COVID-19 Tweet Links: A Preliminary Investigation of Type and Relevance.

We conducted an exploratory study of the links found in Twitter tweets. Our results showed that the largest category of tweet links was social media platforms followed by alternative news sites. Government agencies and educational institutions were under-represented. In terms of relevance, about 75% of the links were related to COVID-19 but disappointingly, only 40% of the links were directly related to their respective tweets' topics.

Examining COVID-19 Tweet Diffusion Using an Integrated Social Amplification of Risk and Issue-Attention Cycle Framework.

Drawing upon the social amplification of risk (SARF) and the issue-attention cycle framework, we examined the amplification of COVID-19 risk-related tweets through (a) topics: key interests of discussion; (b) temperament: emotions of tweets; (c) topography (i.e., location); and (d) temporality (i.e., over time). We computationally analyzed 1,641,273 tweets, and conducted manual content analysis on a subset of 6,000 tweets to identify how topics, temperament, and topography of COVID-19 tweets were associated with risk amplification - retweet and favorite count - using negative binomial regression. We found 11 dominant COVID-19 topics-health impact, economic impact, reports of lockdowns, report of new cases, the need to stay home, coping with COVID-19, news about President Trump, government support, fight with COVID-19 by non-government entities, origins, and preventive measure in our corpus of tweets across the issue-attention cycle. The negative binomial regression results showed that at the pre-problem stage, topics on President Trump, speculation of origins, and initiatives to fight COVID-19 by non-government entities were most likely to be amplified, underscoring the inherent politicization of COVID-19 and erosion of trust in governments from the start of the pandemic. We also found that while tweets with negative emotions were consistently amplified throughout the issue-attention cycle, surprisingly tweets with positive emotions were amplified during the height of the pandemic - this counter-intuitive finding indicated signs of premature and misplaced optimism. Finally, our results showed that the locations of COVID-19 tweet amplification corresponded to the shifting COVID-19 hotspots across different continents across the issue-attention cycle. Theoretical and practical implications of risk amplification on social media are discussed.

How do knowledge diversity and ego-network structures affect firms' sustainable innovation: evidence from alliance innovation networks of China's new energy industries

Purpose>Facing the global public health emergency (GPHE), the conflict of cultural differences and the imbalance of vital resources such as knowledge among different organizations are becoming more severe, which affects the enthusiasm and sustainability of firms' innovation heavily. It is an urgent problem to be solved for firms how to make use of internal knowledge and external power to help firms' sustainable innovation (FSI). Thus, the purpose of this study is to deeply analyze how firms' internal knowledge diversity (KD) and external ego-network structures [ego-network density (ED) and honest brokers (HB)] affect FSI, as well as how the ego-network structures (ED and HB) moderate the relationship between KD and FSI based on the perspective of the ego network.Design/methodology/approach>Based on the data of the alliance innovation networks of China's new energy industries in 2009–2019, this study uses the social network analysis method and negative binomial regression model to explore the effect of KD and ego-network structures (ED and HB) on FSI, as well as the moderating effects of ego-network structures (ED and HB) on the relationship between KD and FSI based on the perspective of ego network.Findings>This study finds that KD, ED and HB can boost FSI. Moreover, ED plays a negative moderating role in the relationship between KD and FSI. However, the negative moderating effect of HB on the relationship between KD and FSI is not significant.Research limitations/implications>This study presents fresh empirical evidence and new insights for firms on how to make full use of firms' internal KD and external ego-network structures to facilitate FSI.Originality/value>First, this study not only enriches the research on the consequences of KD but also expands our understanding of the knowledge-based view to some extent. Second, this study not only enriches the motivation research of the FSI based on the perspective of ego-network in the context of the GPHE but also expands the application scope of social network theory and sustainable innovation' theory in part. Third, this paper is a new attempt to apply social network theory and knowledge-based view at the same time.

Autoimmune hepatitis after COVID-19 vaccination.

