Knowledge and attitudes of us adults regarding COVID-19

Willingness to Embrace COVID-19 Vaccination Amongst Residents in a Low-Income Nation: Insights From a Cross-Sectional Study on a Limited Cohort

The global pandemic had a significant impact on countries around the world, both politically and socioeconomically. It is crucial that swift decisions and actions need to be taken to prevent negative outcomes. The development of vaccines has emerged as a potential necessity for countries worldwide. Ethiopia began vaccinating health professionals and high-risk individuals in March 2021, according to a report from the World Health Organization citing the Ethiopian Federal Ministry of Health. This study aimed to assess the determinants of willingness to receive the COVID-19 vaccine among Debre Markos city administration residents. A community-based cross-sectional study design was employed to recruit 845 individuals as a sample. Descriptive statistics and logistic regression were used as statistical analysis techniques. Among the total 845 samples, the overall response rate was 98.34%. Two hundred forty-two participants showed their willingness to receive vaccines. Age (AOR = 2.56; 95%CI = [1.87-3.23]), sex (Female) (AOR = 3.45; 95% CI = [2.07-5.26]), having children (AOR = 1.21; 95% CI = [1.02-1.90]), and Chronic Disease (AOR = 2.98; 95% CI = [1.67-3.50]) were significantly and positively associated with willingness to receive COVID 19 vaccines at 95% CI. Although most of the participants were aware of the possibility of COVID-19 to cause fever; and its transmission, only a small percentage of the total participants showed their willingness to receive the vaccine if it was available to them. Elderly and individuals with chronic diseases need to get a priority of taking those vaccinations.

Vaccine sentiment analysis using BERT + NBSVM and geo-spatial approaches

Since the spread of the coronavirus flu in 2019 (hereafter referred to as COVID-19), millions of people worldwide have been affected by the pandemic, which has significantly impacted our habits in various ways. In order to eradicate the disease, a great help came from unprecedentedly fast vaccines development along with strict preventive measures adoption like lockdown. Thus, world wide provisioning of vaccines was crucial in order to achieve the maximum immunization of population. However, the fast development of vaccines, driven by the urge of limiting the pandemic caused skeptical reactions by a vast amount of population. More specifically, the people's hesitancy in getting vaccinated was an additional obstacle in fighting COVID-19. To ameliorate this scenario, it is important to understand people's sentiments about vaccines in order to take proper actions to better inform the population. As a matter of fact, people continuously update their feelings and sentiments on social media, thus a proper analysis of those opinions is an important challenge for providing proper information to avoid misinformation. More in detail, sentiment analysis (Wankhade et al. in Artif Intell Rev 55(7):5731-5780, 2022. 10.1007/s10462-022-10144-1) is a powerful technique in natural language processing that enables the identification and classification of people feelings (mainly) in text data. It involves the use of machine learning algorithms and other computational techniques to analyze large volumes of text and determine whether they express positive, negative or neutral sentiment. Sentiment analysis is widely used in industries such as marketing, customer service, and healthcare, among others, to gain actionable insights from customer feedback, social media posts, and other forms of unstructured textual data. In this paper, Sentiment Analysis will be used to elaborate on people reaction to COVID-19 vaccines in order to provide useful insights to improve the correct understanding of their correct usage and possible advantages. In this paper, a framework that leverages artificial intelligence (AI) methods is proposed for classifying tweets based on their polarity values. We analyzed Twitter data related to COVID-19 vaccines after the most appropriate pre-processing on them. More specifically, we identified the word-cloud of negative, positive, and neutral words using an artificial intelligence tool to determine the sentiment of tweets. After this pre-processing step, we performed classification using the BERT + NBSVM model to classify people's sentiments about vaccines. The reason for choosing to combine bidirectional encoder representations from transformers (BERT) and Naive Bayes and support vector machine (NBSVM ) can be understood by considering the limitation of BERT-based approaches, which only leverage encoder layers, resulting in lower performance on short texts like the ones used in our analysis. Such a limitation can be ameliorated by using Naive Bayes and Support Vector Machine approaches that are able to achieve higher performance in short text sentiment analysis. Thus, we took advantage of both BERT features and NBSVM features to define a flexible framework for our sentiment analysis goal related to vaccine sentiment identification. Moreover, we enrich our results with spatial analysis of the data by using geo-coding, visualization, and spatial correlation analysis to suggest the most suitable vaccination centers to users based on the sentiment analysis outcomes. In principle, we do not need to implement a distributed architecture to run our experiments as the available public data are not massive. However, we discuss a high-performance architecture that will be used if the collected data scales up dramatically. We compared our approach with the state-of-art methods by comparing most widely used metrics like Accuracy, Precision, Recall and F-measure. The proposed BERT + NBSVM outperformed alternative models by achieving 73% accuracy, 71% precision, 88% recall and 73% F-measure for classification of positive sentiments while 73% accuracy, 71% precision, 74% recall and 73% F-measure for classification of negative sentiments respectively. These promising results will be properly discussed in next sections. The use of artificial intelligence methods and social media analysis can lead to a better understanding of people's reactions and opinions about any trending topic. However, in the case of health-related topics like COVID-19 vaccines, proper sentiment identification could be crucial for implementing public health policies. More in detail, the availability of useful findings on user opinions about vaccines can help policymakers design proper strategies and implement ad-hoc vaccination protocols according to people's feelings, in order to provide better public service. To this end, we leveraged geospatial information to support effective recommendations for vaccination centers.

