Using mobile phone-based text message to recruit representative samples: Assessment of a cross-sectional survey about the COVID-19 vaccine hesitation

Objective This paper aims to understand vaccine hesitancy in the post-epidemic era by analyzing texts related to vaccine reviews and public attitudes toward three prominent vaccine brands: Sinovac, AstraZeneca, and Pfizer, and exploring the relationship of vaccine hesitancy with the prevalence of epidemics in different regions. Methods We collected 165629 Twitter user comments associated with the vaccine brands. The comments were labeled based on willingness and attitude toward vaccination. We utilize a causality deep learning model, the Bert multi-channel convolutional neural network (BertMCNN), to predict users’ willingness and attitude mutually. Results When applied to the provided dataset, the proposed BertMCNN model demonstrated superior performance to traditional machine learning algorithms and other deep learning models. It is worth noting that after March 2022, the public was more hesitant about the Sinovac vaccines. Conclusions This study reveals a connection between vaccine hesitancy and the prevalence of the epidemic in different regions. The analytical results obtained from this method can assist governmental health departments in making informed decisions regarding vaccination strategies.

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Prediction and reasons for COVID-19 second dose vaccine hesitation: a cross-sectional study in a municipality of Brazil

Filho

Zoqui²,

Duarte³

et al. 2023

Sao Paulo Med. J.

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BACKGROUND: Hesitation and refusal to take a second dose of the vaccine for coronavirus disease 19 (COVID-19) are prevalent. OBJECTIVES: We aimed to identify predictive factors for hesitation or refusal and describe groups with higher rates of vaccine hesitancy. DESIGN AND SETTING: A cross-sectional study in Assis City, Brazil. METHODS: The study included adults who passed the due date for taking the COVID-19 second dose vaccine. Participants were recruited in December 2021 using a mobile-based text message. Sociodemographic and clinical data and reasons for hesitance were collected. The outcome was the attitude towards completing the recommended second dose of the vaccine. Bivariate and multivariate Poisson analyses were performed to determine the adjusted predictors. RESULTS: Participants between 30-44 years of age had a 2.41 times higher prevalence of hesitation than those aged 18-29 years. In addition, people who had adverse events or previously had COVID-19 had 4.7 and 5.4 times higher prevalences of hesitation, respectively (P value < 0.05). CONCLUSION: We found a significant group of adults aged between 30-44 years who refused the second dose of the COVID-19 vaccine. Furthermore, those who reported adverse effects after the first dose and those who had COVID-19 previously were a significant group for refusal.

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Using mobile phone-based text message to recruit representative samples: Assessment of a cross-sectional survey about the COVID-19 vaccine hesitation

Cited by 5 publications

References 19 publications

Revolutionize Infectious Prevention Using Artificial Intelligence and Deep Learning

Revolutionize Infectious Prevention Using Artificial Intelligence and Deep Learning

Understanding COVID-19 vaccine hesitancy of different regions in the post-epidemic era: A causality deep learning approach

Prediction and reasons for COVID-19 second dose vaccine hesitation: a cross-sectional study in a municipality of Brazil

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