The application of network agenda setting model during the COVID-19 pandemic based on latent dirichlet allocation topic modeling

Based on Network Agenda Setting Model, this study collected 42,516 media reports from Party Media, commercial media, and We Media of China during the COVID-19 pandemic. We trained LDA models for topic clustering through unsupervised machine learning. Questionnaires (N = 470) and social network analysis methods were then applied to examine the correlation between media network agendas and public network agendas in terms of explicit and implicit topics. The study found that the media reports could be classified into 14 topics by the LDA topic modeling, and the three types of media presented homogeneity in the topics of their reports, yet had their own characteristics;there was a significant correlation between the media network agenda and the public network agenda, and the We Media reports had the most prominent effect on the public network agenda;the correlation between the media agenda and the implicit public agenda was higher than that of the explicit public agenda. Overall, findings showed a significant correlation between network agendas among different media.

Temporal variation of gaseous elemental mercury in a northern coastal city in China: Monsoon and COVID-19 lockdown effects

Continuous measurements of gaseous elemental mercury (GEM) were conducted in Qingdao from March 2020 to March 2021. The average concentration of GEM was (2.39 ± 1.07 ng/m3) with a variation range of 0.27–10.78 ng/m3. GEM exhibited a clear pattern of daily variation, with daily peaks occurring between 11:00–13:00. GEM concentrations were higher in winter (2.80 ± 1.28 ng/m3) than that in summer (2.18 ± 1.05 ng/m3). The high winter concentrations were related to coal-fired heating and the increased frequency of polluted weather in northern China. Principal component analysis showed that the main factors affecting GEM concentration were fossil fuel combustion, natural source release and atmospheric diffusion conditions. The anthropogenic emission sources were the main source of GEM in spring and winter, and natural sources of GEM was large in summer. The potential source contribution function suggested that North and Central China were the main potential sources of GEM, and there were large differences in the potential sources of GEM in different seasons. Comparing the GEM in the same time periods in 2018, 2020, and 2021, government policies, temporary lockdown measures for the COVID-19 epidemic, and urban village renovation led to a decreasing trend of GEM concentrations. This study contributes to a better understanding of the effects of long-range transport of air masses and anthropogenic emissions on atmospheric mercury in eastern coastal cities and offshore areas.

A Note on COVID-19 Diagnosis Number Prediction Model in China (preprint)

Objective: In December 2019, pneumonia infected with the novel coronavirus burst in Wuhan, China. We aimed to use a mathematical model to predict number of diagnosed patients in future to ease anxiety on the emergent situation. Methods: According to all diagnosis number from WHO website and combining with the transmission mode of infectious diseases, the mathematical model was fitted to predict future trend of outbreak. Our model was based on the epidemic situation in China, which could provide referential significance for disease prediction in other countries, and provide clues for prevention and intervention of relevant health authorities. In this retrospective, all diagnosis number from Jan 21 to Feb 10, 2020 reported from China was included and downloaded from WHO website. We develop a simple but accurate formula to predict the next day diagnosis number: ，where N i is the total diagnosed patient till the i th day, and was estimated as 0.904 at Feb 10. Results: Based on this model, it is predicted that the rate of disease infection will decrease exponentially. The total number of infected people is limited; thus, the disease will have limited impact. However, new diagnosis will last to end of March. Conclusions: Through the establishment of our model, we can better predict the trend of the epidemic in China.

A Note on COVID-19 Diagnosis Number Prediction Model in China (preprint)

In December 2019, pneumonia infected with the novel coronavirus burst in Wuhan, China. We aimed to use a mathematical model to predict number of diagnosed patients in future to ease anxiety on the emergent situation. Our model was based on the epidemic situation in China, which could provide referential significance for disease prediction in other countries, and provide clues for prevention and intervention of relevant health authorities. In this retrospective, all diagnosis number from Jan 21 to Feb 10, 2020 reported from China was included and downloaded from WHO website. We develop a simple but accurate formula to predict the next day diagnosis number: N_i/N_(i-1) =〖(N_(i-1)/N_(i-2) )〗^α，where Ni is the total diagnosed patient till the ith day, and α was estimated as 0.904 at Feb 10. Based on this model, it is predicted that the rate of disease infection will decrease exponentially. The total number of infected people is limited; thus, the disease will have limited impact. However, new diagnosis will last to end of March. Through the establishment of our model, we can better predict the trend of the epidemic in China.

