Dr.'s Eye: The Design and Evaluation of a Video Conferencing System to Support Doctor Appointments in Home Settings

The spread of COVID-19 has encouraged the practice of using video conferencing for family doctor appointments. Existing applications and off-the-shelf devices face challenges in dealing with capturing the correct view of patients' bodies and supporting ease of use. We created Dr.'s Eye, a video conferencing prototype to support varying types of body exams in home settings. With our prototype, we conducted a study with participants using mock appointments to understand the simultaneous use of the camera and display and to get insights into the issues that might arise in real doctor appointments. Results show the benefits of providing more flexibility with a decoupled camera and display, and privacy protection by limiting the camera view. Yet, challenges remain in maneuvering two devices, presenting feedback for the camera view, coordinating camera work between the participant and the examiner, and reluctance towards showing private body regions. This inspires future research on how to design a video system for doctor appointments. © 2023 ACM.

Design of Folate-Containing Liposomal Nucleic Acid Delivery Systems for Antitumor Therapy

The outbreak of multiple disaster sites during the coronavirus disease 2019 (COVID-19) pandemic has presented challenges due to varying access time intensity, population density, and medical resources at each site. To address these issues, this study focuses on 13 districts and counties in Wuhan, China. The importance of each research area is analyzed using the improved PageRank and TOPSIS algorithms to determine the optimal site selection plan. Additionally, a particle swarm algorithm is used to construct an emergency material dispatching model that targets both distribution and site selection costs to solve the multi-distribution center dispatching problem. The results suggest that constructing 10 distribution centers can satisfy the demand for epidemic prevention and control in Wuhan city while saving costs associated with site selection and material distribution. Compared to the previous optimal solution, the distribution and site selection costs under the optimal solution decreased by 27.9% and 17.82%, respectively. This approach can serve as a basis for dispatching emergency materials during public health emergencies.

Emergency Material Scheduling Optimization Method Using Multi-Disaster Point Distribution Approach

The outbreak of multiple disaster sites during the coronavirus disease 2019 (COVID-19) pandemic has presented challenges due to varying access time intensity, population density, and medical resources at each site. To address these issues, this study focuses on 13 districts and counties in Wuhan, China. The importance of each research area is analyzed using the improved PageRank and TOPSIS algorithms to determine the optimal site selection plan. Additionally, a particle swarm algorithm is used to construct an emergency material dispatching model that targets both distribution and site selection costs to solve the multi-distribution center dispatching problem. The results suggest that constructing 10 distribution centers can satisfy the demand for epidemic prevention and control in Wuhan city while saving costs associated with site selection and material distribution. Compared to the previous optimal solution, the distribution and site selection costs under the optimal solution decreased by 27.9% and 17.82%, respectively. This approach can serve as a basis for dispatching emergency materials during public health emergencies. © 2023 by the authors.

A Bayesian two-stage group sequential scheme for ordinal endpoints

Ordinal endpoints are common in clinical studies. For example, many clinical trials for evaluating COVID-19 infection therapies have adopted an ordinal scale as recommended by the World Health Organization. Despite their importance in clinical studies, design methods for ordinal endpoints are limited;in practice, a dichotomized approach is often used for simplicity. Here, we introduce a Bayesian group sequential scheme to assess ordinal endpoints, which considers a proportional-odds (PO) model, a nonproportional-odds (NPO) model, and a PO/NPO-switch model to handle various scenarios. Extensive simulations are conducted to demonstrate desirable performance, and the R package BayesOrdDesign has been made publicly available.

