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As the COVID-19 pandemic continues to unfold, the approach of the Chinese government remains under the spotlight, obscuring the complex landscape of responses to the outbreak within the country. Drawing upon the author's social media experiences as well as textual analysis of a wide range of sources, this paper explores how the Chinese public responded to the outbreak in complex and nuanced ways through social media. The findings challenge conventional views of Chinese social media as simply sites of self-censorship and surveillance. On the contrary, during the COVID-19 outbreak, social media became spaces of active public engagement, in which Chinese citizens expressed care and solidarity, engaged in claim-making and resistance, and negotiated with authorities. This paper situates this public engagement within a broader context of China's health-care reforms, calling attention to persistent structural and political issues, as well as the precarious positionalities of health-care workers within the health system. [ABSTRACT FROM AUTHOR]

Background: Many models are published which predict outcomes in hospitalized COVID-19 patients. The generalizability of many is unknown. We evaluated the performance of selected models from the literature and our own models to predict outcomes in patients at our institution.Methods: We searched the literature for models predicting outcomes in inpatients with COVID-19. We produced models of mortality or criticality (mortality or ICU admission) in a development cohort. We tested external models which provided sufficient information and our models using a test cohort of our most recent patients. The performance of models was compared using the area under the receiver operator curve (AUC).Results: Our literature review yielded 41 papers. Of those, 8 were found to have sufficient documentation and concordance with features available in our cohort to implement in our test cohort. All models were from Chinese patients. One model predicted criticality and seven mortality. Tested against the test cohort, internal models had an AUC of 0.84 (0.74-0.94) for mortality and 0.83 (0.76-0.90) for criticality. The best external model had an AUC of 0.89 (0.82-0.96) using three variables, another an AUC of 0.84 (0.78-0.91) using ten variables. AUC's ranged from 0.68 to 0.89. On average, models tested were unable to produce predictions in 27% of patients due to missing lab data.Conclusion: Despite differences in pandemic timeline, race, and socio-cultural healthcare context some models derived in China performed well. For healthcare organizations considering implementation of an external model, concordance between the features used in the model and features available in their own patients may be important. Analysis of both local and external models should be done to help decide on what prediction method is used to provide clinical decision support to clinicians treating COVID-19 patients as well as what lab tests should be included in order sets. [ABSTRACT FROM AUTHOR]