Media Bias Matters: Understanding the Impact of Politically Biased News on Vaccine Attitudes in Social Media (preprint)

News media has been utilized as a political tool to stray from facts, presenting biased claims without evidence. Amid the COVID-19 pandemic, politically biased news (PBN) has significantly undermined public trust in vaccines, despite strong medical evidence supporting their efficacy. In this paper, we analyze: (i) how inherent vaccine stances subtly influence individuals' selection of news sources and participation in social media discussions; and (ii) the impact of exposure to PBN on users' attitudes toward vaccines. In doing so, we first curate a comprehensive dataset that connects PBN with related social media discourse. Utilizing advanced deep learning and causal inference techniques, we reveal distinct user behaviors between social media groups with various vaccine stances. Moreover, we observe that individuals with moderate stances, particularly the vaccine-hesitant majority, are more vulnerable to the influence of PBN compared to those with extreme views. Our findings provide critical insights to foster this line of research.

Challenges for COVID-19 surveillance through wastewater-based epidemiology in post-pandemic era: A retrospective study in 222 USA counties (preprint)

The post-pandemic world still faces ongoing COVID-19 infections, where wastewater-based epidemiology (WBE) is recognized as an efficient tool for the population-wide surveillance of COVID-19 infections. Utilizing weekly county-level wastewater-surveillance data during pandemic across 222 counties from 49 states in United States of America (USA) from June 2021-November 2022 and covering 104 M populations, we retrospectively evaluated the correlation between SARS-CoV-2 RNA concentrations in wastewater (CRNA) and reported cases as well as the impacts of demographics, socioeconomical, test accessibility, epidemiological, environmental factors and international air travelling on reported cases under the corresponding CRNA. The lift of travel restrictions in June 2022, a milestone for the post-pandemic era, shifted the correlation between CRNA and COVID-19 incidence in following 7-day and 14-day from 0.70 (IQR: 0.30-0.88) and 0.74 (IQR: 0.31-0.90) to 0.01 (IQR: -0.31-0.36) and -0.01 (IQR: -0.38-0.45), respectively. In post-pandemic period, under the same CRNA, the reported case numbers were impacted by international passengers, test accessibility, Omicron prevalence, ratio of population aged between 18-65, minority vulnerability, and healthcare system. These factors bring new challenges in post-pandemic era, which needs additional attention while using WBE surveillance for COVID-19 infections. This study facilitates public health authorities to dynamically adjust their WBE to the local context to achieve optimal community surveillance.

STANCE-C3: Domain-adaptive Cross-target Stance Detection via Contrastive Learning and Counterfactual Generation (preprint)

Stance detection is the process of inferring a person's position or standpoint on a specific issue to deduce prevailing perceptions toward topics of general or controversial interest, such as health policies during the COVID-19 pandemic. Existing models for stance detection are trained to perform well for a single domain (e.g., COVID-19) and a specific target topic (e.g., masking protocols), but are generally ineffectual in other domains or targets due to distributional shifts in the data. However, constructing high-performing, domain-specific stance detection models requires an extensive corpus of labeled data relevant to the targeted domain, yet such datasets are not readily available. This poses a challenge as the process of annotating data is costly and time-consuming. To address these challenges, we introduce a novel stance detection model coined domain-adaptive Cross-target STANCE detection via Contrastive learning and Counterfactual generation (STANCE-C3) that uses counterfactual data augmentation to enhance domain-adaptive training by enriching the target domain dataset during the training process and requiring significantly less information from the new domain. We also propose a modified self-supervised contrastive learning as a component of STANCE-C3 to prevent overfitting for the existing domain and target and enable cross-target stance detection. Through experiments on various datasets, we show that STANCE-C3 shows performance improvement over existing state-of-the-art methods.

