Exploring and Monitoring the Reasons for Hesitation with COVID-19 Vaccine Based on Social-Platform Text and Classification Algorithms

Safety of an inactivated COVID-19 vaccine (CoronaVac) in children aged 7-14 years in Taizhou, China

Our study aimed to evaluate the safety of CoronaVac, an inactivated vaccine made by Sinovac, in children aged 7-14. We conducted a parent-administered online survey to monitor adverse reactions after vaccinating children in Taizhou, China, from February 15, 2021, to January 19, 2022. 767 parents completed the survey after receiving a questionnaire via WeChat. Overall, 15.3 % (117/767) of children experienced adverse effects after the first dose, and 12.2 % (88/724) after the second. Muscle pain was the most common adverse reaction post-first dose (10.0 %), while localized pain or itching at the injection site was most common after the second dose (7.6 %). In conclusion, the vaccine has a low incidence of side effects. The mild to moderate, transient, and common nature of these effects further boosts parents' confidence in vaccinating their children.

COVID-19 vaccine hesitancy worldwide and its associated factors: a systematic review and meta-analysis

Introduction

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has taken a toll on humans, and the development of effective vaccines has been a promising tool to end the pandemic. However, for a vaccination program to be successful, a considerable proportion of the community must be vaccinated. Hence, public acceptance of coronavirus disease 2019 (COVID-19) vaccines has become the key to controlling the pandemic. Recent studies have shown vaccine hesitancy increasing over time. This systematic review aims to evaluate the COVID-19 vaccine hesitancy rate and related factors in different communities.

Method

A comprehensive search was performed in MEDLINE (via PubMed), Scopus, and Web of Science from January 1, 2019 to January 31, 2022. All relevant descriptive and observational studies (cross-sectional and longitudinal) on vaccine hesitancy and acceptance were included in this systematic review. In the meta-analysis, odds ratio (OR) was used to assess the effects of population characteristics on vaccine hesitancy, and event rate (acceptance rate) was the effect measure for overall acceptance. Publication bias was assessed using the funnel plot, Egger's test, and trim-and-fill methods.

Result

A total of 135 out of 6,417 studies were included after screening. A meta-analysis of 114 studies, including 849,911 participants, showed an overall acceptance rate of 63.1%. In addition, men, married individuals, educated people, those with a history of flu vaccination, those with higher income levels, those with comorbidities, and people living in urban areas were less hesitant.

Conclusion

Increasing public awareness of the importance of COVID-19 vaccines in overcoming the pandemic is crucial. Being men, living in an urban region, being married or educated, having a history of influenza vaccination, having a higher level of income status, and having a history of comorbidities are associated with higher COVID-19 vaccine acceptance.

Multivariate time series short term forecasting using cumulative data of coronavirus

Coronavirus emerged as a highly contagious, pathogenic virus that severely affects the respiratory system of humans. The epidemic-related data is collected regularly, which machine learning algorithms can employ to comprehend and estimate valuable information. The analysis of the gathered data through time series approaches may assist in developing more accurate forecasting models and strategies to combat the disease. This paper focuses on short-term forecasting of cumulative reported incidences and mortality. Forecasting is conducted utilizing state-of-the-art mathematical and deep learning models for multivariate time series forecasting, including extended susceptible-exposed-infected-recovered (SEIR), long-short-term memory (LSTM), and vector autoregression (VAR). The SEIR model has been extended by integrating additional information such as hospitalization, mortality, vaccination, and quarantine incidences. Extensive experiments have been conducted to compare deep learning and mathematical models that enable us to estimate fatalities and incidences more precisely based on mortality in the eight most affected nations during the time of this research. The metrics like mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are employed to gauge the model's effectiveness. The deep learning model LSTM outperformed all others in terms of forecasting accuracy. Additionally, the study explores the impact of vaccination on reported epidemics and deaths worldwide. Furthermore, the detrimental effects of ambient temperature and relative humidity on pathogenic virus dissemination have been analyzed.

