eHealth Literacy of Chinese Residents During the Coronavirus Disease 2019 Pandemic: A Cross-sectional Survey.

The Coronavirus Disease 2019 (COVID-19) pandemic has become a leading societal concern. eHealth literacy is important in the prevention and control of this pandemic. The purpose of this study is to identify eHealth literacy of Chinese residents about the COVID-19 pandemic and factors influencing eHealth literacy. A total of 15 694 individuals clicked on the link to the questionnaire, and 15 000 agreed to participate and completed the questionnaire for a response rate of 95.58%. Descriptive statistics, χ2 test, and logistic regression analysis were conducted to analyze participants' level of eHealth literacy about COVID-19 and its influencing factors. The results showed 52.2% of participants had relatively lower eHealth literacy regarding COVID-19 (eHealth literacy score ≤ 48). The scores of the information judgment dimension (3.09 ± 0.71) and information utilization dimension (3.18 ± 0.67) of the eHealth literacy scale were relatively lower. The logistics regression showed that sex, age, education level, level of uncertainty, having people around the respondent diagnosed with COVID-19, relationship with family, and relationship with others were associated to eHealth literacy (χ2 = 969.135, P < .001). The public's eHealth literacy about COVID-19 needs to be improved, especially the ability to judge and utilize online information. Close collaboration among global health agencies, governments, healthcare institutions, and media is needed to provide reliable online information to the public. Interventions to improve eHealth literacy should take into account and accentuate the importance of sex, age, educational background, level of uncertainty, exposure to disease, and social support.

Systematic identification of chemical components in Fufang Shuanghua oral liquid and screening of potential active components against SARS-CoV-2 protease.

Coronavirus disease (COVID-19) caused by SARS-COV-2 infection has been widely prevalent in many countries and has become a common challenge facing mankind. Traditional Chinese medicine (TCM) has played a prominent role in this pandemic, and especially TCM with the function of "heat-clearing and detoxifying" has shown an excellent role in anti-virus. Fufang Shuanghua oral liquid (FFSH) has been used to treat the corresponding symptoms of influenza such as fever, nasal congestion, runny nose, sore throat, and upper respiratory tract infections in clinic, which are typical symptoms of COVID-19. The content of chlorogenic acid, andrographolide and dehydrated andrographolide as the quality control components of FFSH is not less than 1.0 mg/mL, 60 µg/mL and 60 µg/mL respectively. In this study, UPLC-Q-TOF-MS/MS was employed to describe the chemical profile of FFSH. Virtual screening and fluorescence resonance energy transfer (FRET) were used to screen the effective components of FFSH acting on SARS-CoV-2 main protease (Mpro). As a result, 214 compounds in FFSH were identified or preliminarily characterized by UPLC-Q-TOF-MS/MS, and 61 active ingredients with potential inhibitory effects on Mpro were selected through receptor-based and ligand-based virtual screening. In particular, quercetin, forsythoside A, and linoleic acid showed a good inhibitory effect on Mpro in FRET evaluation with IC50 values of 26.15 µM, 22.26 µM and 47.09 µM respectively, and had a strong binding affinity with the receptor Mpro (6LU7) in molecular docking. CYS145 and HIS41 were the main amino acid residues affected by small molecules in the protein binding domain. In brief, we characterized, for the first time, 214 chemical components in FFSH, and three of them, including quercetin, forsythoside A and linoleic acid, were screened out to exert beneficial anti-COVID-19 effects through CYS145 and HIS41 sites, which may provide a new research strategy for TCM to develop new therapeutic drugs against COVID-19.

Rapid artificial intelligence solutions in a pandemic-The COVID-19-20 Lung CT Lesion Segmentation Challenge.

Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A; n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge - 2020.

Different Methods of COVID-19 Detection

By the end of March 2020, the State Drug Administration had approved more than 20 SARS-CoV-2 detection products, most of which are based on RT-PCR. Because it involves multiple manual operation steps, relies on complex thermal cycle process, and has defects such as long detection time, the application potential of this kind of technology is limited in rapid detection. Rapid antigen test can play an important role in guiding patient management, public health prevention and control decision-making, and COVID-19 surveillance, especially for grass-root areas where clinical diagnosis, treatment, prevention and control may be hindered due to lack of nucleic acid testing capabilities or long testing time. According to WHO recommendations, based on the high specificity of the antigen test, a positive result indicates a novel coronavirus infection. In areas with widespread community spread, rapid antigen test can be used to detect and isolate positive cases early in health facilities, COVID-19 testing centers/sites, nursing homes, prisons, schools, frontline and health care workers, and to trace contacts.