Vaccination is one of the most vigorous ways to intervene in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Cases of autoimmune hepatitis (AIH) after coronavirus disease (COVID-19) vaccination have been increasingly reported. Twenty-seven cases of AIH are summarized in this study, providing emerging evidence of autoimmune reactions in response to various COVID-19 vaccines, including in patients with special disease backgrounds such as primary sclerosing cholangitis (PSC), liver transplantation, and previous hepatitis C virus (HCV) treatment. Molecular mimicry, adjuvants, epitope spreading, bystander activation, X chromosome, and sceptical hepatotropism of SARS-CoV-2 may account for, to some extent, such autoimmune phenomena. Immunosuppressive corticosteroids perform well with or without azathioprine in such post-COVID-19-vaccination AIH. However, determination of the exact mechanism and establishment of causality require further confirmation.

Linking the Pathway from Exposure to Online Vaccine Information to Cyberchondria During the COVID-19 Pandemic: A Moderated Mediation Model.

Cyberchondria describes excessive health information seeking on the Internet is associated with escalating concerns and anxiety. Drawing upon the stimulus-organism-response model, this study proposes a moderated mediation model to explore how people develop cyberchondria when they search for COVID-19 vaccine-related information on the Internet. To test the proposed model, an online survey was conducted in China. Results showed that there was a direct and positive relationship between exposure to online vaccine information and cyberchondria. This relationship was also partially mediated by perceived information overload. Moreover, e-health literacy negatively moderated the indirect relationship between exposure to online vaccine information and cyberchondria through perceived information overload. The results from this study can advance our knowledge on the development of cyberchondria during public health crises, and inform health professionals and social media operators on developing evidence-based interventions to manage this issue.

Linking online vaccine information seeking to vaccination intention in the context of the COVID-19 pandemic. (Theme Issue: Processing COVID information.)

This study examines how online vaccine information seeking is related to vaccination intention in the United States and China during the initial stage of their coronavirus disease 2019 vaccination programs. Analysis of the pooled sample showed a positive relationship between online vaccine information seeking and vaccination intention. There was also a negative indirect effect via perceived information overload, vaccine risk perception, and negative affective response. Multigroup analysis revealed differences between the United States and China. This study highlights the bright and dark sides of online health information during a global pandemic and has practical implications for communication campaigns to promote health-related behaviors.

Linking Online Vaccine Information Seeking to Vaccination Intention in the Context of the COVID-19 Pandemic

This study examines how online vaccine information seeking is related to vaccination intention in the United States and China during the initial stage of their coronavirus disease 2019 vaccination programs. Analysis of the pooled sample showed a positive relationship between online vaccine information seeking and vaccination intention. There was also a negative indirect effect via perceived information overload, vaccine risk perception, and negative affective response. Multigroup analysis revealed differences between the United States and China. This study highlights the bright and dark sides of online health information during a global pandemic and has practical implications for communication campaigns to promote health-related behaviors. [ FROM AUTHOR] Copyright of Science Communication is the property of Sage Publications Inc. 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 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 . (Copyright applies to all s.)

Super Dominant Pathobiontic Bacteria in the Nasopharyngeal Microbiota Cause Secondary Bacterial Infection in COVID-19 Patients.