Analysis of Differences in User Groups and Post Sentiment of COVID-19 Vaccine Hesitators in Chinese Social-Media Platforms

(1) Background: The COVID-19 epidemic is still global and no specific drug has been developed for COVID-19. Vaccination can both prevent infection and limit the spread of the epidemic. Eliminating hesitation to the COVID-19 vaccine and achieving early herd immunity is a common goal for all countries. However, efforts in this area have not been significant and there is still a long way to go to eliminate vaccine hesitancy. (2) Objective: This study aimed to uncover differences in the characteristics and sentiments of COVID-19 vaccine hesitators on Chinese social-media platforms and to achieve a classification of vaccine-hesitant groups. (3) Methods: COVID-19-vaccine-hesitation posts and user characteristics were collected on the Sina Microblog platform for posting times spanning one year, and posts were identified for hesitation types. Logistic regression was used to conduct user-group analysis. The differences in user characteristics between the various types of COVID-19 vaccine posts were analysed according to four user characteristics: gender, address type, degree of personal-information disclosure, and whether they followed health topics. Sentiment analysis was conducted using sentiment analysis tools to calculate the sentiment scores and sentiment polarity of various COVID-19 vaccine posts, and the K-W test was used to uncover the sentiment differences between various types of COVID-19-vaccine-hesitation posts. (4) Results: There are differences in the types of COVID-19-vaccine-hesitation posts posted by users with different characteristics, and different types of COVID-19-vaccine-hesitation posts differ in terms of sentiment. Differences in user attributes and user behaviors are found across the different COVID-19-vaccine-hesitation types. Ultimately, two COVID-19-vaccine-hesitant user groups were identified: Body-related and Non-bodily-related. Users who posted body-related vaccine-hesitation posts are more often female, disclose more personal information and follow health topics on social-media platforms. Users who posted non-bodily-related posts are more often male, disclose less personal information, and do not follow health topics. The average sentiment score for all COVID-19-vaccine-hesitant-type posts is less than 0.45, with negative-sentiment posts outweighing positive- and neutral-sentiment posts in each type, among which the "Individual rights" type is the most negative. (5) Conclusions: This paper complements the application of user groups in the field of vaccine hesitation, and the results of the analysis of group characteristics and post sentiment can help to provide an in-depth and comprehensive analysis of the concerns and needs of COVID-19 vaccine hesitators. This will help public-health agencies to implement more targeted strategies to eliminate vaccine hesitancy and improve their work related to the COVID-19 vaccine, with far-reaching implications for COVID-19-vaccine promotion and vaccination.

COVID-19 Vaccine Hesitancy in Denmark and Russia: A qualitative typology at the nexus of agency and health capital

Vaccination of the world population is being embraced by 184 countries as the main strategy to end the COVID-19 pandemic; vaccination rates are stalling even in countries with high vaccine availability, though. This article investigates the phenomenon of vaccine hesitancy in two such countries, the Kingdom of Denmark and the Russian Federation, through a qualitative study of the different types of hesitancy to COVID-19 vaccination programs and their underlying mechanisms. The analysis reveals a typology along the dimensions of agency and health capital: resisting hesitancy based on mistrust of authority, paralyzed hesitancy based on personal fear, informed hesitancy based on informed choice, and empowered hesitancy based on empowered choice. While the mechanisms underlying vaccine hesitancy are to a great extent comparable between the two countries, differences in population size, societal cohesion, and political culture seem to impact the prevalence and severity of types and, thereby, the outcomes of national COVID-19 vaccination programs and national campaigns for mitigating COVID-19 vaccine hesitancy. The implications of these findings extend beyond the particular context of COVID-19 and the countries studied, supporting and nuancing existing models for vaccine hesitancy, as well as providing a starting point for tailored campaigns for mitigating vaccine hesitancy.

