Social support and connective affordances: Examining responses to early Covid-19 patient support seeking on microblogs

Focusing on the new Super-Topics Platform (STP) of Weibo, this study examined microblog users' responses to the support seeking of early Covid-19 patients suffering the first outbreak in China. A total of 853 patients' support-seeking messages, along with 81,000 comments to 270 patients' help-requests, were crawled and analyzed. Results showed that content characteristics influenced endorsing, sharing, and commenting by users. Furthermore, the study identified three types of social support present in Weibo viewers' comments: emotional, informational, and diffusional supports. These social support types were inherently linked to the connective affordances, which are more inclusive than paralinguistic digital affordances, of microblogs. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

COVID-19 Vaccines on TikTok: A Big-Data Analysis of Entangled Discourses.

Focusing on social media affordances and China's social/political context, the present study analyzed the digital communication practices about COVID-19 vaccines on a popular social media platform-TikTok-which is called DouYin in China. Overall, this study identified five major forces partaking in constructing the discourses, with government agencies and state media being the dominant contributors. Furthermore, video posters demonstrated different patterns of utilizing social media affordances (e.g., hashtags) in disseminating their messages. The top hashtags adopted by state media were more representative of international relations and Taiwan; those by government agencies were of updates on pandemic outbreaks; those by individual accounts were of mainstream values and health education; those by commercial media were of celebrities and health education; those by enterprise accounts were of TikTok built-in marketing hashtags. The posted videos elicited both cognitive and affective feedback from online viewers. Implications of the findings were discussed in the context of health communication and global recovery against the backdrop of the COVID-19 pandemic and Chinese culture.

Evolution of the PB1 gene of human influenza A (H3N2) viruses circulating between 1968 and 2019.

One avian H3N2 influenza virus, providing its PB1 and HA segments, reassorted with one human H2N2 virus and caused a pandemic outbreak in 1968, killing over 1 million people. After its introduction to humanity, the pandemic H3N2 virus continued adapting to humans and has resulted in epidemic outbreaks every influenza season. To understand the functional roles of the originally avian PB1 gene in the circulating strains of human H3N2 influenza viruses, we analyzed the evolution of the PB1 gene in all human H3N2 isolates from 1968 to 2019. We found several specific residues dramatically changed around 2002-2009 and remained stable through to 2019. Then, we verified the functions of these PB1 mutations in the genetic background of the early pandemic virus, A/Hong Kong/1/1968(HK/68), as well as a recent seasonal strain, A/Jiangsu/34/2016 (JS/16). The PB1 V709I or PB1 V113A/K586R/D619N/V709I induced higher polymerase activity of HK/68 in human cells. And the four mutations acted cooperatively that had an increased replication capacity in vitro and in vivo at an early stage of infection. In contrast, the backward mutant, A113V/R586K/N619D/I709V, reduced polymerase activity in human cells. The PB1 I709V decreased viral replication in vitro, but this mutant only showed less effect on mice infection experiment, which suggested influenza A virus evolved in human host was not always consisted with highly replication efficiency and pathogenicity in other mammalian host. Overall, our results demonstrated that the identified PB1 mutations contributed to the viral evolution of human influenza A (H3N2) viruses.

Modeling COVID-19 Vaccine Adverse Effects with a Visualized Knowledge Graph Database.

In this study, we utilized ontology and machine learning methods to analyze the current results on vaccine adverse events. With the VAERS (Vaccine Adverse Event Reporting System) Database, the side effects of COVID-19 vaccines are summarized, and a relational/graph database was implemented for further applications and analysis. The adverse effects of COVID-19 vaccines up to March 2022 were utilized in the study. With the built network of the adverse effects of COVID-19 vaccines, the API can help provide a visualized interface for patients, healthcare providers and healthcare officers to quickly find the information of a certain patient and the potential relationships of side effects of a certain vaccine. In the meantime, the model was further applied to predict the key feature symptoms that contribute to hospitalization and treatment following receipt of a COVID-19 vaccine and the performance was evaluated with a confusion matrix method. Overall, our study built a user-friendly visualized interface of the side effects of vaccines and provided insight on potential adverse effects with ontology and machine learning approaches. The interface and methods can be expanded to all FDA (Food and Drug Administration)-approved vaccines.

Prediction on transmission trajectory of COVID-19 based on particle swarm algorithm.