PTSD and its relationship with the Fear of COVID-19 and COVID-19 burden among health care workers after the full liberalization of COVID-19 prevention and control policy in China: a cross-sectional study (preprint)

Background Over the past three years, the COVID-19 pandemic has brought an overwhelming impact on China's hospital system and health care workers, and can lead to post-traumatic stress disorder (PTSD). Previous research has shown that during the COVID-19 pandemic, there will be long-term negative impacts on healthcare workers' mental health. Indeed, PTSD has emerged as one of the significant mental health issues for health care workers arising from the COVID-19 pandemic. The status of PTSD of healthcare workers directly affects the quality of national medical and health services. Therefore, it is vital to understand the PTSD situation of healthcare workers in the post-pandemic era.Methods This study was conducted three years after the global COVID-19 pandemic (January 15 to January 16, 2023). The study was administered through a web-based survey platform. The PC-PTSD-5 Chinese Version Scale, Fear of COVID-19 Scale(FCV-19S), Social support Scale, COVID-19 burden Scale, GAD-2, and PHQ-2 were used by the Questionnaire Star platform.Results The prevalence of PTSD in health care workers was 24.3% (232/955). depress(P < 0.001), anxiety(P < 0.05), the Fear of COVID-19(P < 0.001), and COVID-19 burden(P < 0.001) were highly correlated with PTSD in health care workers. Social support(P < 0.05) was a protective factor of smartphone addiction.Conclusions This survey shows that PTSD was highly prevalent among Chinese healthcare workers after the COVID-19 pandemic. Governments and leaders of medical institutions should through psychological interventions to address the current situation of PTSD among health care workers and develop targeted programs and strategies to reduce their psychological problems.

Wastewater-based epidemiology predicts COVID-19-induced hospital and ICU admission numbers in over 100 USA counties (preprint)

With the ease of coronavirus disease (COVID-19) emergency status globally, a population-wide low-cost prediction for COVID-19-induced hospitalization and intensive care unit (ICU) admission numbers is essential for healthcare systems. For the first time, we evaluated the feasibility of using wastewater-based epidemiology (WBE) to predict COVID-19-induced hospitalization and ICU admission numbers in 102 counties across 42 states in the United States of America (USA), covering a population of nearly 60 million, through random forest models using the county-level weekly wastewater surveillance data (over 15 months). WBE-based models accurately predicted the admission numbers, allowing a preparation window of 5-28 days. In real applications, periodically updated WBE-based models showed good accuracy and transferability, with mean absolute error within 20 and 2 patients/100k population for upcoming hospitalization and ICU admission numbers in 28 days, respectively. Our study demonstrated the potential of using WBE as a cost-effective method to provide early warnings for healthcare systems.

Prevalence and Predictors of Smartphone Addiction among Medical Students in China during the COVID-19 pandemic: A cross-sectional study (preprint)

Objective Smartphone addiction is a common condition among medical students. Recent studies have reported the prevalence of Smartphone addiction among students, but there are fewer studies on the situation and influencing factors of smartphone addiction among medical students during the COVID-19 pandemic. In our study, we aimed to assess the prevalence of Chinese medical students’ smartphone addiction and its Predictors. Method A cross-sectional survey was conducted from March 13 to 30, 2022, by administering an online questionnaire to the students of Wannan Medical College. Data analysis was conducted using IBM SPSS ver. 20.0. Results Of 3227 students who completed the survey, 1,968 (61.0%) had smartphone addiction. Gender (p=0.000), adaptation to the online learning (p=0.001), perceived study pressure (p=0.000), high psychological stress (p=0.000), academic burnout (p=0.000) and eHealth Literacy (p=0.000) strong associations of smartphone addiction. PSS-4 and academic burnout were highly correlated with smartphone addiction （P<0.001）. EHealth Literacy was a protective factor of smartphone addiction. Conclusion This survey shows that the Chinese medical students had a very high rate of smartphone addiction during the COVID-19 pandemic. The COVID-19 pandemic has brought about changes in the way medical students learn and increased associated, which has exacerbated the already existing smartphone addiction among medical college students. Therefore, medical colleges educators develop educational strategies to reduce smartphone addiction is essential.

