Public emotional responses to crisis: The COVID-19 pandemic in Wuhan and London

Online public responses during crises provide a window into how people emotionally react to them. Capitalizing on the international nature of the COVID-19 pandemic, we performed cross-cultural examination of group and individual differences in public emotional responses. We collected 1,106,395 Weibo posts in Wuhan from July 2019 to June 2020 and 6,564,014 tweets in London from October 2019 to July 2020, and found that the public mood in both cities followed a similar pattern during the onset of the COVID-19 pandemic: a stage of plunging mood followed by a period of recovery. We further examined the relationship between individuals' personality and mood changes. Our results showed that in Wuhan, emotionally stable people experienced more dramatic mood changes, while in London, people high in agreeableness and conscientiousness were more negatively affected during the lockdown period. Based on our findings, we suggest effective crisis management strategies for both policymakers and individuals.

Requesting a commitment in school teachers to teach in unprecedented ways: The mediating role of teacher agency

The necessity for the development and enhancement of teacher commitment to satisfying students' learning needs in response to the COVID crisis is increasingly highlighted. It is not known, however, how to increase commitment in schoolteachers to boost online teaching in light of the fact that they, too, are struggling to cope with the rapid, unexpected change. A total of 601 teachers from primary and secondary schools across China participated in this study, with an average teaching experience of 15.9 years. Structural equation modelling was used to verify the significance of contextual, cognitive, affective and behavioural factors in boosting teachers' commitment to online teaching. The findings demonstrated that teacher agency played a complete mediating role in the predicting power of other factors to teacher commitment. Therefore, it was recommended that attention be paid to the practice and opportunities for teacher agentic actions, which necessitates real encounters with online teaching, allowing teachers to act meaningfully and initiate a new set of teaching strategies. Practitioner notes What is already known about this topic The large-scale transition to emergency online teaching serves as the catalyst for creating a blended or hybrid model of education provision in the long term. How hard teachers work to perform at their best and overcome obstacles to support students' learning needs in new environment relies on the intensity of teacher commitment to change. Online and blended learning requires teachers to not only be prepared for a diverse learning environment but also to build and rebuild their own identity as future teachers. What this paper adds This study adds to our knowledge of how traditional F2F classroom teachers reinvented their roles and responsibilities in response to the pandemic-driven challenges based on real-world experiences. As a result of the COVID-19 lockdown school closures, schoolteachers' commitment to enhancing online teaching efforts has increased. The study highlights the complete mediating role of teacher agency in the predicting power of cognitive and affective factors to teacher commitment. Implications for practice and/or policy To learn more about how to be a good online teacher, future teachers need greater deliberate effort in diverse online teaching activities. Future teachers should be equipped with not only new technological and remote instructional strategies and skills, but also with confidence in, value for, and actual experiences with online teaching in a technology-rich environment. For teachers to obtain hands-on experience in integrating technology with distance teaching pedagogy at a time of rapid change, schools should have some days online and offer blended learning opportunities wherever possible. © 2023 British Educational Research Association.

Service center location problems with decision dependent utilities and a pandemic case study

We study a service center location problem with ambiguous utility gains upon receiving service. The model is motivated by the problem of deciding medical clinic/service centers, possibly in rural communities, where residents need to visit the clinics to receive health services. A resident gains his utility based on travel distance, waiting time, and service features of the facility that depend on the clinic location. The elicited location-dependent utilities are assumed to be ambiguously described by an expected value and variance constraint. We show that despite a non-convex nonlinearity, given by a constraint specified by a maximum of two second-order conic functions, the model admits a mixed 0-1 second-order cone (MISOCP) formulation. We study the non-convex substructure of the problem, and present methods for developing its strengthened formulations by using valid tangent inequalities. Computational study shows the effectiveness of solving the strengthened formulations. Examples are used to illustrate the importance of including decision dependent ambiguity. An illustrative example to identify locations for Covid-19 testing and vaccination is used to further illustrate the model and its properties. © 2023 The Authors. Naval Research Logistics published by Wiley Periodicals LLC.

Contrastive Representation Learning for Expression Recognition from Masked Face Images

With the worldwide spread of COVID-19, people are trying different ways to prevent the spread of the virus. One of the most useful and popular ways is wearing a face mask. Most people wear a face mask when they go out, which makes facial expression recognition become harder. Thus, how to improve the performance of the facial expression recognition model on masked faces is becoming an important issue. However, there is no public dataset that includes facial expressions with masks. Thus, we built two datasets which are a real-world masked facial expression database (VIP-DB) and a man-made masked facial expression database (M-RAF-DB). To reduce the influence of masks, we utilize contrastive representation learning and propose a two-branches network. We study the influence of contrastive learning on our two datasets. Results show that using contrastive representation learning improves the performance of expression recognition from masked face images. © 2022 ACM.