Vaccination rates among international students: Insights from a university health vaccination initiative

Objective: To examine the effects of a university's health vaccination initiative in increasing vaccination rates among international students/scholars in the United States. Methods: The vaccination initiative included: increasing vaccination opportunities by holding a pre-registration event, providing vaccine recommendations from healthcare professionals including a bilingual health interpreter, implementing campus-based marketing strategies, sending reminders using social media, and offering free and affordable vaccines. Results: Total 575 international students/scholars attended from 2016 to 2019 (N = 118, 163, 193, and 101, respectively), showing an increase compared to 2015. The most common vaccines administered were for influenza, human papillomavirus (HPV), tetanus, diphtheria, and acellular pertussis (Tdap), and Hepatitis A. Slightly less than one-quarter of participants received three or more vaccines. More women than men received HPV vaccine. Participants shared they would not have been vaccinated without this initiative and wished for more vaccination events. Conclusions: Future efforts are needed to implement this initiative across universities to further evaluate its effectiveness.

Sentimental and spatial analysis of COVID-19 vaccines tweets

The world has to face health concerns due to huge spread of COVID. For this reason, the development of vaccine is the need of hour. The higher vaccine distribution, the higher the immunity against coronavirus. Therefore, there is a need to analyse the people's sentiment for the vaccine campaign. Today, social media is the rich source of data where people share their opinions and experiences by their posts, comments or tweets. In this study, we have used the twitter data of vaccines of COVID and analysed them using methods of artificial intelligence and geo-spatial methods. We found the polarity of the tweets using the TextBlob() function and categorized them. Then, we designed the word clouds and classified the sentiments using the BERT model. We then performed the geo-coding and visualized the feature points over the world map. We found the correlation between the feature points geographically and then applied hotspot analysis and kernel density estimation to highlight the regions of positive, negative or neutral sentiments. We used precision, recall and F score to evaluate our model and compare our results with the state-of-the-art methods. The results showed that our model achieved 55% & 54% precision, 69% & 85% recall and 58% & 64% F score for positive class and negative class respectively. Thus, these sentimental and spatial analysis helps in world-wide pandemics by identify the people's attitudes towards the vaccines.

Human sentiments monitoring during COVID-19 using AI-based modeling

The whole world is facing health challenges due to wide spread of COVID-19 pandemic. To control the spread of COVID-19, the development of its vaccine is the need of hour. Considering the importance of the vaccines, many industries have put their efforts in vaccine development. The higher immunity against the COVID can be achieved by high intake of the vaccines. Therefore, it is important to analysis the people's behaviour and sentiments towards vaccines. Today is the era of social media, where people mostly share their emotions, experience, or opinions about any trending topic in the form of tweets, comments or posts. In this study, we have used the freely available COVID-19 vaccines dataset and analysed the people reactions on the vaccine campaign using artificial intelligence methods. We used TextBlob() function of python and found out the polarity of the tweets. We applied the BERT model and classify the tweets into negative and positive classes based on their polarity values. The classification results show that BERT has achieved maximum values of precision, recall and F score for both positive and negative sentiment classification.