Prevalence and factors associated with insomnia among medical students in China during the COVID-19 pandemic: characterization and associated factors (preprint)

Background Insomnia has become an important issue in recent years. Insomnia is affected by many factors. Previous research has shown that during the COVID-19 epidemic, there would be a long-term negative effect on the mental health of medical college students. The state of medical college students’ insomnia directly determines the results of medical education and the career development prospects of the medical students themselves. Therefore, it is very important to understand the insomnia situation of medical students in the post-epidemic era. Methods This study was conducted 2 years after the global COVID-19 pandemic (April 1-April 23, 2022). The study used an online questionnaire, administered through a web-based survey platform. The Athens Insomnia Scale (AIS), Fear of COVID‐19 Scale (FCV-19S), GAD-2, PHQ-2, and socio-demographic information were surveyed by the Questionnaire Star platform. Results The prevalence of insomnia was 27.8.0% (636/2289). Chi-squared tests revealed that, loneliness(P<0.001), depression and anxiety co-morbidities(P<0.001), age(P<0.001), and grade(P<0.001) strong associations of insomnia. Fear of COVID-19 was highly correlated with insomnia (P<0.001). adapting to online classes(P<0.001) was a protective factor of smartphone addiction. Conclusions The aim of this study was to investigate the prevalence of insomnia and identify associated influencing factors among college students in Anhui Province, China. This survey shows that Insomnia was highly prevalent among the Chinese medical college students during the COVID-19 pandemic. These negative insomnia issues could decline learning enthusiasm, productivity, and eventually, affecting the medical college student academic performance. Therefore, the government and schools should formulate targeted programs and strategies to reduce insomnia among medical college students.

Improving Vaccine Stance Detection by Combining Online and Offline Data (preprint)

Differing opinions about COVID-19 have led to various online discourses regarding vaccines. Due to the detrimental effects and the scale of the COVID-19 pandemic, detecting vaccine stance has become especially important and is attracting increasing attention. Communication during the pandemic is typically done via online and offline sources, which provide two complementary avenues for detecting vaccine stance. Therefore, this paper aims to (1) study the importance of integrating online and offline data to vaccine stance detection; and (2) identify the critical online and offline attributes that influence an individual's vaccine stance. We model vaccine hesitancy as a surrogate for identifying the importance of online and offline factors. With the aid of explainable AI and combinatorial analysis, we conclude that both online and offline factors help predict vaccine stance.

CoVaxNet: An Online-Offline Data Repository for COVID-19 Vaccine Hesitancy Research (preprint)

Despite the astonishing success of COVID-19 vaccines against the virus, a substantial proportion of the population is still hesitant to be vaccinated, undermining governmental efforts to control the virus. To address this problem, we need to understand the different factors giving rise to such a behavior, including social media discourses, news media propaganda, government responses, demographic and socioeconomic statuses, and COVID-19 statistics, etc. However, existing datasets fail to cover all these aspects, making it difficult to form a complete picture in inferencing about the problem of vaccine hesitancy. In this paper, we construct a multi-source, multi-modal, and multi-feature online-offline data repository CoVaxNet. We provide descriptive analyses and insights to illustrate critical patterns in CoVaxNet. Moreover, we propose a novel approach for connecting online and offline data so as to facilitate the inference tasks that exploit complementary information sources.

A Population-Level Analysis of Changes in Circadian Rhythms and Sleep and Their Association with Negative Emotions during the Outbreak of COVID-19 in China

Since 2019, COVID-19 began spreading globally and has significantly affected peoples' daily lifestyles. The public was asked to stay at home for constant quarantine and community containment starting on 23 January 2020. To assess the circadian rhythms and sleep changes and their influential factors during the COVID-19 outbreak, a questionnaire was administered to 451 Chinese participants during 20–31 January 2020. The changes in circadian rhythm, sleep–wake cycle, dining, and exercise of the participants and their correlation with negative emotions were analyzed. Furthermore, the effects of three factors (holiday, quarantine, and concerns regarding the pandemic situation) on these changes were assessed. We found that 34.6% of the participants reported circadian rhythm disturbance. Moreover, 67.2% presented negative emotions (worry, fear, downheartedness, anxiety, depression, and stupefaction) regarding the pandemic situation, among which worry was the most prevalent. Gender and age were significant factors for changes in the circadian phases and emotions. There was a correlation between circadian rhythm alterations and negative emotions. In addition, holiday, quarantine, and concerns regarding the pandemic situation had significant effects on circadian rhythms and sleep in a substantial part of the population. Regression analysis demonstrated reciprocal influences between many of these variables. Our findings suggest that circadian rhythms, sleep, and negative emotions in the normal population need to be considered during the pandemic period and that the adjustment of circadian rhythms could help promote sleep, restore emotions and improve public health.

