COVID-19 anti-vaccine attitude and hesitancy.

High vaccination rates are required around the world to create herd immunity and terminate the current COVID-19 pandemic growth. With the steady rise in COVID-19 vaccine supplies, hesitancy and rejection to be vaccinated has become a problem worldwide for large vaccine coverage. Understanding the causes of vaccine avoidance or hesitancy can help to increase vaccination intentions in the general population. A number of factors contributed to increasing hesitancy. Some causes of COVID-19 vaccine hesitancy include anti-vaccine myths and confusing messages about some severe side effects of few vaccines, confusion over protection levels, poor health literacy (lack of accurate knowledge about vaccines and virus), deficient legal liability from the vaccine manufacturers, political and economic intentions, mistrust and suspicion of medical companies, concern of efficacy against to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants, safety concerns (elderly, people with preexisting comorbidities) and some socio-demographic factors. Urgent interventions and policies targeting the corresponding factors are needed. Recognizing obstacles to vaccine uptake helps in the development of effective solutions to solve them. Evidence-based and behaviorally guided approaches should be used to achieve high acceptance and uptake. Supplementary Information: The online version contains supplementary material available at 10.1007/s40200-022-01018-y.

COVID-19 anti-vaccine attitude and hesitancy

High vaccination rates are required around the world to create herd immunity and terminate the current COVID-19 pandemic growth. With the steady rise in COVID-19 vaccine supplies, hesitancy and rejection to be vaccinated has become a problem worldwide for large vaccine coverage. Understanding the causes of vaccine avoidance or hesitancy can help to increase vaccination intentions in the general population. A number of factors contributed to increasing hesitancy. Some causes of COVID-19 vaccine hesitancy include anti-vaccine myths and confusing messages about some severe side effects of few vaccines, confusion over protection levels, poor health literacy (lack of accurate knowledge about vaccines and virus), deficient legal liability from the vaccine manufacturers, political and economic intentions, mistrust and suspicion of medical companies, concern of efficacy against to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants, safety concerns (elderly, people with preexisting comorbidities) and some socio-demographic factors. Urgent interventions and policies targeting the corresponding factors are needed. Recognizing obstacles to vaccine uptake helps in the development of effective solutions to solve them. Evidence-based and behaviorally guided approaches should be used to achieve high acceptance and uptake. Supplementary Information The online version contains supplementary material available at 10.1007/s40200-022-01018-y.

COVID-19 related stigma among the general population in Iran.

BACKGROUND: COVID-19 related stigma has been identified as a critical issue since the beginning of the pandemic. We developed a valid and reliable questionnaire to measure COVID-19 related enacted stigma, inflicted by the non-infected general population. We applied the questionnaire to measure COVID-19 related enacted stigma among Tehran citizens from 27 to 30 September 2020. METHODS: A preliminary questionnaire with 18 items was developed. The total score ranged from 18 to 54; a higher score indicated a higher level of COVID-19 related stigma. An expert panel assessed the face and content validity. Of 1637 randomly recruited Tehran citizens without a history of COVID-19 infection, 1064 participants consented and were interviewed by trained interviewers by phone. RESULTS: Item content validity index (I-CVI), Item content validity ratio (I-CVR), and Item face validity index (I-FVI) were higher than 0.78 for all 18 items. The content and face validity were established with a scale content validity index (S-CVI) of 0.90 and a scale face validity index (S-CVI) of 93.9%, respectively. Internal consistency of the questionnaire with 18 items was confirmed with Cronbach's alpha of 0.625. Exploratory factor analysis revealed five latent variables, including "blaming", "social discrimination", "dishonor label", "interpersonal contact", and "retribution and requital attitude". The median of the stigma score was 24 [25th percentile: 22, 75the percentile: 28]. A large majority (86.8%) of participants reported a low level of stigma with a score below 31. None of the participants showed a high level of stigma with a score above 43. We found that the higher the educational level the lower the participant's stigma score. CONCLUSION: We found a low level of stigmatizing thoughts and behavior among the non-infected general population in Tehran, which may be due to the social desirability effect, to the widespread nature of COVID-19, or to the adaptation to sociocultural diversity of the large city.

Predictors of COVID-19 Preventive Behavior Adoption Intention in Malaysia.

Preventive behavior adoption is the key to reduce the possibility of getting COVID-19 infection. This paper aims to examine the determinants of intention to adopt preventive behavior by incorporating perception of e-government information and services and perception of social media into the theory of reasoned action. A cross-sectional online survey was carried out among Malaysian residents. Four hundred four valid responses were obtained and used for data analysis. A partial least-square-based path analysis revealed direct effects of attitude and subjective norm in predicting intention to adopt preventive behavior. In addition, perception of e-government information and services and perception of social media were found to be significant predictors of attitude toward preventive behavior. The findings highlight the importance of digital platforms in improving people's attitudes toward preventive behavior and in turn contain the spread of the infectious disease.

COMPLEXITY-BASED ANALYSIS OF THE ALTERATIONS IN THE STRUCTURE OF CORONAVIRUSES

The coronavirus has influenced the lives of many people since its identification in 1960. In general, there are seven types of coronavirus. Although some types of this virus, including 229E, NL63, OC43, and HKU1, cause mild to moderate illness, SARS-CoV, MERS-CoV, and SARS-CoV-2 have shown to have severer effects on the human body. Specifically, the recent known type of coronavirus, SARS-CoV-2, has affected the lives of many people around the world since late 2019 with the disease named COVID-19. In this paper, for the first time, we investigated the variations among the complex structures of coronaviruses. We employed the fractal dimension, approximate entropy, and sample entropy as the measures of complexity. Based on the obtained results, SARS-CoV-2 has a significantly different complex structure than SARS-CoV and MERS-CoV. To study the high mutation rate of SARS-CoV-2, we also analyzed the long-term memory of genome walks for different coronaviruses using the Hurst exponent. The results demonstrated that the SARS-CoV-2 shows the lowest memory in its genome walk, explaining the errors in copying the sequences along the genome that results in the virus mutation. [ABSTRACT FROM AUTHOR] Copyright of Fractals is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Attitude Toward Protective Behavior Engagement During COVID-19 Pandemic in Malaysia: The Role of E-government and Social Media.