Isolating multiple formats of human monoclonal neutralizing antibodies against SARS-CoV-2 by in vitro site-directed antibody screening (preprint)

Neutralizing antibody is one of the most effective interventions for acute pathogenic infection. Currently, over three million people have been identified for SARS-CoV-2 infection but SARS-CoV-2-specific vaccines and neutralizing antibodies are still lacking. SARS-CoV-2 infects host cells by interacting with angiotensin converting enzyme-2 (ACE2) via the S1 receptor-binding domain (RBD) of its surface spike glycoprotein. Therefore, blocking the interaction of SARS-CoV-2-RBD and ACE2 by antibody would cause a directly neutralizing effect against virus. In the current study, we selected the ACE2 interface of SARS-CoV-2-RBD as the targeting epitope for neutralizing antibody screening. We performed site-directed screening by phage display and finally obtained one IgG antibody (4A3) and several domain antibodies. Among them, 4A3 and three domain antibodies (4A12, 4D5, and 4A10) were identified to act as neutralizing antibodies due to their capabilities to block the interaction between SARS-CoV-2-RBD and ACE2-positive cells. The domain antibody 4A12 was predicted to have the best accessibility to all three ACE2-interfaces on the spike homotrimer. Pseudovirus and authentic SARS-CoV-2 neutralization assays showed that all four antibodies could potently protect host cells from virus infection. Overall, we isolated multiple formats of SARS-CoV-2-neutralizing antibodies via site-directed antibody screening, which could be promising candidate drugs for the prevention and treatment of COVID-19.

Prediction of the clinical outcome of COVID-19 patients using T lymphocyte subsets with 340 cases from Wuhan, China: a retrospective cohort study and a web visualization tool (preprint)

Background Wuhan, China was the epicenter of the 2019 coronavirus outbreak. As a designated hospital, Wuhan Pulmonary Hospital has received over 700 COVID-19 patients. With the COVID-19 becoming a pandemic all over the world, we aim to share our epidemiological and clinical findings with the global community. Methods In this retrospective cohort study, we studied 340 confirmed COVID-19 patients from Wuhan Pulmonary Hospital, including 310 discharged cases and 30 death cases. We analyzed their demographic, epidemiological, clinical and laboratory data and implemented our findings into an interactive, free access web application. Findings Baseline T lymphocyte Subsets differed significantly between the discharged cases and the death cases in two-sample t-tests: Total T cells (p < 2.2e-16), Helper T cells (p < 2.2e-16), Suppressor T cells (p = 1.8-14), and TH/TS (Helper/Suppressor ratio, p = 0.0066). Multivariate logistic regression model with death or discharge as the outcome resulted in the following significant predictors: age (OR 1.05, p 0.04), underlying disease status (OR 3.42, p 0.02), Helper T cells on the log scale (OR 0.22, p 0.00), and TH/TS on the log scale (OR 4.80, p 0.00). The McFadden pseudo R-squared for the logistic regression model is 0.35, suggesting the model has a fair predictive power. Interpretation While age and underlying diseases are known risk factors for poor prognosis, patients with a less damaged immune system at the time of hospitalization had higher chance of recovery. Close monitoring of the T lymphocyte subsets might provide valuable information of the patients condition change during the treatment process. Our web visualization application can be used as a supplementary tool for the evaluation.

COVID-19 Epidemic Outside China: 34 Founders and Exponential Growth (preprint)

Background: In December 2019, pneumonia infected with a novel coronavirus burst in Wuhan, China. Now the situation is almost controlled in China but is worse outside China. We aimed to build a mathematical model to capture the global trend of epidemics outside China. Methods: In this retrospective, outside-China diagnosis number reported from Jan 21 to Feb 28, 2020 was downloaded from WHO website. We develop a simple regression model on these numbers: log10 (Nt+34)=0.0515*t+2.075 where Nt is the total diagnosed patient till the ith day, t=1 at Feb 1. Findings: Based on this model, we estimate that there have been about 34 unobserved founder patients at the beginning of spread outside China. The global trend is approximately exponential, with the rate of 10 folds every 19 days.