A satellite-data-driven framework to rapidly quantify air-basin-scale NOx emissions and its application to the Po Valley during the COVID-19 pandemic

The evolving nature of the COVID-19 pandemic necessitates timely estimates of the resultant perturbations to anthropogenic emissions. Here we present a novel framework based on the relationships between observed column abundance and wind speed to rapidly estimate the air-basin-scale NOx emission rate and apply it at the Po Valley in Italy using OMI and TROPOMI NO2 tropospheric column observations. The NOx chemical lifetime is retrieved together with the emission rate and found to be 15–20 h in winter and 5–6 h in summer. A statistical model is trained using the estimated emission rates before the pandemic to predict the trajectory without COVID-19. Compared with this business-as-usual trajectory, the real emission rates show three distinctive drops in March 2020 (-42%), November 2020 (-38%), and March 2021 (-39%) that correspond to tightened COVID-19 control measures. The temporal variation of pandemic-induced NOx emission changes qualitatively agrees with Google and Apple mobility indicators. The overall net NOx emission reduction in 2020 due to the COVID-19 pandemic is estimated to be 22%.

Rapid Artificial Intelligence Solutions in a Pandemic - The COVID-19-20 Lung CT Lesion Segmentation Challenge.

Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A; n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge - 2020.

Relationship between eHealth literacy and psychological status during COVID-19 pandemic: A survey of Chinese residents.

AIMS: To investigate the eHealth literacy and the psychological status of Chinese residents during the COVID-19 pandemic and explore their interrelationship. BACKGROUND: The COVID-19 outbreak has placed intense psychological pressure on community residents. Their psychological status may be affected by eHealth literacy due to home isolation during this rampant pandemic. METHODS: This is a Web-based cross-sectional survey conducted on the JD Health platform, which resulted in 15,000 respondents having participated in this survey. The eHealth Literacy Questionnaire (EHLQ), Patient Health Questionnaire-9 (PHQ-9), Insomnia Severity Index (ISI) and Impact of Event Scale-Revised (IES-R) were used. The Pearson correlation was used to analyse the relationship between eHealth literacy and depression, insomnia and post-traumatic stress disorder. RESULTS: The score of eHealth literacy was 48.88 ± 8.46, and 11.4%, 6.8% and 20.1% of respondents experienced moderate to severe depression, insomnia and post-traumatic stress disorder. eHealth literacy negatively correlated with depression (r = -0.331), insomnia (r = -0.366) and post-traumatic stress disorder (r = -0.320). CONCLUSION: eHealth literacy is closely related to psychological status. Improving eHealth literacy may contribute to maintaining good psychological well-being. IMPLICATIONS FOR NURSING MANAGEMENT: It is necessary to strengthen the education of primary health care providers to enhance their ability to help community residents effectively use eHealth information.

Risk Factors for Poor Outcomes of Diabetes Patients With COVID-19: A Single-Center, Retrospective Study in Early Outbreak in China.