Anti-Inflammatory Effects of Camellia fascicularis Polyphenols via Attenuation of NF-κB and MAPK Pathways in LPS-Induced THP-1 Macrophages

Purpose Plant polyphenols possess beneficial functions against various diseases. This study aimed to identify phenolic ingredients in Camellia fascicularis (C. fascicularis) and investigate its possible underlying anti-inflammatory mechanism in lipopolysaccharide (LPS)-induced human monocytes (THP-1) macrophages. Methods C. fascicularis polyphenols (CFP) were characterized by ultra-performance liquid chromatography (UPLC) combined with quadrupole-time-of-flight mass/mass spectrometry (Q-TOF-MS/MS). The THP-1 cells were differentiated into macrophages under the stimulation of phorbol 12-myristate 13-acetate (PMA) and then treated with LPS to build a cellular inflammation model. The cell viability was detected by CCK-8 assay. The levels of reactive oxygen species (ROS) were assessed by flow cytometry. The secretion and expression of inflammatory cytokines were tested by enzyme-linked immunosorbent assay (ELISA) and real-time polymerase chain reaction (RT-PCR). In addition, the nuclear factor-kappa B (NF-κB) and mitogen-activated protein kinase (MAPK) signaling pathways were analyzed by Western blotting. Results Twelve phenolic constituents including (–)-epicatechin, casuariin, agastachoside, etc. in CFP were identified. The CCK-8 assay showed that CFP exhibited no significant cytotoxicity between 100 and 300 μg/mL. After treated with CFP, the release of ROS was significantly suppressed. CFP inhibited inflammation in macrophages by attenuating the polarization of LPS-induced THP-1 macrophages, down-regulating the expression of the pro-inflammatory cytokines IL-6, IL-1β and TNF-α, and up-regulating the expression of the anti-inflammatory cytokine IL-10. Western blotting experiments manifested that CFP could markedly inhibit the phosphorylation of p65, ERK and JNK, thereby suppressing the activation of NF-κB and MAPK signaling pathways. Conclusion These findings indicated that CFP exerted anti-inflammatory activity by inhibiting the activation NF-κB and MAPK pathways which may induce the secretion of pro-inflammatory cytokines. This study offers a reference for C. fascicularis as the source of developing natural, safe anti-inflammatory agents in the future.

Attenuated replication and pathogenesis of SARS-CoV-2 B.1.1.529 Omicron (preprint)

SARS-CoV-2 Omicron emerged in November 2021 and is rapidly spreading among the human populations. The variant contains 34 changes in its spike protein including 15 substitutions at the receptor-binding domain (RBD). While recent reports reveal that the Omicron variant can robustly escape from vaccine and therapeutic neutralization antibodies, the pathogenicity of the virus remains unknown. Here, we investigate the virological features and pathogenesis of the Omicron variant using in vitro and in vivo models. Our results demonstrate that the replication of the Omicron variant is dramatically attenuated in Calu3 and Caco2 but not in VeroE6 cells. Further mechanistic investigations reveal that the Omicron variant is deficient in transmembrane serine protease 2 (TMPRSS2) usage in comparison to that of WT, Alpha, Beta, and Delta variant, which explained its inefficient replication in Calu3 and Caco2 cells. Importantly, the replication of the Omicron variant is markedly attenuated in both the upper and lower respiratory tract of infected K18-hACE2 mice in comparison to that of WT and Delta variant, which results in its dramatically ameliorated lung pathology. When compared with SARS-CoV-2 WT, Alpha, Beta, and Delta variant, infection by the Omicron variant causes the least body weight loss and mortality rate. Overall, our study demonstrates that the Omicron variant is significantly attenuated in virus replication and pathogenicity in comparison with WT and previous variants. Our data suggest the current global vaccination strategy has forced SARS-CoV-2 into a new evolutionary trajectory towards reduced replication fitness in exchange of better immune escape. These findings are critical for setting policy in the pandemic control and disease management of COVID-19.