The coronavirus disease 2019 (COVID-19) pandemic is still evolving and affecting millions of lives. E-government and social media have been used widely during this unprecedented time to spread awareness and educate the public on preventive measures. However, the extent to which the 2 digital platforms bring to improve public health awareness and prevention during a health crisis is unknown. In this study, we examined the influence of e-government and social media on the public's attitude to adopt protective behavior. For this purpose, a Web survey was conducted among 404 Malaysian residents during the Recovery Movement Control Order (RMCO) period in the country. Descriptive and multiple regression analyses were conducted using IBM SPSS software. Social media was chosen by most of the respondents (n = 331 or 81.9%) as the source to get information related to COVID-19. Multiple regression analysis suggests the roles of e-government and social media to be significantly related to people's attitudes to engage in protective behavior. In conclusion, during the COVID-19 outbreak, public health decision makers may use e-government and social media platforms as effective tools to improve public engagement on protective behavior. This, in turn, will help the country to contain the transmission of the virus.

COMPLEXITY-BASED DETECTION OF SIMILARITY BETWEEN ANIMAL CORONAVIRUSES AND SARS-CoV-2 IN HUMANS

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is the most dangerous type of coronavirus and has infected over 25.3 million people around the world (including causing 848,000 deaths). In this study, we investigated the similarity between the genome walks of coronaviruses in various animals and those of human SARS-CoV-2. Based on the results, although bats show a similar pattern of coronavirus genome walks to that of SARS-CoV-2 in humans, decoding the complex structure of coronavirus genome walks using sample entropy and fractal theory showed that the complexity of the pangolin coronavirus genome walk has a 94% match with the complexity of the SARS-CoV-2 genome walk in humans. This is the first reported study that found a similarity between the hidden characteristics of pangolin coronavirus and human SARS-CoV-2 using complexity-based analysis. The results of this study have great importance for the analysis of the origin and transfer of the virus.

INFORMATION AND COMPLEXITY-BASED ANALYSIS OF THE VARIATIONS OF THE CORONAVIRUS GENOME BETWEEN DIFFERENT COUNTRIES

Coronavirus disease (COVID-19) is a pandemic disease that has had a deadly effect on all countries around the world. Since an essential step in developing a vaccine is to consider genomic variations of a virus, in this research, we analyzed the variations of the coronavirus genome between different countries. For this purpose, we benefit from complexity and information theories. We analyzed the variations of the fractal dimension and Shannon entropy of genome walks for two-hundred samples of coronavirus genomes from 10 countries, including the Czech Republic, France, Thailand, USA, Japan, Taiwan, China, Australia, Greece, and India. The result of the analysis showed the significant variations (P-value=0.0001) in the complexity and information content of genome walks between different countries, and therefore, we conclude that the structure of the coronavirus genome is significantly different among different countries. This is a novel and very significant investigation that should be considered for developing a vaccine for this deadly virus.

COMPLEXITY-BASED CLASSIFICATION OF THE CORONAVIRUS GENOME VERSUS GENOMES OF THE HUMAN IMMUNODEFICIENCY VIRUS (HIV) AND DENGUE VIRUS

Coronavirus disease (COVID-19) is a pandemic disease that has affected almost all around the world. The most crucial step in the treatment of patients with COVID-19 is to investigate about the coronavirus itself. In this research, for the first time, we analyze the complex structure of the coronavirus genome and compare it with the other two dangerous viruses, namely, dengue and HIV. For this purpose, we employ fractal theory, sample entropy, and approximate entropy to analyze the genome walk of coronavirus, dengue virus, and HIV. Based on the obtained results, the genome walk of coronavirus has greater complexity than the other two deadly viruses. The result of statistical analysis also showed the significant difference between the complexity of genome walks in case of all complexity measures. The result of this analysis opens new doors to scientists to consider the complexity of a virus genome as an index to investigate its danger for human life.

COMPLEXITY-BASED CLASSIFICATION OF THE CORONAVIRUS DISEASE (COVID-19)

COVID-19 is a pandemic disease, which massively affected human lives in more than 200 countries. Caused by the coronavirus SARS-CoV-2, this acute respiratory illness affects the human lungs and can easily spread from person to person. Since the disease heavily affects human lungs, analyzing the X-ray images of the lungs may prove to be a powerful tool for disease investigation. In this research, we use the information contained within the complex structures of X-ray images between the cases of COVID-19 and other respiratory diseases, whereas the case of healthy lungs is taken as the reference point. To analyze X-ray images, we benefit from the concept of Shannon's entropy and fractal theory. Shannon's entropy is directly related to the amount of information contained within the X-ray images in question, whereas fractal theory is used to analyze the complexity of these images. The results, obtained in this study, show that the method of fractal analysis can detect the level of infection among different respiratory diseases and that COVID-19 has the worst effect on the human lungs. In other words, the complexity of X-ray images is proportional to the severity of the respiratory disease. The method of analysis, employed in this study, can be used even further to analyze how COVID-19 progresses in affected patients.