ABSTRACT
The COVID-19 pandemic has had a profound impact on the global community, and vaccination has been recognized as a crucial intervention. To gain insight into public perceptions of COVID-19 vaccines, survey studies and the analysis of social media platforms have been conducted. However, existing methods lack consideration of individual vaccination intentions or status and the relationship between public perceptions and actual vaccine uptake. To address these limitations, this study proposes a text classification approach to identify tweets indicating a user's intent or status on vaccination. A comparative analysis between the proportions of tweets from different categories and real-world vaccination data reveals notable alignment, suggesting that tweets may serve as a precursor to actual vaccination status. Further, regression analysis and time series forecasting were performed to explore the potential of tweet data, demonstrating the significance of incorporating tweet data in predicting future vaccination status. Finally, clustering was applied to the tweet sets with positive and negative labels to gain insights into underlying focuses of each stance. © 2023 ACM.
ABSTRACT
The sudden onslaught of the COVID-19 pandemic brought people the most serious global public health emergency since the end of World War II. Posing a threat to human survival and development, the pandemic has given prominence to global issues concerning life safety and ecological security and triggered studies on their integrated advancement. Only by fully understanding the importance and urgency of the life-ecology integrated security and establishing the new idea, new strategy, new plan, and basic path can we survive deadly emergencies, build a community with a shared future and health for all, and jointly contribute to and share the benefits of a safe, healthy and beautiful world with common prosperity, long-lasting peace and stability of humanity.
ABSTRACT
The problem of the survival of humankind has acquired a new relevance. If recently the whole world was horrified by the prospects of a nuclear war, now the value priorities have shifted from the technogenic forms of struggle of some states and peoples against others to bio-political constants. Although the issues of viral infection, as well as the social consequences of the use of AI technologies concern all 8 billion earthlings, however, the biopolitics in the era of coronavirus and AI reflects all the same patterns: not solidarity in research and solving problems, but rivalry and absolutization of national interest. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Coronavirus disease 2019 (COVID-19) has become a global pandemic, causing heavy losses to the global community. Novel Coronavirus development speed, the scope of the large, so that biomedical workers have to face together. Meanwhile COVID-19 (SARS-CoV-2) has a strong infection rate as well as certain mortality rate, which brings challenges to the treatment of the disease. Vaccination is an important means to prevent infectious diseases. At present, many research institutions around the world have launched the research as well as advance of COVID-19 vaccines. The current situation of SARS-CoV2 pandemic has triggered development and use of epidemiological vaccines in the medical community. Aiming at vaccines evolution is to acquire straight as proof of vaccines efficient in defend people from SARS-CoV-2 as well as COVID-19 in order to effective vaccine manufacture will be selectively. One vaccine candidate against COVID-19 may fight acute, disease, either contagion, or a vaccine which reduces anyone the elements may aid in disease control. This article introduces the morphological characteristics, mechanism of action, clinical utility, enhance as well as differences of COVID-19 vaccines development, In order to compare mRNA vaccine ， subunit vaccine and attenuated vaccine and studied from multiple medical perspectives to promote the selection and use of vaccine in a safer way and ensure the best efficacy end points, prevent grave pathema and doom, as assessed about phase 3 clinical trials. hoping to help comprehend development as well as vaccination point at COVID-19 vaccine in the future. © 2022 SPIE. All rights reserved.
ABSTRACT
The outbreak of the coronavirus created a conventional challenge for the global community due to the lack increased a number of COVID-19 positive patients and stretched healthcare systems. Therefore, the global world starts to look for unconventional approaches and technologies to combat the dissemination of the COVID-19 virus, for example, utilizing Robotics and Artificial Intelligence. Using robotics for medical services is facing many challenges that may lead to failure. The most important of these challenges is that the healthcare workers are not trained well on using robotics, don't have enough knowledge, awareness of the importance of using robotics especially during pandemics, and the relatively high cost of robotics. Thus, in this paper, we assess the levels of knowledge, attitude, and practices of healthcare workers about using robotics during pandemics. Further, a quantitative and a qualitative approach using a questionnaire to collect data is used. Over 40 healthcare workers participate, who have a fair background about robots. However, they have no idea about using robots in general and their field in particular. © 2022 IEEE.
ABSTRACT
Since the COVID-19 pandemic broke out in early 2020, the global community has been living in fear, stress, and isolation. The COVID-19 vaccine might provide a solution to the ongoing global crisis. This study seeks to monitor the trends of depression that have been discussed on Twitter before and after the COVID-19 vaccine was released and explores whether such differences were universal or geographical. Specifically, this paper investigates the variations in sentiment in different geographic regions and the change of sentiments before and after the vaccine release. We collect tweets containing keywords "COVID-19"and 'depression' and rely on releasing date of the COVID-19 vaccine as a division point. The experiment results reveal that topics related to depression varied significantly across different regions before and after the COVID-19 vaccine was released. For example, tweets posted in America are focused on social lockdown and infection with COVID-19 when referring to depression. In contrast, tweets from European countries discuss more typical depression symptoms and Brexit. The tweets posted in Asian, African, and Oceanian contain more discussions on stress. Our analysis further indicates that Asian users' stress is mainly from the study while Oceanian and African users' stress is primarily from family. Another interesting finding in our paper is that tweets show a common desire for normal social activities after the vaccine release, regardless of geographical locations. © 2022 IEEE.
