVER operating characteristic curves , *CONTACT tracing , *PROBABILITY theory , *ALGORITHMS - Abstract
The spread of the COVID-19 virus has brought forward a global pandemic that has halted many businesses and placed restrictions on daily activities. Vaccination of residents is crucial for a small country like Qatar to contain the spread of the virus. This study will aim to investigate and report the general outlook of Qatari residents toward the vaccine, the points of hesitancies, and how the contact tracing application (Ehteraz) can play a role in promoting vaccination. A questionnaire survey was given, and respondents answered a series of background questions and questions related to vaccine hesitancy and the effectiveness of the Ehteraz application. A broad search strategy was used to identify the main contributors to willingness and hesitancy with the COVID-19 vaccination. Regression models and test-train machine learning techniques were used for analysis. The results showed that dominance analysis was effective in highlighting the main barriers and promoters in taking the vaccine and that respondent's characteristics can be used to predict vaccination attitude. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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