Lockdown lifted: measuring spatial resilience from London's public transport demand recovery

The disruptive effects of the COVID-19 pandemic has rapidly shifted how individuals navigate in cities. Governments are concerned that travel behavior will shift toward a car-driven and homeworking future, shifting demand away from public transport use. These concerns place the recovery of public transport in a possible crisis. A resilience perspective may aid the discussion around recovery–particularly one that deviates from pre-pandemic behavior. This paper presents an empirical study of London's public transport demand and introduces a perspective of spatial resilience to the existing body of research on post-pandemic public transport demand. This study defines spatial resilience as the rate of recovery in public transport demand within census boundaries over a period after lockdown restrictions were lifted. The relationship between spatial resilience and urban socioeconomic factors was investigated by a global spatial regression model and a localized perspective through Geographically Weighted Regression (GWR) model. In this case study of London, the analysis focuses on the period after the first COVID-19 lockdown restrictions were lifted (June 2020) and before the new restrictions in mid-September 2020. The analysis shows that outer London generally recovered faster than inner London. Factors of income, car ownership and density of public transport infrastructure were found to have the greatest influence on spatial patterns in resilience. Furthermore, influential relationships vary locally, inviting future research to examine the drivers of this spatial heterogeneity. Thus, this research recommends transport policymakers capture the influences of homeworking, ensure funding for a minimum level of service, and advocate for a polycentric recovery post-pandemic. © 2023 Wuhan University. Published by Informa UK Limited, trading as Taylor & Francis Group.

Propagation research of SARS-CoV-2 based on evolutionary tree and spectral clustering

RNA viruses have the characteristics of a high mutation rate. New Coronavirus (SARS-CoV-2), as a RNA virus, has been mutated to some extent since the outbreak of New Coronavirus pneumonia (COVID-19). It is of great significance to study the evolution and variation of novel coronavirus genes to analyze the source of virus infection and understand the evolution of viruses. This research is based on the Novel Coronavirus 2019 database at the National Genomics Data Center. We combined macro and micro. We used the phylogenetic tree to analyze the gene fragments of the virus, constructed an evolutionary tree with a depth of 301, searched the root node of the tree to find the source of the virus in the data set and used spectral clustering to analyze the degree of novel Coronavirus variation in each country and the clustering results were visualized to make them easier to observe. The experimental results show that the strain sample at the top of the evolutionary tree originated in New Zealand based on the existing data. In the evolutionary tree, the evolutionary process of the virus can be divided into three branches. After clustering the virus source data and constructing the visual map of the variation degree of SARS-COV-2, we found that the viruses in South Africa, New Zealand and other countries had a higher degree of variation, and the viruses in Australia, the United States and other countries have a relatively lower degree of virus variation. © 2022 IEEE.

China's Government Information Capacity, Medical Resource Allocation and COVID-19 Prevention and Control

The fight against the COVID-19 epidemic is a war against an "invisible enemy". Access to accurate information and appropriate allocation of medical resources are key to containing the spread of the virus as soon as possible. The Chinese government has great power to collect information from individuals and basic-level organizations. It also has strong ability to pool and allocate medical resources. The fight against COVID-19 can be deemed as a quasi-natural experiment and based on this, we examine how government information capacity and medical resource allocation influence epidemic prevention and control in 286 Chinese cities (prefecture level and above). The findings are as follows: (1) Government information capacities improve the effectiveness of prevention and control policies. At city level, for every 0.1 point of increase in government information capacity score, the number of confirmed cases will reduce by 66.5, and the number of deaths per 10000 people will be down by 0.008. (2) The quantity of medical resources available has no direct influence on the effectiveness of epidemic prevention and control, but higher allocation efficiency does bring higher effectiveness. (3) The government can, on the one hand, allocate public resources based on information, and on the other hand guide the flow of social resources by releasing relevant information. Both can improve the allocation efficiency of medical resources. These findings have some policy implications for improving global emergency management. © 2021 Cheng Liu et al., published by De Gruyter.

US County-level Risk Factors Associated with COVID-19 Exacerbation During the Vaccination Era