Coronavirus disease 2019 (COVID-19) is a respiratory infectious disease responsible for many infections worldwide. Differences in respiratory microbiota may correlate with disease severity. Samples were collected from 20 severe and 51 mild COVID-19 patients. High-throughput sequencing of the 16S rRNA gene was used to analyze the bacterial community composition of the upper and lower respiratory tracts. The indices of diversity were analyzed. When one genus accounted for >50% of reads from a sample, it was defined as a super dominant pathobiontic bacterial genus (SDPG). In the upper respiratory tract, uniformity indices were significantly higher in the mild group than in the severe group (P < 0.001). In the lower respiratory tract, uniformity indices, richness indices, and the abundance-based coverage estimator were significantly higher in the mild group than in the severe group (P < 0.001). In patients with severe COVID-19, SDPGs were detected in 40.7% of upper and 63.2% of lower respiratory tract samples. In patients with mild COVID-19, only 10.8% of upper and 8.5% of lower respiratory tract samples yielded SDPGs. SDPGs were present in both upper and lower tracts in seven patients (35.0%), among which six (30.0%) patients possessed the same SDPG in the upper and lower tracts. However, no patients with mild infections had an SDPG in both tracts. Staphylococcus, Corynebacterium, and Acinetobacter were the main SDPGs. The number of SDPGs identified differed significantly between patients with mild and severe COVID-19 (P < 0.001). SDPGs in nasopharyngeal microbiota cause secondary bacterial infection in COVID-19 patients and aggravate pneumonia. IMPORTANCE The nasopharyngeal microbiota is composed of a variety of not only the true commensal bacterial species but also the two-face pathobionts, which are one a harmless commensal bacterial species and the other a highly invasive and deadly pathogen. In a previous study, we found that the diversity of nasopharyngeal microbiota was lost in severe influenza patients. We named the genus that accounted for over 50% of microbiota abundance as super dominant pathobiontic genus, which could invade to cause severe pneumonia, leading to high fatality. Similar phenomena were found here for SARS-CoV-2 infection. The diversity of nasopharyngeal microbiota was lost in severe COVID-19 infection patients. SDPGs in nasopharyngeal microbiota were frequently detected in severe COVID-19 patients. Therefore, the SDPGs in nasopharynx microbiota might invade into low respiratory and be responsible for secondary bacterial pneumonia in patients with SARS-CoV-2 infection.

Water demand prediction optimization method in Shenzhen based on the zero-sum game model and rolling revisions

In this study, a deep learning model based on zero-sum game (ZSG) was proposed for accurate water demand prediction. The ensemble learning was introduced to enhance the generalization ability of models, and the sliding average was designed to solve the non-stationarity problem of time series. To solve the problem that the deep learning model could not predict water supply fluctuations caused by emergencies, a hypothesis testing method combining Student's t-test and discrete wavelet transform was proposed to generate the envelope interval of the predicted values to carry out rolling revisions. The research methods were applied to Shenzhen, a megacity with extremely short water resources. The research results showed that the regular bidirectional models were superior to the unidirectional model, and the ZSG-based bidirectional models were superior to the regular bidirectional models. The bidirectional propagation was conducive to improving the generalization ability of the model, and ZSG could better guide the model to find the optimal solution. The fluctuations in water supply were mainly caused by the floating population, but the fluctuation was still within the envelope interval of the predicted values. The predicted values after rolling revisions were very close to the measured values.

Understanding the Temporal Effects on Tweetcussion of COVID-19 Vaccine.

In the fight against COVID-19, the Pfizer and BioNTech vaccine announcement marked a significant turning point. Analysing the topics discussed surrounding the announcement is critical to shed light on how people respond to the vaccination against COVID-19. Specifically, since the COVID-19 vaccine was developed at unprecedented speed, different segments of the public with a different understanding of the issues may react and respond differently. We analysed Twitter tweets to uncover the issues surrounding people's discussion of the vaccination against COVID-19. Through the use of Latent Dirichlet Allocation (LDA), nine topics were identified pertaining to vaccine-related tweets. We analysed the temporal differences in the nine topics, prior and after the official vaccine announcement.

Understanding the effects of message cues on COVID-19 information sharing on Twitter.