Trends of Telehealth Utilization among Cardiovascular Disease Patients before and during the Covid-19 Pandemic and Cost Analysis

Objectives: To determine the costs and trends of telehealth utilization before and during the COVID-19 pandemic for patients with cardiovascular diseases (CVD). Methods: We calculated the proportions of patients with CVD who used telehealth before (January 2019-February 2020) and during (March 2020-April 2021) the pandemic using MarketScan Commercial Claims and Encounters. We compared the trends of telehealth utilization among CVD patients by sex, age, claim type, payment plan, and rural/urban status. We calculated mean, SD, median, and IQR of total and average telehealth costs. Results: The results show that telehealth utilization spiked in March 2020 and further rapidly increased and peaked in April 2020. Telehealth utilization increased from 0.021% in March 2019 to 6.7% in March 2020 and from 0.024% in April 2019 to 39.8% in April 2020. After peaking in April 2020, telehealth utilization rapidly fell in May-June 2020 and then gradually decreased before rebounding in October-December 2020. It resumed the decline through April 2021 remained much higher than the prepandemic level. Telehealth utilization was higher during the pandemic for females than males;for younger age groups (aged 18-44 or 45-64) than older age group (aged 65+);for commercial claims than Medicare supplemental insurance;for capitated insurance plans than non-capitated insurance plans;and for patients in urban areas than rural areas. From March 2020-February 2021, the mean (SD) telehealth cost per visit was $115.7 (66) (median [IQR], $104.8 [$75.8-$139.4]), with patient out-of-pocket cost $25.5 (42.8) (median [IQR], $10 [$0-$30]), insurance cost $90.4 (71.0) (median [IQR], $83 [$45.3-$121.3]), and the mean (SD) number of telehealth utilizations per patient was 1.67 (1.8) (median [IQR], 1 [1-2]). Conclusions: Policymakers, health care practitioners, and insurance companies should be aware of trends and costs of telehealth utilization among patients with CVD. Our results show that telehealth would facilitate better management of CVD. Our results may inform future policies and practices to meet the increased demand for telehealth.

Estimation of epidemiological parameters of COVID-19 epidemic caused by Delta variant strain in Guangzhou

Objective An epidemic of COVID-19 caused by an imported Delta variant strain in Guangzhou was investigated, and the transmission chain, transmission characteristics and infection of each case were analyzed, so as to provide a theoretical basis for predicting disease development and epidemic prevention and control. Methods By collecting the information released by Guangzhou government, the confirmed cases with a clear transmission chain were selected, and the infectious disease indicators such as serial interval (SI), basic reproduction number (Rq) and time-dependent reproduction number (Rt) were calculated to analyze the epidemiological characteristics. Results From May 21 to June 20, 2021, a total of 144 cases of indigenous COVID-19 were confirmed in Guangzhou, among which 67 pairs of cases with a clear transmission chain were selected. SI was calculated to follow the Gamma distribution, with a mean of 4. 27 d and a standard deviation of 2.65 d. Rq = 3. 18 (95% CI: 2. 1974.428), and Rt showed an obvious decreasing trend over time. On June 10, Rt = 0.97 (95% CI: 0. 751 -1. 214), which was lower than 1. Since then Rt had been less than 1, and it got smaller and smaller over time. Conclusion In this COVID-19 epidemic, the SI was shorter and the Rq was larger, which indicated that the Delta variant strain had a faster transmission rate and stronger transmissibility than the COVID-19 infected in Wuhan in 2020.

COVID-HateBERT: A Pre-trained Language Model for COVID-19 related Hate Speech Detection

With the dramatic growth of hate speech on social media during the COVID-19 pandemic, there is an urgent need to detect various hate speech effectively. Existing methods only achieve high performance when the training and testing data come from the same data distribution. The models trained on the traditional hateful dataset cannot fit well on COVID-19 related dataset. Meanwhile, manually annotating the hate speech dataset for supervised learning is time-consuming. Here, we propose COVID-HateBERT, a pre-trained language model to detect hate speech on English Tweets to address this problem. We collect 200M English tweets based on COVID-19 related hateful keywords and hashtags. Then, we use a classifier to extract the 1.27M potential hateful tweets to re-train BERT-base. We evaluate our COVID-HateBERT on four benchmark datasets. The COVID-HateBERT achieves a 14.8%-23.8% higher macro average F1 score on traditional hate speech detection comparing to baseline methods and a 2.6%-6.73% higher macro average F1 score on COVID-19 related hate speech detection comparing to classifiers using BERT and BERTweet, which shows that COVID-HateBERT can generalize well on different datasets. © 2021 IEEE.