Comparative analysis of single cell lung atlas of bat, cat, tiger and pangolin (preprint)

Horseshoe bats (Rhinolophus sinicus) might help maintain coronaviruses severely affecting human health, such as SARS-CoV and SARS-CoV-2. It has long been suggested that bats may be more tolerant of viral infection than other mammals due to their unique immune system, but the exact mechanism remains to be fully explored. During the COVID-19 pandemic, multiple animal species were diseased by SARS-CoV-2 infection, especially in the respiratory system. Herein, single-cell transcriptomic data of the lungs of a horseshoe bat, a cat, a tiger, and a pangolin were generated. The receptor distribution of twenty-eight respiratory viruses belonging to fourteen viral families were characterized for the four species. Comparison on the immune-related transcripts further revealed limited cytokine activations in bats, which might explain the reason why bats experienced only mild diseases or even no symptoms upon virus infection. Our findings might increase our understanding of the immune background of horseshoe bats and their insensitivity to virus infections.

A Survey on Echo Chambers on Social Media: Description, Detection and Mitigation (preprint)

Echo chambers on social media are a significant problem that can elicit a number of negative consequences, most recently affecting the response to COVID-19. Echo chambers promote conspiracy theories about the virus and are found to be linked to vaccine hesitancy, less compliance with mask mandates, and the practice of social distancing. Moreover, the problem of echo chambers is connected to other pertinent issues like political polarization and the spread of misinformation. An echo chamber is defined as a network of users in which users only interact with opinions that support their pre-existing beliefs and opinions, and they exclude and discredit other viewpoints. This survey aims to examine the echo chamber phenomenon on social media from a social computing perspective and provide a blueprint for possible solutions. We survey the related literature to understand the attributes of echo chambers and how they affect the individual and society at large. Additionally, we show the mechanisms, both algorithmic and psychological, that lead to the formation of echo chambers. These mechanisms could be manifested in two forms: (1) the bias of social media's recommender systems and (2) internal biases such as confirmation bias and homophily. While it is immensely challenging to mitigate internal biases, there has been great efforts seeking to mitigate the bias of recommender systems. These recommender systems take advantage of our own biases to personalize content recommendations to keep us engaged in order to watch more ads. Therefore, we further investigate different computational approaches for echo chamber detection and prevention, mainly based around recommender systems.

Screening of cell-virus, cell-cell, gene-gene interactions among kingdoms of life at single cell resolution (preprint)

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) issued a significant and urgent threat to global health. The exact animal origin of SARS-CoV-2 remains obscure and understanding its host range is vital for preventing interspecies transmission. Previously, we have assessed the target cell profiles of SARS-CoV-2 in pets, livestock, poultry and wild animals. Herein, we expand this investigation to a wider range of animal species and viruses to provide a comprehensive source for large-scale screening of potential virus hosts. Single cell atlas for several mammalian species (alpaca, hamster, hedgehog, chinchilla etc.), as well as comparative atlas for lung, brain and peripheral blood mononuclear cells (PBMC) for various lineages of animals were constructed, from which we systemically analyzed the virus entry factors for 113 viruses over 20 species from mammalians, birds, reptiles, amphibians and invertebrates. Conserved cellular connectomes and regulomes were also identified, revealing the fundamental cell-cell and gene-gene cross-talks between these species. Overall, our study could help identify the potential host range and tissue tropism of SARS-CoV-2 and a diverse set of viruses and reveal the host-virus co-evolution footprints.

Viral receptor profiles of masked palm civet revealed by single-cell transcriptomics (preprint)

Civets are small mammals belonging to the family Viverridae. The masked palm civets (Paguma larvata) served as an intermediate host in the bat-to-human transmission of severe acute respiratory syndrome coronavirus (SARS-CoV) in 2003. Because of their unique role in the SARS outbreak, civets were suspected as a potential intermediate host of SARS-CoV-2, the etiological pathogen of the COVID-19 pandemic. Besides their susceptibility to coronaviruses, civets can also be infected by other viruses, such as canine distemper viruses, parvoviruses, influenza viruses, etc. Regarding the ecological and economical role of civets, it is vital to evaluate the potential threats from different pathogens to these animals. Receptor binding is a necessary step for virus entry into host cells. Understanding the distribution of receptors of various viruses provides hints to their potential tissue tropisms. Herein, we characterized the cell atlas of five important organs (the frontal lobe, lung, liver, spleen and kidney) of masked palm civets (Paguma larvata) and described the expression profiles of receptor associated genes of 132 viruses from 25 families, including 16 viruses from 10 families reported before that can attack civets and 116 viruses with little infection record.