Utilization Intention of Community Pharmacy Service under the Dual Threats of Air Pollution and COVID-19 Epidemic: Moderating Effects of Knowledge and Attitude toward COVID-19

The utilization of pharmacy services in response to the threat of COVID-19 infection remains unclear in areas suffering from air pollution, and little is known regarding the effects of knowledge and attitude (KA) toward COVID-19 on this preventive behavior. This study aimed to explore how the residents perceived and reacted to the new threats of the epidemic and how KA may affect the correlation. Based on the health belief model (HBM), this research took the pharmacy service utilization (PSU) as an example to explain the preventive behavior. The samples were 375 respondents recruited from five districts near the industrial parks. T-test, ANOVA, and regression analyses of SPSS 22.0 were used to analyze the data. Test results show that self-efficacy was the strongest predictor, followed by the net perceived benefit. KA moderated the association of perceived threat and PSU intention. The levels of air pollution of a district may not be a good predictor for the preventive behavior against COVID-19.

COVID-19 Vaccination Acceptance among Healthcare Workers and General Population at the Very Beginning of the National Vaccination Program in Poland: A Cross-Sectional, Exploratory Study

The aim of the study was to assess the acceptance level of COVID-19 vaccination among healthcare workers (HCW) and the general population in Poland at the start of the national COVID-19 vaccination program from 18-31 December 2020. A cross-sectional anonymous survey was conducted in a group of 1976 people: 1042 health professionals and 934 non-medical professionals using an on-line questionnaire. The most skeptical about the COVID-19 vaccine were students of non-medical faculties, non-medical professions, and administrative-technical health service staff (26.2%, 38.7% and 41.2%, respectively). The most positive attitude to vaccination was reported by doctors, medical students and pharmacists (80.6%, 76.9% and 65.7%, respectively). Doctors (64.7%) and medical students (63.7%) most often declared confidence in vaccines compared to nurses (34.5%). Distrust about vaccine safety was declared by nurses (46.6%) and pharmacists (40.0%). HCW encouraged others to vaccinate more eagerly (65.8%) than non-medical professions (28.3%). Thus, a considerable proportion of HCW in Poland expressed concern about vaccines just prior to the beginning of the COVID-19 immunization program. The significant decrease in the willingness to vaccinate observed in Poland towards the end of 2021 must be considered in the light of the serious COVID-19 vaccination hesitancy in the Polish population.

College students' sleep difficulty during COVID-19 and correlated stressors: A large-scale cross-sessional survey study

Objective

Sleep difficulty is one of the main concerns during the COVID-19 pandemic. This study examined factors related to vaccination and physical and psychological health conditions, and sleep difficulty in college students in China.

Methods

An online, cross-sectional, anonymous survey was used to investigate college students' perceived sleep difficulty and relevant components (i.e., physical health condition, psychological distress, knowledge of vaccine, and autonomy of vaccine uptake). Hierarchical ordinal logistic regression was conducted to examine the proposed model with the control of participants' demographics (i.e., gender and age).

Results

Valid data of 3,145 students from 43 universities in mainland China was collected in January 2021. The average age of participants was 20.8 years old (S. D. = 2.09). The majority were single (97.4%), and about half were male (49.8%). Results showed that participants had less psychological distress when they had more knowledge about the COVID-19 vaccine and more autonomy to decide whether to receive it. In addition, participants with better physical health experienced less sleep difficulty. In contrast, those with more psychological distress experienced more sleep difficulty.

Conclusions

These findings can inform healthcare providers about the relationship between different factors and difficulty sleeping and aid them in developing interventions addressing sleep difficulties associated with the global pandemic. Health authorities also can improve vaccine uptake and reduce hesitancies in future vaccination campaigns based on the study results showing that greater vaccine knowledge and autonomy reduced psychological distress.

Worldwide Vaccination Willingness for COVID-19: A Systematic Review and Meta-Analysis

Countries across the globe are currently experiencing a third or fourth wave of SARS-CoV-2 infections; therefore, the need for effective vaccination campaigns is higher than ever. However, effectiveness of these campaigns in disease reduction is highly dependent on vaccination uptake and coverage in susceptible populations. Therefore, this systematic review and meta-analysis estimated the vaccination intention and identified determinants of willingness and hesitancy. This study updates the existing body of literature on vaccination willingness, and was conducted according to the PRISMA guidelines. PubMed was searched for publications, selecting only studies published between 20 October 2020 and 1 March 2021, in English, with participants aged >16 years of age. The search identified 411 articles, of which 63 surveys were included that accounted for more than 30 countries worldwide. The global COVID-19 vaccination willingness was estimated at 66.01% [95% CI: 60.76–70.89% I² = 99.4% [99.3%; 99.4%]; τ² = 0.83]. The vaccination willingness varied within as well as between countries. Age, gender, education, attitudes and perceptions about vaccines were most frequently observed to be significantly associated with vaccine acceptance or refusal.