This study aimed to predict the transmission trajectory of the 2019 Corona Virus Disease (COVID-19). The particle swarm optimization (PSO) algorithm was combined with the traditional susceptible exposed infected recovered (SEIR) infectious disease prediction model to propose a SEIR-PSO prediction model on the COVID-19. In addition, the domestic epidemic data from February 25, 2020 to March 20, 2020 in China were selected as the training set for analysis. The results showed that when the conversion rate, recovery rate, and mortality rate of the SEIR-PSO model were 1/5, 1/15, and 1/13, its predictive effect on the number of people diagnosed with COVID-19 was the closest to the real data; and the SEIR-PSO model showed a mean-square errors (MSE) value of 1304.35 and mean absolute error (MAE) value of 1069.18, showing the best prediction effect compared with the susceptible infectious susceptible (SIS) model and the SEIR model. In contrary to the standard particle swarm optimization (SPSO) and linear weighted particle swarm optimization (LPSO), which were two classical improved PSO algorithms, the reliability and diversity of the SEIR-PSO model were higher. In summary, the SEIR-PSO model showed excellent performance in predicting the time series of COVID-19 epidemic data, and showed reliable application value for the prevention and control of COVID-19 epidemic.

A systematic review of prediction methods for emergency management

With the trend of global warming and destructive human activities, the frequent occurrences of catastrophes have posed devastating threats to human life and social stability worldwide. The emergency management (EM) system plays a significant role in saving people's lives and reducing property damage. The prediction system for the occurrence of emergency events and resulting impacts is widely recognized as the first stage of the EM system, the accuracy of which has a significant impact on the efficiency of resource allocation, dispatching, and evacuation. In fact, the number and variety of contributions to prediction techniques, such as statistic analysis, artificial intelligence, and simulation method, are exploded in recent years, motivating the need for a systematic analysis of the current works on disaster prediction. To this end, this paper presents a systematic review of contributions on prediction methods for emergency occurrence and resource demand of both natural and man-made disasters. Through a detailed discussion on the features of each type of emergency event, this paper presents a comprehensive survey of state-of-the-art prediction technologies which have been widely applied in EM. After that, we summarize the challenges of current efforts and point out future directions.

Country Image in COVID-19 Pandemic: A Case Study of China.

Country image has a profound influence on international relations and economic development. In the worldwide outbreak of COVID-19, countries and their people display different reactions, resulting in diverse perceived images among foreign public. Therefore, in this article, we take China as a specific and typical case and investigate its image with aspect-based sentiment analysis on a large-scale Twitter dataset. To our knowledge, this is the first study to explore country image in such a fine-grained way. To perform the analysis, we first build a manually-labeled Twitter dataset with aspect-level sentiment annotations. Afterward, we conduct the aspect-based sentiment analysis with BERT to explore the image of China. We discover an overall sentiment change from non-negative to negative in the general public, and explain it with the increasing mentions of negative ideology-related aspects and decreasing mentions of non-negative fact-based aspects. Further investigations into different groups of Twitter users, including U.S. Congress members, English media, and social bots, reveal different patterns in their attitudes toward China. This article provides a deeper understanding of the changing image of China in COVID-19 pandemic. Our research also demonstrates how aspect-based sentiment analysis can be applied in social science researches to deliver valuable insights.

Severity and Consolidation Quantification of COVID-19 From CT Images Using Deep Learning Based on Hybrid Weak Labels.

Early and accurate diagnosis of Coronavirus disease (COVID-19) is essential for patient isolation and contact tracing so that the spread of infection can be limited. Computed tomography (CT) can provide important information in COVID-19, especially for patients with moderate to severe disease as well as those with worsening cardiopulmonary status. As an automatic tool, deep learning methods can be utilized to perform semantic segmentation of affected lung regions, which is important to establish disease severity and prognosis prediction. Both the extent and type of pulmonary opacities help assess disease severity. However, manually pixel-level multi-class labelling is time-consuming, subjective, and non-quantitative. In this article, we proposed a hybrid weak label-based deep learning method that utilize both the manually annotated pulmonary opacities from COVID-19 pneumonia and the patient-level disease-type information available from the clinical report. A UNet was firstly trained with semantic labels to segment the total infected region. It was used to initialize another UNet, which was trained to segment the consolidations with patient-level information using the Expectation-Maximization (EM) algorithm. To demonstrate the performance of the proposed method, multi-institutional CT datasets from Iran, Italy, South Korea, and the United States were utilized. Results show that our proposed method can predict the infected regions as well as the consolidation regions with good correlation to human annotation.