Analyzing and documenting human information behaviors in the context of global public health crises such as the COVID-19 pandemic are critical to informing crisis management. Drawing on the Elaboration Likelihood Model, this study investigates how three types of peripheral cues-content richness, emotional valence, and communication topic-are associated with COVID-19 information sharing on Twitter. We used computational methods, combining Latent Dirichlet Allocation topic modeling with psycholinguistic indicators obtained from the Linguistic Inquiry and Word Count dictionary to measure these concepts and built a research model to assess their effects on information sharing. Results showed that content richness was negatively associated with information sharing. Tweets with negative emotions received more user engagement, whereas tweets with positive emotions were less likely to be disseminated. Further, tweets mentioning advisories tended to receive more retweets than those mentioning support and news updates. More importantly, emotional valence moderated the relationship between communication topics and information sharing-tweets discussing news updates and support conveying positive sentiments led to more information sharing; tweets mentioning the impact of COVID-19 with negative emotions triggered more sharing. Finally, theoretical and practical implications of this study are discussed in the context of global public health communication.

Factors influencing COVID-19 vaccination intention: The roles of vaccine knowledge, vaccine risk perception, and doctor-patient communication.

OBJECTIVE: This study aims to investigate factors influencing COVID-19 vaccination intention in the United States. METHODS: A cross-sectional survey was conducted with 800 respondents recruited from an online panel managed by a survey company. Path analysis was employed to examine the relationships between the study variables. RESULTS: First, perceived susceptibility to COVID-19 vaccine side effects was negatively associated with vaccination intention whereas perceived severity did not show any significant impact. Second, vaccine-related knowledge was not directly related to vaccination intention, but it had an indirect and positive effect on vaccination intention via decreasing perceived susceptibility. Third, doctor-patient communication strengthened the negative effect of vaccine knowledge on perceived susceptibility and severity. CONCLUSION: The results of this study offer insights on how to increase people's vaccination intention and reduce their psychological concerns when making COVID-19 vaccine-related decisions. PRACTICE IMPLICATIONS: Government agencies should actively promote the effectiveness and importance of vaccination, while addressing concerns about vaccine safety in the public; Health initiatives also need to enhance the level of knowledge about COVID-19 vaccines through various media channels; Doctors can start the conversations about COVID-19 vaccination with their patients at the point of care and/or via online communication platforms.

Case Report: Identification of SARS-CoV-2 in Cerebrospinal Fluid by Ultrahigh-Depth Sequencing in a Patient With Coronavirus Disease 2019 and Neurological Dysfunction.

We reported that the complete genome sequence of SARS-Coronavirus-2 (SARS-CoV-2) was obtained from a cerebrospinal fluid (CSF) sample by ultrahigh-depth sequencing. Fourteen days after onset, seizures, maxillofacial convulsions, intractable hiccups and a significant increase in intracranial pressure developed in an adult coronavirus disease 2019 patient. The complete genome sequence of SARS-CoV-2 obtained from the cerebrospinal fluid indicates that SARS-CoV-2 can invade the central nervous system. In future, along with nervous system assessment, the pathogen genome detection and other indicators are needed for studying possible nervous system infection of SARS-CoV-2.

Uncovering temporal differences in COVID-19 tweets.

In the fight against the COVID-19 pandemic, understanding how the public responds to various initiatives is an important step in assessing current and future policy implementations. In this paper, we analyzed Twitter tweets using topic modeling to uncover the issues surrounding people's discussion of the disease. Our focus was on temporal differences in topics, prior and after the declaration of COVID-19 as a pandemic. Nine topics were identified in our analysis, each of which showed distinct levels of discussion over time. Our results suggest that as the pandemic progresses, the concerns of the public vary as new developments come to light.

Online InformationSeeking and Disease Prevention Intent During COVID-19 Outbreak

Guided by the risk information seeking and processing (RISP) model, this study aims to examine the key determinants that predispose individuals? online information seeking behavior and prevention intent during the COVID-19 outbreak. Through an online survey with 741 respondents in China, results indicate that affective responses, informational subjective norms, and information insufficiency are positively related to online information seeking about COVID-19. Furthermore, online information seeking is positively associated with prevention intent, and attitude toward prevention partially mediates this association. Finally, theoretical and practical implications of this study are discussed in the context of COVID-19.