ABSTRACT
How to evaluate the effectiveness of epidemic prevention measures scientifically and reasonably has become the most urgent task for governments around the world. The rapid spread of the coronavirus has brought great challenges to the global community. In this paper, the effectiveness of the following three epidemic prevention measures, including makeshift hospitals, closed cities, and wearing masks, is evaluated by establishing relevant mathematical models and applying the circulating neural network. For the scenes of closed cities and makeshift hospitals, the improved model of SEIR eight cabins is established in this paper, and the parameters are updated in real-time by Long Short-Term Memory. The following results are obtained: the closed city measures greatly reduce the probability of the transfer of susceptible persons to latent ones, and the epidemic is effectively controlled. Regarding the issue of wearing masks, this paper established the MUEIR model and solved with a particle swarm optimization algorithm. It is concluded that the number of infected people decreased by 42% compared with the natural situation, indicating the effectiveness of wearing masks. In conclusion, the effectiveness of the above three epidemic prevention measures is scientifically evaluated, and artificial intelligence technology is combined to achieve intelligent dynamic prediction of the epidemic development trend. © 2022 IEEE.
ABSTRACT
As the world grapples with COVID-19 pandemic and, with increasing acceptance that climate change poses existential threat, globalization is once again coming to the fore of socioeconomic dialogue. A major lesson from the pandemic incident is that global communities are more entwined, and globalization has come to stay, though pace may be slowed but it cannot be reversed. As global energy sources transits from fossil-fuel dominated system to energy system that accommodates renewables in the mix, both local and global dimensions will become significant in policy formulation and decision making (especially in areas such as energy security, inequality, climate change, economic growth among others). Global trade in energy is increasing, and with advent of renewable resources, the trend is expected to continue. To manage this trend during energy transition, energy producing, and consuming countries must forge enhance cooperation especially in policy formulation and implementation - to ensure energy security for all stakeholders. Increasing disparity in wages and inequality gap will compel national governments to pursue policies that promotes energy transition with economic empowerment through deregulation, decentralization, and digitization. Engineering managers will play pivotal role in the effective implementation of such policies - ensuring seamless interactions of processes (including infrastructure developments that facilitate environmental and economic benefits for all stakeholders). This paper will examine energy transition from the perspective of local content and globalization. In addition, interacting variables, and conflicting dimensions (energy security, climate change, inequality among others) will be discussed. Opportunities (global and regional) for engineering management within the context of energy transition will be highlighted. Overall, the objective of this paper will have been achieved with appropriate awareness created for the engineering management community about opportunities within energy transition. © American Society for Engineering Management, 2021
ABSTRACT
Due to COVID-19, universities with limited expertise with the digital environment had to rapidly transition to online teaching and assessment. This transition did not create a new problem but has offered more opportunities for contract cheating and diversified the types of such services. While universities and lecturers were adjusting to the new teaching styles and developing new assessment methods, opportunistic contract cheating providers have been offering $50 COVID-19 discounts and students securing the services of commercial online tutors to take their online exams or to take advantage of real-time assistance from 'pros' while sitting examinations. The article contributes to the discourse on contract cheating by reporting on an investigation of the scope and scale of the growing problems related to academic integrity exacerbated by an urgent transition to online assessments during the COVID-19 pandemic. The dark reality is the illegal services are developing at a faster pace than the systems required to curb them, as demonstrated by the results. The all-penetrating issues indicate systemic failures on a global scale that cannot be addressed by an individual academic or university acting alone. Multi-level solutions including academics, universities and the global community are essential. Future research must focus on developing a model of collaboration to address this problem on several levels, taking into account (1) individual academics, (2) universities, (3) countries and (4) international communities.
ABSTRACT
BACKGROUND: The COVID-19 pandemic is a worldwide epidemic declared by the world health organization as a public health emergency of concern and consequently inducing huge mental health and psychological reactions. AIMS: This study is aimed to summarize the existing data regarding anxiety, depression, and psychological distress during the covid-19 pandemic among the wider population so that effective intervention strategies will be initiated. METHODS: Pieces of literature that assessed anxiety, depression, and psychological distress among the general population during the COVID pandemic period were systematically gathered. Data extraction in Microsoft excel was done by two independent reviewers using predefined criteria. The analysis was done using a stata-11 and random effect model. A sub-group and sensitivity analysis was done. Besides, the funnel plot and eggers publication bias test was tested. RESULTS: Sixteen studies that assessed 78,225 participants were included. Nine studies were included in the meta-analysis for anxiety prevalence. The average prevalence of anxiety was found to be 38.12%. A sub-group analysis showed that anxiety was 33.33% in China and 47.70% in other countries (Italy, Turkey, and India). Anxiety prevalence in studies measured with the DASS-21 scale, GAD-7 scale, and other tools (SAS, HADS, and 5-point Likert scale) was 23.4%, 40.73%, and 44.47% respectively. The prevalence of anxiety in studies that assessed a sample size above 2,000 participants was 40.33%. The average prevalence of depression among included studies was 34.31% and a sub-group analysis showed that depression was higher in China (36.32%) than in other countries (28.3%). Moreover, six studies reported psychological distress and the average prevalence was 37.54%. CONCLUSION: This study revealed that anxiety, depression, and psychological distress are potential public mental health problems of the global community that suggests the need for early recognition and initiation of interventions during the COVID-19 pandemic period.