Mechanisms and Attributes of Echo Chambers in Social Media (preprint)

Echo chambers may exclude social media users from being exposed to other opinions, therefore, can cause rampant negative effects. Among abundant evidence are the 2016 and 2020 US presidential elections conspiracy theories and polarization, as well as the COVID-19 disinfodemic. To help better detect echo chambers and mitigate its negative effects, this paper explores the mechanisms and attributes of echo chambers in social media. In particular, we first illustrate four primary mechanisms related to three main factors: human psychology, social networks, and automatic systems. We then depict common attributes of echo chambers with a focus on the diffusion of misinformation, spreading of conspiracy theory, creation of social trends, political polarization, and emotional contagion of users. We illustrate each mechanism and attribute in a multi-perspective of sociology, psychology, and social computing with recent case studies. Our analysis suggest an emerging need to detect echo chambers and mitigate their negative effects.

Prevalence and Factors Associated with Post-Traumatic Stress Disorder in Healthcare Workers Exposed to COVID-19 in Wuhan, China: A Cross-Sectional Survey (preprint)

Background: The COVID-19 pandemic has posed significant threats to both physical and psychological health of healthcare workers working in the front-line combating COVID-19. However, evidence regarding the long-term impact of COVID-19 is limited. Therefore, we conducted this cross-sectional survey to investigate the prevalence, factors and impact of post-traumatic stress disorder (PTSD) in healthcare workers exposed to COVID-19 eight months after end of outbreak in Wuhan, China. Methods: A web-based questionnaire was delivered as a link via the communication application WeChat to those healthcare workers who worked at several COVID-19 units in Wuhan during the outbreak (from November 2019 to April 2020). The questionnaire included questions on social-demographic data, the post-traumatic stress disorder checklist-5, the family care index questionnaire, and the quality of life scale. The prevalence, risk and protective factors, and impact of post-traumatic stress disorder healthcare workers were then analyzed with logistic regression.Results: Among the 659 participants, 90 healthcare workers were still suffering from PTSD eight months after the end of outbreak of COVID-19 in Wuhan, in which avoidance and negative impact were the most affected dimensions. Suffering from chronic disease, experiencing social isolation, and job dissatisfaction came up as independent risk factors of PTSD, while obtaining COVID-19 related information at an appropriate frequency, good family function, and working in well-prepared mobile cabin hospitals surfaced as protective factors. The impact of PTSD on COVID-19-exposed healthcare workers was apparent by shortened sleeping time, feeling of loneliness, poorer quality of life and intention to resign.Conclusions: Eight months after the end of the COVID-19 outbreak in Wuhan, the level of PTSD in healthcare workers exposed to COVID-19 was still high. Apart from the common recognized risk factors, comorbid of chronic disease was identified as the new independent risk factors of developing PTSD. For countries where the pandemic is still ongoing or in the case of future outbreaks of new communicable diseases, this research may contribute to preventing cases of PTSD in healthcare workers exposed to infectious diseases under such circumstances.

The expression of PDCD1 and CD274 in T cells and macrophages correlated positively with COVID-19 severity (preprint)

The immune responses underlying the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remain unclear. To help understand the pathology of coronavirus disease 2019 (COVID-19) pandemics, public data were analyzed and the expression of PDCD1 (encoding PD-1) and CD274 (encoding PD-L1) in T cells and macrophages were identified to correlate positively with COVID-19 severity.

MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformation (preprint)

The COVID-19 epidemic is considered as the global health crisis of the whole society and the greatest challenge mankind faced since World War Two. Unfortunately, the fake news about COVID-19 is spreading as fast as the virus itself. The incorrect health measurements, anxiety, and hate speeches will have bad consequences on people's physical health, as well as their mental health in the whole world. To help better combat the COVID-19 fake news, we propose a new fake news detection dataset MM-COVID(Multilingual and Multidimensional COVID-19 Fake News Data Repository). This dataset provides the multilingual fake news and the relevant social context. We collect 3981 pieces of fake news content and 7192 trustworthy information from English, Spanish, Portuguese, Hindi, French and Italian, 6 different languages. We present a detailed and exploratory analysis of MM-COVID from different perspectives and demonstrate the utility of MM-COVID in several potential applications of COVID-19 fake news study on multilingual and social media.