Combined and interactive effects of alcohol drinking and cigarette smoking on the risk of severe illness and poor clinical outcomes in patients with COVID-19: a multicentre retrospective cohort study.

OBJECTIVES: Cigarette smoking is an established risk factor for illness severity and adverse outcomes in patients with COVID-19. Alcohol drinking may also be a potential risk factor for disease severity. However, the combined and interactive effects of drinking and smoking on COVID-19 have not yet been reported. This study aimed to examine the combined and interactive effects of alcohol drinking and cigarette smoking on the risk of severe illness and poor outcomes in patients with COVID-19. STUDY DESIGN: This was a multicentre retrospective cohort study. METHODS: This study retrospectively reviewed the data of 1399 consecutive hospitalised COVID-19 patients from 43 designated hospitals. Patients were grouped according to different combinations of drinking and smoking status. Multivariate mixed-effects logistic regression models were used to estimate the combined and interactive effects of drinking and smoking on the risk of severe COVID-19 and poor clinical outcomes. RESULTS: In the study population, 7.3% were drinkers/smokers, 4.3% were drinkers/non-smokers and 4.9% were non-drinkers/smokers. After controlling for potential confounders, smokers or drinkers alone did not show a significant increase in the risk of severe COVID-19 or poor clinical outcomes compared with non-drinkers/non-smokers. Moreover, this study did not observe any interactive effects of drinking and smoking on COVID-19. Drinkers/smokers had a 62% increased risk (odds ratio = 1.62, 95% confidence interval: 1.01-2.60) of severe COVID-19 but did not have a significant increase in the risk for poor clinical outcomes compared with non-drinkers/non-smokers. CONCLUSIONS: Combined exposure to drinking and smoking increases the risk of severe COVID-19, but no direct effects of drinking or smoking, or interaction effects of drinking and smoking, were detected.

Sensitive and quantitative detection of SARS-CoV-2 antibodies from vaccinated serum by MoS2-field effect transistor

Recently, the coronavirus disease (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread globally with major impact on public health. Novel methods that enable fast and efficient detection of the virus and the associated biomarkers, such as SARS-CoV-2 antibodies, may provide alterative opportunities for early diagnosis, disease status monitoring, and the development of vaccines. Here, we report the fabrication of a functionalized MoS2-field effect transistor (FET) for sensitive and quantitative detection of antibodies against SARS-CoV-2 spike protein receptor binding domain (S-RBD) in vaccinated serum specimens. The device was modified by SARS-CoV-2 S-RBD fusion protein on the surface and enabled rapid detection of SARS-CoV-2 antibodies. In addition, an on-chip calibration method was established for quantitative measurement. Furthermore, this method was applied to measure the levels of S-RBD antibodies in serum specimens from vaccinated donors. The devices showed no response to negative control samples from individuals who did not receive vaccination, suggesting the high specificity of this method. This study illustrated the successful fabrication of S-RBD functionalized MoS2-FET with potential clinical applications to facilitate vaccine development and efficacy evaluation.

[Preliminary clinical analysis of direct renin inhibitor aliskiren in the treatment of severe coronavirus disease 2019 patients with hypertension].

Objective: To explore the feasibility of direct renin inhibitor aliskiren for the treatment of severe or critical coronavirus disease 2019 (COVID-19) patients with hypertension. Methods: The antihypertensive effects and safety of aliskiren was retrospectively analyzed in three severe and one critical COVID-19 patients with hypertension. Results: Four patients, two males and two females, with an average age of 78 years (66-87 years), were referred to hospital mainly because of respiratory symptoms. Three were diagnosed by positive novel coronavirus 2019 (2019-nCoV) nucleic acid or antibody, and the critical patient with cardiac insufficiency was clinically determined. Two patients were treated with calcium channel antagonist (CCB), one with angiotensin converting enzyme inhibitor (ACEI), and one with angiotensin Ⅱ receptor antagonist (ARB). After admission, ACEI and ARB were discontinued, one patient with heart failure was treated by aliskiren combined with diuretic.Three patients were treated with aliskiren combined with CCB among whom two withdrew CCB due to low blood pressure after 1 to 2 weeks. Based on comprehensive treatment including antiviral and oxygenation treatment, blood pressure was satisfactorily controlled by aliskiren after three to four weeks without serious adverse events. All patients were finally discharged. Conclusion: Our preliminary clinical data shows that antihypertensive effect of aliskiren is satisfactory and safe for severe COVID-19 patients complicated with hypertension.