The COVID-19 pandemic has caused unprecedented challenges to public health and disruption to everyday life. The news in 2020 was dominated by the worldwide spread of COVID-19, overwhelming healthcare providers and drastically changing people's lives. In 2021, the release of vaccines from multiple pharmaceutical companies changed the focus to ending the pandemic through mass inoculation. Nevertheless, the vaccine acceptance rate differs significantly across US counties, ranging from 99% to 0.1%. Our study investigates the principal risk factors in predicting COVID-19 infection and mortality rates at the county level during the early vaccination era. We are particularly interested in the role of vaccination in curbing the exacerbation of COVID-19. To this end, we first compare the efficacy of six established machine learning algorithms to predict county-level infection and mortality rates. Next, we perform risk factor analysis by identifying common principal predictors revealed by the models. Our experimental results suggest that vaccination plays an essential role in limiting COVID-19 infection and mortality. Furthermore, socioeconomic factors (e.g., severe housing problems and median household income) are more predictive of county-level mortality rate than intuitive features such as availability of healthcare resources (e.g., total numbers of hospitals/ICU beds/MDs). Our findings could provide additional insights to assist in COVID-19 resource allocation and priority setting. © 2022 IEEE.

Assessing Deep Learning Approaches in Detecting Masked Facial Expressions

Automated facial expression recognition (FER) is an active research area due to its practical importance in a wide range of applications. In recent years, deep learning-based approaches have delivered promising performances in FER, leveraging the latest advances in computer vision. However, mask-wearing after the onset of the COVID-19 pandemic has posed challenges for the existing models when the salient features from the masked region are unavailable. This study investigates what effects facial masks will bring to expression detection using state-of-the-art deep learners. Specifically, we evaluate three deep neural networks in recognizing six emotional categories on masked facial images and compare the results to unmasked counterparts reported by prior studies. We based our work on the FER2013 dataset and augmented regular face images with artificial masks utilizing the Dlib and OpenCV libraries. Our experimental results indicate that deep learning models can be effective in recognizing some masked expressions (e.g., 'Happy', 'Surprise', and 'Neural') but fall short on the others (e.g., 'Angry', 'Fear', 'Sad'). Furthermore, with the presence of facial masks, angry faces are most likely to be misclassified as neural, and fear is the most challenging emotion to detect. © 2022 IEEE.

Reports of clinical trial protocols of coronavirus disease 2019: A systematic review

Background: To examine the outcomes heterogeneity of clinical trial protocols of coronavirus disease 2019 (COVID-19) to prioritize the establishment of a core outcome set. Methods: Databases of the International Committee of Medical Journal Editors - accepted clinical trial registry platforms were searched on February 14, 2020 and May 31, 2020. Randomized controlled trials and non-randomized controlled trials of COVID-19 were considered. Patient condition was classified as common, severe, or critical. Interventions included traditional Chinese medicine and Western medicine. We excluded trials that involved discharged patients, psychological intervention, and complications of COVID-19. The general information and outcomes, outcome measurement instruments, and measurement times were extracted. The results were analyzed by descriptive analysis. Results: In all, 19 registry platforms were searched. A total of 97 protocols were selected from among 160 protocols for the first search. For protocols of traditional Chinese medicine clinical trials, 76 outcomes from 16 outcome domains were reported, and almost half (34/76, 44.74%) of the outcomes were reported only once;the most frequently reported outcome was time taken for severe acute respiratory syndrome coronavirus 2 RNA to become negative. Twenty-seven (27/76, 35.53%) outcomes provided one or more outcome measurement instruments. Ten outcomes provided one or more measurement time frame. For protocols of Western medicine clinical trials, 126 outcomes from 17 outcome domains were reported;almost half (62/126, 49.21%) of the outcomes were reported only once;the most frequently reported outcome was proportion of patients with negative severe acute respiratory syndrome coronavirus 2. Twenty-seven outcomes provided one or more outcome measurement instruments. Forty (40/126, 31.75%) outcomes provided one or more measurement time frame. There were > 40 duplicated outcomes between the clinical trials protocols of traditional Chinese medicine and western medicine protocols. All of them were included in the Delphi survey when developing core outcome set for COVID-19. A total of 1,027 protocols were selected from 2,741 protocols for the second search. Forty-two new outcomes and 47 new outcome measurement instruments were reported. Conclusion: Outcome reporting in clinical trial protocols of COVID-19 is inconsistent. Thus, establishing a core outcome set is necessary for diagnosis and management.

Did the Communication Barriers During the Lockdown Reduce Students' Satisfaction?