Background: Diabetes has been found to increase severity and mortality under the current pandemic of coronavirus disease of 2019 (COVID-19). Up to date, the clinical characteristics of diabetes patients with COVID-19 and the risk factors for poor clinical outcomes are not clearly understood. Methods: The study was retrospectively carried out on enrolled diabetes patients with laboratory confirmed COVID-19 infection from a designated medical center for COVID-19 from January 25th, 2020 to February 14th, 2020 in Wuhan, China. The medical record was collected and reviewed. Univariate and multivariate analyses were performed to assess the risk factors associated with the severe events which were defined as a composite endpoint of admission to intensive care unit, the use of mechanical ventilation, or death. Results: A total of 52 diabetes patients with COVID-19 were finally included in the study. 21 (40.4%) patients had developed severe events in 27.50 (IQR 12.25-35.75) days follow-up, 15 (28.8%) patients experienced life-threatening complications and 8 patients died with a recorded mortality rate of 15.4%. Only 13 patients (41.9%) were in optimal glycemic control with HbA1c value of <7.0%. In addition to general clinical characteristics of COVID-19, the severe events diabetes patients showed higher counts of white blood cells and neutrophil, lower lymphocytes (40, 76.9%), high levels of hs-CRP, erythrocyte sedimentation rate (ESR) and procalcitonin (PCT) as compared to the non-severe diabetes patients. Mild higher level of cardiac troponin I (cTNI) (32.0 pg/ml; IQR 16.80-55.00) and D-dimer (1.70 µg/L, IQR 0.70-2.40) were found in diabetes patients with severe events as compared to the non-severe patients (cTNI:20.00 pg/ml, IQR5.38-30.00, p = 0.019; D-dimer: 0.70 µg/L, IQR 0.30-2.40, p = 0.037). After adjusting age and sex, increased level of cTNI was found to significantly associate with the incidence of severe events (HR: 1.007; 95% CI: 1.000-1.013; p = 0.048), Furthermore, using of α-glucosidase inhibitors was found to be the potential protectant for severe events (HR: 0.227; 95% CI: 0.057-0.904; p = 0.035). Conclusion: Diabetes patients with COVID-19 showed poor clinical outcomes. Vigorous monitoring of cTNI should be recommended for the diabetes patients with COVID-19. Usage of α-glucosidase inhibitors could be a potential protectant for the diabetes patients with COVID-19.

Quarantine At Home May Not Enough!-From the Epidemiological Data In Shaanxi Province of China (preprint)

Objectives : A pneumonia associated with 2019 novel coronavirus (2019-nCoV, subsequently named SARS-CoV2) emerged worldwide since December, 2019. We aimed to describe the epidemiological characteristics of 2019 coronavirus disease (COVID-19) in Shaanxi province of China.  Results: 1. Among the 245 patients, 132 (53.9%) were males and 113 (46.1%) were females. The average age was 46.15±16.43 years, ranging from 3 to 89 years. 2.  For the clinical type, 1.63% (4/245) patients were mild type , 84.90% (208/245) were moderate type, 7.76% (19/245) were severe type, 5.31% (13/245) were critical type and only 0.41% (1/245) was asymptomatic. 3. Of the 245 patients, 116 (47.35%) were input case, 114 (46.53%) were non-input case , and 15 (6.12%) were unknown exposure. 4. 48.57% (119/245) cases were family cluster , involving 42 families. The most common pattern of COVID-19 family cluster was between husband and wife or between parents and children. 

A Universal Model for Prediction of COVID-19 Pandemic Based on Machine Learning (preprint)

Background: With the current worldwide spreading of the coronary virus (COVID-19) pandemic, accurately predicting the rate of spread of the virus has become an urgent need. Methods: In this article we propose a universal COVID-19 prediction model that is independent of country-specific factors in this paper. By analyzing the pandemic data in China, we combined the advantages of Gaussian function with that of chi-square distribution function, to render an innovative mathematical model named the H-Gaussian with five parameters to be learned, and solved the parameters by a gradient descent algorithm. Results: We trained the model with partial historical pandemic data to predict subsequent pandemic trends in several regions, and validated the predictions with real data. The H-Gaussian model was experimentally shown to correctly predict the pandemic trends, and the parameters had good interpretability. Conclusions: On this basis, the global trends of the pandemic are given based on the data currently available, as well as suggestions for subsequent prevention strategies.

Are COVID-19 infected children with gastrointestinal symptoms different from those without symptoms? A comparative study of the clinical characteristics and epidemiological trend of 244 pediatric cases from Wuhan (preprint)

Objective: COVID-19 patients presenting with gastrointestinal (GI) symptoms occur in both adults and children. To date, however, no large sample size study focusing on gastrointestinal symptoms in pediatric cases has been published. We analyzed COVID-19 infected children in Wuhan who presented with initial GI symptoms to determine the GI characteristics and epidemiological trend of the disease. Design: We retrospectively analyzed 244 children patients confirmed with COVID-19 at Wuhan Children's Hospital from 21 Jan to 20 Mar 2020. Symptomatic cases were divided into two groups according to whether the patients presented with or without GI symptoms on admission. Demographic, epidemiological, symptoms, and laboratory data were compared. We also analyzed the respective trends of case number changes of GI cases and asymptomatic cases. Results: 34 out of 193 symptomatic children had GI symptoms. They had lower median age and weight, a higher rate of fever, a longer length of stay and more hematological and biochemical abnormalities than patients without GI symptoms. There was no significant difference in chest CT findings or stool SARS-CoV-2 test positive percentages between the two groups. The number of patients admitted with GI symptoms showed an overall downward trend with time. At the time of writing, 242 patients were discharged, one died, and one critically ill patient was still in the intensive care unit. Conclusion: COVID-19 infected children with GI symptoms are prone to presenting with more clinical and laboratory abnormalities than patients without GI symptoms. More attention and timely hospital admission are needed for these patients.