A Cross-sectional Online Survey on Public Attitudes towards Wearing Face Masks and Washing Hands to Prevent the Spread of COVID-19 in Indonesia

BACKGROUND: The COVID-19 infection spreads quickly and easily, hence people are required to obey health protocols. Attitudes play an important role in building people’s readiness to use face masks and wash hands. AIM: The study aims at analyzing several factors influencing people’s attitudes towards wearing face masks and washing hands in Indonesia. METHODS: The study employs a cross-sectional online survey involving 400 adult respondents in the Java region from July to September 2020. RESULTS: Of 400 respondents, 54.3% showed positive attitudes toward wearing face masks and 59.3% towards washing hands. The multivariate analysis showed that people’s attitudes towards wearing face masks were influenced by age and knowledge. Respondents aged 36–45 years old had positive attitudes on wearing face masks 3.9 times (p = 0.038) and aged ≥46 years old 4.1 times (p = 0.039) compared to aged 18–35 years old. Furthermore, attitudes on washing hands were influenced by gender, age groups, and knowledge. Female had positive attitudes towards washing hands 1.7 times (p = 0.029) compared to male. Respondents aged 36–45 years old had positive attitudes on washing hands 5 times (p = 0.037) and aged ≥46 years old 4.8 times (p = 0.05) compared to aged 18–35 years old. Knowledge acted as the confounding factor. CONCLUSION: The age and knowledge factors influenced positive attitude of using masks and washing hands were influenced by gender, age, and knowledge. Health education programs are recommended to increase knowledge about COVID-19, this is very helpful for the young generation of Indonesia to have a positive attitude.

Assessing Attitude Toward COVID-19 Vaccination in South Korea

Vaccines are the most effective strategy to safeguard against COVID-19 and it is crucial to assess community acceptance of COVID-19 vaccination. This exploratory study aimed to assess the attitude of immigrants toward the acceptance of COVID-19 vaccines in South Korea. A web-based anonymous study was completed by 463 immigrants. The data were statistically analyzed using a logistic regression model and ANOVA test. On a scale of 0-6, the average attitude toward the COVID-19 vaccination was 4.17 ± 1.73, indicating generally positive attitudes. The proportion of the immigrants who were certain to get COVID-19 vaccination was 55.3%. Only 36.7% reported that the COVID-19 vaccines are safe. Of the immigrants, 72.6% showed high acceptance and 27.4% low acceptance toward the COVID-19 vaccines. Vaccine safety concern was the major predictor for COVID-19 vaccine acceptance. Up-to-date, valid information on COVID-19 vaccine safety, and vaccine risk communication strategies are required to increase vaccine acceptability.

Knowledge, Attitudes and Perceptions of COVID-19 Vaccination among Healthcare Workers of an Inner-City Hospital in New York

Introduction: New York City is one of the areas most affected by the COVID-19 pandemic in the United States. Healthcare workers are among those at high risk of contracting the virus, and a vital source of information and trust in vaccines to the community. Methods: This study was conducted about attitudes towards COVID-19 vaccination among healthcare workers at a public hospital in New York City during the beginning of COVID-19 vaccination. 428 hospital employees responded. Results: Several factors were significantly associated with vaccine attitudes, including demographics such as gender (p = 0.002), age (p = 0.005), race (p < 0.001) and home location (p < 0.001), role within the hospital (p < 0.001), knowledge about the virus (p < 0.001) and confidence in and expectations about personal protective equipment and behaviors (p < 0.001). Structural equation modeling revealed that the most predictive factors were prior vaccine attitudes and concern with the speed of testing and approval of the vaccines (p < 0.001). Multivariate analysis reinforced these, while also identifying perceived personal risk as significant (p = 0.033). Conclusions: Several modifiable factors that reflect confidence in science, scientific knowledge, personal risk perception, experience and medical authority are correlated with vaccine attitudes, indicating that a holistic educational approach to improve trust in science is likely to be effective in long-term reduction in vaccine hesitancy.