The lockdown control measures implemented against the pandemic of COVID-19 have had a global effect on various aspects of our lives as a society. Considering the impact of the lockdown caused by COVID-19 on adolescents, we conducted practical longitudinal research on the changes in adolescent satisfaction before and after lockdown. A total of 221 students aged 13-19 years from a professional adolescent football school in China participated in a self-report satisfaction questionnaire before and after the lockdown. The results showed that the satisfaction of adolescents improved significantly after the lockdown. There were significant differences based on age in the improvement rate, but the correlation between the students' home regions (and how they were affected by COVID-19) and satisfaction improvement was not significant. To examine the possible reasons behind the improvement in adolescent satisfaction, we then analyzed in detail the online teaching and training methods implemented by the school during the lockdown. Based on this investigation, we outlined recommendations to guide future practice. This research is expected to deepen the theory and practice associated with the development of Chinese adolescent teaching, which may be applied to other training institutions.

How do green financing and green logistics affect the circular economy in the pandemic situation: key mediating role of sustainable production

Emerging economies are striving to realize their potential for sustainable production in achieving zero-carbon agenda. Due to natural resource constraints, businesses must focus on green production resources to develop the circular economy. Therefore, this study aims to identify the key role of green financing and logistics in adopting sustainable production and circular economy. We have collected the data from 240 respondents from the Chinese manufacturing sector following the COVID-19 peak in late 2020 and analyzed using structural equation modeling. As per research findings, green financing and green logistics have a significant and positive effect on sustainable production and the circular economy. Second, sustainable production has a significant positive influence on the circular economy. Manifestly, sustainable production was discovered to play an important mediating role among these variables. Besides, the novel Importance-performance map analysis shows each constructs performance and importance value towards the circular economy. This paper contributed to the literature and highlighted the importance of each construct. Moreover, the study findings implied that green financing and green logistics should be integrated into organizational procuring and financing strategies for manufacturing green and sustainable goods, and advancing the circular economy goals.

Sentiment analysis for news and social media in COVID-19

During the COVID-19 epidemic, the news is overwhelming in people's daily life. So, we aim to extract key information from a large amount of public news. This paper focus on the daily sentiment distribution of news and public opinion on Weibo that refers to the key word COVID-19. First, we refining the key news from all the news in a day to deal with long and large news data. Second, we transformer the headline into a high-dimensional vector. And then, divided them into k categories on the strength of k-means clustering algorithm. Finally, choose the closet news to the mean vector as the key news of the day. Moreover, we conduct sentiment analysis on all key news and Weibo data. By comparing the sentiment trend of news and Weibo, this study provides a new channel to analyze social public opinion. © 2020 ACM.

Mathematical Modeling of Business Reopening When Facing SARS-CoV-2 Pandemic: Protection, Cost, and Risk

The sudden onset of the coronavirus (SARS-CoV-2) pandemic has resulted in tremendous loss of human life and economy in more than 210 countries and territories around the world. While self-protections such as wearing masks, sheltering in place, and quarantine policies and strategies are necessary for containing virus transmission, tens of millions of people in the U.S. have lost their jobs due to the shutdown of businesses. Therefore, how to reopen the economy safely while the virus is still circulating in population has become a problem of significant concern and importance to elected leaders and business executives. In this study, mathematical modeling is employed to quantify the profit generation and the infection risk simultaneously from a business entity's perspective. Specifically, an ordinary differential equation model was developed to characterize disease transmission and infection risk. An algebraic equation is proposed to determine the net profit that a business entity can generate after reopening and take into account the costs associated of several protection/quarantine guidelines. All model parameters were calibrated based on various data and information sources. Sensitivity analyses and case studies were performed to illustrate the use of the model in practice. The results show that with a combination of necessary infection protection measures implemented, a business entity may stand a good opportunity to generate a positive net profit while successfully controlling the within-business infection prevalence under that in the general population. The use of personal protective equipment (PPE) is also found of significant importance, especially at the early stage of business reopening. © Copyright © 2020 Miao, Gao, Feng, Zhong, Zhu, Wu, Swartz, Luo, DeSantis, Lai, Bauer, Pérez, Rong and Lairson.