An SEIR Model for Assessment of Current COVID-19 Pandemic Situation in the UK (preprint)

The ongoing COVID-19 pandemic spread to the UK in early 2020 with the first few cases being identified in late January. A rapid increase in confirmed cases started in March, and the number of infected people is however unknown, largely due to the rather limited testing scale. A number of reports published so far reveal that the COVID-19 has long incubation period, high fatality ratio and non-specific symptoms, making this novel coronavirus far different from common seasonal influenza. In this note, we present a modified SEIR model which takes into account the time lag effect and probability distribution of model states. Based on the proposed model, it is estimated that the actual total number of infected people by 1 April in the UK might have already exceeded 610,000. Average fatality rates under different assumptions at the beginning of April 2020 are also estimated. Our model also reveals that the R0 value is between 7.5-9 which is much larger than most of the previously reported values. The proposed model has a potential to be used for assessing future epidemic situations under different intervention strategies.

Epidemiological, clinical and virological characteristics of 74 cases of coronavirus-infected disease 2019 (COVID-19) with gastrointestinal symptoms.

OBJECTIVE: The SARS-CoV-2-infected disease (COVID-19) outbreak is a major threat to human beings. Previous studies mainly focused on Wuhan and typical symptoms. We analysed 74 confirmed COVID-19 cases with GI symptoms in the Zhejiang province to determine epidemiological, clinical and virological characteristics. DESIGN: COVID-19 hospital patients were admitted in the Zhejiang province from 17 January 2020 to 8 February 2020. Epidemiological, demographic, clinical, laboratory, management and outcome data of patients with GI symptoms were analysed using multivariate analysis for risk of severe/critical type. Bioinformatics were used to analyse features of SARS-CoV-2 from Zhejiang province. RESULTS: Among enrolled 651 patients, 74 (11.4%) presented with at least one GI symptom (nausea, vomiting or diarrhoea), average age of 46.14 years, 4-day incubation period and 10.8% had pre-existing liver disease. Of patients with COVID-19 with GI symptoms, 17 (22.97%) and 23 (31.08%) had severe/critical types and family clustering, respectively, significantly higher than those without GI symptoms, 47 (8.14%) and 118 (20.45%). Of patients with COVID-19 with GI symptoms, 29 (39.19%), 23 (31.08%), 8 (10.81%) and 16 (21.62%) had significantly higher rates of fever >38.5°C, fatigue, shortness of breath and headache, respectively. Low-dose glucocorticoids and antibiotics were administered to 14.86% and 41.89% of patients, respectively. Sputum production and increased lactate dehydrogenase/glucose levels were risk factors for severe/critical type. Bioinformatics showed sequence mutation of SARS-CoV-2 with m6A methylation and changed binding capacity with ACE2. CONCLUSION: We report COVID-19 cases with GI symptoms with novel features outside Wuhan. Attention to patients with COVID-19 with non-classic symptoms should increase to protect health providers.

Improved Epidemic Dynamics Model and Its Prediction for COVID-19 in Italy

The outbreak of coronavirus disease 2019 (COVID-19) has become a global public health crisis due to its high contagious characteristics. In this article, we propose a new epidemic-dynamics model combining the transmission characteristics of COVID-19 and then use the reported epidemic data from 15 February to 30 June to simulate the spread of the Italian epidemic. Numerical simulations showed that (1) there was a remarkable amount of asymptomatic individuals;(2) the lockdown measures implemented by Italy effectively controlled the spread of the outbreak;(3) the Italian epidemic has been effectively controlled, but SARS-CoV-2 will still exist for a long time;and (4) the intervention of the government is an important factor that affects the spread of the epidemic.