Recent Developments in the Pathogenesis of COVID-19-associated Myocarditis

Severe acute respiratory syndrome coronavirus-2(SARS-CoV-2)was initially thought to mainly affect the respiratory system. A growing body of research has found that SARS-CoV-2 infection can affect multiple human organ systems, and heart is a major target organ secondary to lung. COVID-19-associated cardiac injury is often seen clinically, about 1%-7% of which is myocarditis. COVID-19-associated myocarditis often has a poor outcome. However, the possible pathogenesis mechanisms of COVID-19-associated cardiac injury, and its progression to COVID-19-associated myocarditis, as well as related pathophysiological changes are still unknown. Available data analysis has revealed that the recognized mechanism of cardiac injury in COVID-19 is ACE2-mediated cardiac injury, and cytokine storm-mediated or immune-mediated cardiac injury is only suggestive currently. Due to limited use of cardiac MRI and endocardial biopsy as well as insufficient autopsy findings and other basic research data in patients with COVID-19-associated myocarditis, the diagnosis and treatment of COVID-19-associated myocarditis are challenges that still exist for clinicians. We reviewed the possible pathophysiological mechanisms of COVID-19-associated myocarditis, which will contribute to improving medical workers' understanding of this disease so that its related mortality may be reduced. Copyright © 2021 by the Chinese General Practice.

On Analyzing COVID-19-related Hate Speech Using BERT Attention

The emergence of COVID-19 has engendered a new wave of online hate speech in social media platforms such as Twitter. Its widespread effects range from acts of cyber-harassment towards certain ethnic communities (e.g., the Asian community), to targeting older people belonging to age groups correlated with higher mortality rates (termed infamously as Boomer Remover). Thus, an urgent need arises for a timely mitigation of this new wave of online hate speech. In this work, we aim to discover the hate-related keywords linked to COVID-19 in hateful tweets posted on Twitter so that users posting such keywords can be asked to reconsider posting them. We first collect a new dataset of tweets targeting older people supplementing with a dataset targeting the Asian community. Then, we develop an approach to analyze the datasets with BERT (a transformer-based model) attention mechanism and discover 186 novel keywords targeting the Asian community and 100 keywords targeting older people. Based on our study, we then propose a control mechanism wherein a user can be asked to reconsider using certain sensitive words identified by our approach. We further perform an exploratory analysis of BERT attention mechanism and find that the most high-impact, long distance attentions are learned in the earlier or later layers of the model depending on the underlying data distribution. Our study indicates that the BERT model in some cases uses a hate keyword and an associated group or individual to make predictions, a finding that is inline with existing hate-speech research, which suggests that hate-speech is often aimed at certain groups or individuals. © 2020 IEEE.

Investing a Better Future: Education for Sustainable Development as a Means to Strengthen the Environmental Education

Since the outbreak of the epidemic, the environment education and the education of sustainable development became the mainfocus once again. It is because some citizens lack consciousness of ecological civilization and sustainable development, caused environmental pollution, indiscriminate killing of wild animals, destruction of social public health, injury and sacrifice of people's physical and mental health, disturbance of urban and rural social life order, etc. The outbreak of the novel coronavirus pneumonia undoubtedly confirmed the urgency of strengthening the global citizens' education for sustainable development. This serious epidemic related to the sale and consumption of wild animals is a wake-up call again. It is necessary to reexamine the relationship between man and nature and the way of human behavior. Education is the key factor for the implementation of sustainable development. Good health and well-being, and quality education are the third and fourth goal for sustainable development proposed by the United Nations. Through content analysis and case analysis, this paper indicated that teachers' educational ability for sustainable development is a professional ability that higher education institutes should apply education for sustainable development as a means to strengthen the environmental education. Teachers identify with the concept of sustainable development education and are willing to practice the values of sustainable development in life and educational activities. © Published under licence by IOP Publishing Ltd.