COVID-19 risk perception framework of the public: an infodemic tool for future pandemics and epidemics

Understanding the risk perceptions of the public is central for risk communications and infodemic management during emergency and preparedness planning as people’s behavior depends on how they perceive the related risks. This qualitative study aimed to identify and describe factors related to COVID-19 risk perceptions of the public in Finland and to make this information readily available to those who communicate with the public during crises. The study is part of a larger project exploring crisis narratives through a mixed-methods approach. The study was based on a dataset of over 10,000 comments on the Facebook and Twitter posts of the Finnish Institute of Health and Welfare (THL) between March-May 2021. The data were analyzed qualitatively using thematic analysis. The study identified concepts linked with the pandemic risk perception that included knowledge, perceptions, personal experiences, trust, attitudes, and cultural values. The findings resulted in a framework of risk perceptions that can be used as taxonomy and a set of key concepts and keywords in social listening to monitor risk perception during future epidemics and pandemics.

Social Listening to Enhance Access to Appropriate Pandemic Information Among Culturally Diverse Populations: Case Study From Finland

Background

Social listening, the process of monitoring and analyzing conversations to inform communication activities, is an essential component of infodemic management. It helps inform context-specific communication strategies that are culturally acceptable and appropriate for various subpopulations. Social listening is based on the notion that target audiences themselves can best define their own information needs and messages.

Objective

This study aimed to describe the development of systematic social listening training for crisis communication and community outreach during the COVID-19 pandemic through a series of web-based workshops and to report the experiences of the workshop participants implementing the projects.

Methods

A multidisciplinary team of experts developed a series of web-based training sessions for individuals responsible for community outreach or communication among linguistically diverse populations. The participants had no previous training in systematic data collection or monitoring. This training aimed to provide participants with sufficient knowledge and skills to develop a social listening system based on their specific needs and available resources. The workshop design took into consideration the pandemic context and focused on qualitative data collection. Information on the experiences of the participants in the training was gathered based on participant feedback and their assignments and through in-depth interviews with each team.

Results

A series of 6 web-based workshops was conducted between May and September 2021. The workshops followed a systematic approach to social listening and included listening to web-based and offline sources; rapid qualitative analysis and synthesis; and developing communication recommendations, messages, and products. Follow-up meetings were organized between the workshops during which participants could share their achievements and challenges. Approximately 67% (4/6) of the participating teams established social listening systems by the end of the training. The teams tailored the knowledge provided during the training to their specific needs. As a result, the social systems developed by the teams had slightly different structures, target audiences, and aims. All resulting social listening systems followed the taught key principles of systematic social listening to collect and analyze data and used these new insights for further development of communication strategies.

Conclusions

This paper describes an infodemic management system and workflow based on qualitative inquiry and adapted to local priorities and resources. The implementation of these projects resulted in content development for targeted risk communication, addressing linguistically diverse populations. These systems can be adapted for future epidemics and pandemics.

A Comparative Study of Natural Language Processing Algorithms Based on Cities Changing Diabetes Vulnerability Data

(1) Background: Poor adherence to management behaviors in Chinese Type 2 diabetes mellitus (T2DM) patients leads to an uncontrolled prognosis of diabetes, which results in significant economic costs for China. It is imperative to quickly locate vulnerability factors in the management behavior of patients with T2DM. (2) Methods: In this study, a thematic analysis of the collected interview materials was conducted to construct the themes of T2DM management vulnerability. We explored the applicability of the pre-trained models based on the evaluation metrics in text classification. (3) Results: We constructed 12 themes of vulnerability related to the health and well-being of people with T2DM in Tianjin. We considered that Bidirectional Encoder Representation from Transformers (BERT) performed better in this Natural Language Processing (NLP) task with a shorter completion time. With the splitting ratio of 6:3:1 and batch size of 64 for BERT, the test accuracy was 97.71%, the completion time was 10 min 24 s, and the macro-F1 score was 0.9752. (4) Conclusions: Our results proved the applicability of NLP techniques in this specific Chinese-language medical environment. We filled the knowledge gap in the application of NLP technologies in diabetes management. Our study provided strong support for using NLP techniques to rapidly locate vulnerability factors in T2DM management.