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This paper explores a contingent sub-public sphere promoted by a group of former critical journalists in China during the coronavirus outbreak at the beginning of 2020. During the initial outbreak of coronavirus in China, the observed former journalists, who left the field of critical journalism during the re-structure of Chinese news industries under Xi Jinping's leadership, drew on their knowledge of Chinese politics and censorship to contest information cover-up by the state, online censorship, and the state's discourses of China as a successful model of combating the virus. Their activities effectively engaged mass participation of Chinese people and promoted critical opinions of the public. The paper bears the primary aim to deepen the understanding of the episodic formation of elite-driven public spheres in China by investigating the nuances of each case. I therefore, in this particular case study, explore concepts of habitus and capital from Bourdieu's field theory as analytical tools in the investigation of the persistence of political participation and government scrutiny by former critical journalists on social media after leaving the field of critical journalism. The empirical research combines materials collected through 15 former critical journalists' WeChat accounts between January and May 2020, along with in-depth interviews. [ABSTRACT FROM AUTHOR]

This paper investigates, for the first time, the presence of financial contagion among several important Chinese coronavirus concept-based stock indices during the recent COVID-19 global pandemic. We utilize a regime-switching skew-normal (RSSN) methodology to test for contagion through the correlation and coskewness channels while considering structural breaks in the different moments. Our results present evidence of contagion effects, which are robust across identified crisis and non-crisis periods, including that of the Wuhan lockdown. Our empirical results offer investors and policy-makers an additional layer of information when evaluating response mechanisms to major crises through the use of concept-based indices. [ABSTRACT FROM AUTHOR]

This paper investigates the expected results of the current COVID-19 outbreak to arrivals of Chinese tourists to the USA and Australia. The growing market share of Chinese tourism and the fact that the county was the first to experience the pandemic make China a suitable proxy for predictions on global tourism. We employ data from the 2003 SARS outbreak to train a deep learning artificial neural network named Long Short Term Memory (LSTM). The neural network is calibrated for the particulars of the current pandemic. Our findings, which are cross-validated using backtesting, suggest that recovery of arrivals to pre-crisis levels can take from 6 to 12 months and this can have significant adverse effects not only on the tourism industry but also on other sectors that interact with it. [ABSTRACT FROM AUTHOR]

Legacy media worldwide face challenges from both digital platforms and new media rivalries. China offers alternative views and modes of journalistic practices in response to the worldwide "professionalism" crisis. The outbreak of the COVID-19 pandemic has provided legacy media with an opportunity to reassert journalistic professionalism at a critical moment. Taking the market-oriented media outlet Caixin as a case, this study explores how Chinese journalists articulate their professional roles, values and ideals in the coverage of COVID-19. Through discourse analysis, we find that Caixin has defended its authority and legitimacy as quality journalism by incorporating "self-reflexivity" and "transparency" in their professional practices in the digital age. In response to the global journalism crisis, while Chinese state media have adopted the Marxist view, positivity and "conditioned autonomy" to counter the Western "professionalism", market-oriented media represented by Caixin have turned inward to "reflexivity" and "transparency" as a compromising strategy in their professional conventions. [ABSTRACT FROM AUTHOR]

The identification in China in December 2019 of a new coronavirus (SARS-CoV-2) immediately rekindled the spotlight on a problem also addressed in the past during the epidemics of SARS in 2002–2003 and MERS in 2012: the implications of a possible infection during pregnancy, both for pregnant women and for fetuses and infants. Pregnancy is characterized by some changes involving both the immune system and the pulmonary physiology, exposing the pregnant woman to a greater susceptibility to viral infections and more serious complications. The objective of this review is therefore to analyze the relationship between pregnancy and known coronaviruses, with particular reference to SARS-CoV-2. [ABSTRACT FROM AUTHOR]

The prevalence of and risk factors for uncertainty stress among residents during the COVID-19 pandemic remain unclear. An online cross-sectional survey was conducted to explore and identify the risk factors for high perceived uncertainty stress among the general public in China during the COVID-19 outbreak. Information about the respondents' socioeconomic characteristics, knowledge of and attitudes towards COVID-19, perceived uncertainty stress, social capital, anxiety, and depressive symptoms was collected and analysed. Among the 1205 respondents, 45.3% (546) reported a high level of uncertainty stress. Multiple linear regression analysis indicated that anxiety (β=3.871,P<0.001) and depression symptoms (β=2.458, P<0.001), family residence (in towns or rural areas) (β=0.947, P<0.001), lack of support for local epidemic control strategies (β=1.253, P<0.001), worry about the pandemic (β=1.191, P<0.001), and symptoms of weakness among family members (β=1.525, P=0.002) were positively associated with perceived uncertainty stress. Cognitive social capital (β=-0.883, P<0.001) and social networks (β=-0.726, P<0.001) were negatively, but social participation (β=0.714, P<0.001) was positively associated with perceived uncertainty stress. Our findings identify factors associated with a higher level of uncertainty stress and should be helpful in the consideration of effective policies and interventions for uncertainty stress during the initial phases of public health emergencies. [ABSTRACT FROM AUTHOR]

Building a national system of social governance (guojia zhili tixi), which is the long-running governance dream of Xi Jinping, has triggered the creation of China's 'smart state' using the tools of new information technologies to advance governance capacity (zhili nengli). These systems were already deployed nationally when the COVID-19 pandemic hit China, but were connected at a lesser capacity, targeting specific domains of security, industry or government administration. In response to the crisis, multiple technologies have been merged, exceeding the scope of their originally intended functions. This is known as function creep, when surveillant technologies remain functional past achievement of their intended purpose, or surveillant assemblages, where multiple surveillant technologies are combined. As more countries turn to digitalisation to increase public security and intensify social and market governance, the expansion of surveillant functions in China and their now-palpable effects on people's lives raises new and pressing questions for scholars and decision-makers alike. [ABSTRACT FROM AUTHOR]

Objectives: Research during prior virus outbreaks has examined vulnerability factors associated with increased anxiety and fear.Design: We explored numerous psychopathology, sociodemographic, and virus exposure-related variables associated with anxiety and perceived threat of death regarding COVID-19.Method: We recruited 908 adults from Eastern China for a cross-sectional web survey, from 24 February to 15 March 2020, when social distancing was heavily enforced in China. We used several machine learning algorithms to train our statistical model of predictor variables in modeling COVID-19-related anxiety, and perceived threat of death, separately. We trained the model using many simulated replications on a random subset of participants, and subsequently externally tested on the remaining subset of participants.Results: Shrinkage machine learning algorithms performed best, indicating that stress and rumination were the most important variables in modeling COVID-19-related anxiety severity. Health anxiety was the most potent predictor of perceived threat of death from COVID-19.Conclusions: Results are discussed in the context of research on anxiety and fear from prior virus outbreaks, and from theory on outbreak-related emotional vulnerability. Implications regarding COVID-19-related anxiety are also discussed. [ABSTRACT FROM AUTHOR]