Factors influencing the interval from isolation to diagnosis of local COVID-19 in Shaanxi Province.

Objective To understand the epidemiological characteristics of COVID-19 in Shaanxi Province from December 9, 2021 to January 20, 2022, and analyze the factors influencing the interval from isolation to diagnosis. Methods We collected the data of local COVID-19 cases from December 9, 2021 to January 20, 2022 published on the official website of Health Commission of Shaanxi Province. Descriptive statistical method was used to analyze the epidemiological characteristics of COVID-19 in Shaanxi Province. Mann-Whitney U test and Kruskal-Wallis H test were used to compare the differences between groups. The unconditional Logistic regression model was applied to analyze the factors influencing the interval between isolation and diagnosis. Results The outbreak of COVID-19 in Shaanxi Province started on December 9, 2021 and ended on January 20, 2022. The overall change trend of the outbreak showed an "inverted V" shape. A total of 2,080 confirmed local cases were reported, and the main type of disease was mild, with an incidence rate of 5.26/100,000. Xi'an had the most cases, accounting for 98. 69% of the total. The reported cases were mainly concentrated in people aged 21 to 55 years old, with a male-to-female sex ratio of 1.19:1. The median interval from isolation to diagnosis was 3 days, the shortest interval being 0 day and the longest interval being 21 days. Unconditional Logistic regression model analysis showed that the way of finding cases was the factor influencing the interval from isolation to diagnosis. Compared with the way of isolation of the key population, the way of the nucleic acid screening could reduce the risk of late detection of confirmed cases by 89% (OR = 0.11, 95% CI: 0. 07 - 0.16). Conclusion The way of finding cases is the factor influencing the interval from isolation to diagnosis. In the face of the recent intensification of the spread of Omicron variant in mainland China, accurate and rapid identification and detection of confirmed cases can not only reduce the risk of the spread of the epidemic, but also endeavor more time and initiative for the treatment of patients, which is the key to curbing the spread of the epidemic.Copyright © 2023 Xi'an Medical University. All rights reserved.

Developing a Place–Time-Specific Transmissibility Index to Measure and Examine the Spatiotemporally Varying Transmissibility of COVID-19

The transmission rate of COVID-19 varies by location and time. A proper measure of the transmissibility of an infectious disease should be place- and time-specific, which is currently unavailable. This research aims to better understand the spatiotemporally changing transmissibility of COVID-19. It contributes to COVID-19 research in three ways. First, it presents a generally applicable modeling framework to estimate the transmissibility of COVID-19 in a specific place and time based on daily reported case data, called space-time effective reproduction number, denoted as (Formula presented.) Then, the developed model is used to create a spatiotemporal data set of (Formula presented.) values at the county level in the United States. Second, it investigates relationships between (Formula presented.) and dynamically changing context factors with multiple machine learning and spatial modeling techniques. The research examines the relationships from a cross-sectional perspective and a longitudinal perspective separately. The longitudinal view allows us to understand how local human dynamics and policy factors influence changes in (Formula presented.) over time in the place, whereas the cross-sectional view sheds light on the demographic, socioeconomic, and environmental factors behind spatial variations of (Formula presented.) at a specific time slice. Some general trends of the relationships are found, but the level of impact by each context factor varies geographically. Third, the best performing local longitudinal models have promising potential to simulate or forecast future transmissibility. The random forest and the exponential regression models based on time-series data gave the best performances. These models were further evaluated against ground truth data of county-level reported cases. Their good prediction accuracies in the case study prove that these machine learning models are promising in their ability to predict transmissibility in hypothetical or foreseeable scenarios. © 2023 by American Association of Geographers.

Factors influencing the interval from isolation to diagnosis of local COVID-19 in Shaanxi Province.

Objective To understand the epidemiological characteristics of COVID-19 in Shaanxi Province from December 9, 2021 to January 20, 2022, and analyze the factors influencing the interval from isolation to diagnosis. Methods We collected the data of local COVID-19 cases from December 9, 2021 to January 20, 2022 published on the official website of Health Commission of Shaanxi Province. Descriptive statistical method was used to analyze the epidemiological characteristics of COVID-19 in Shaanxi Province. Mann-Whitney U test and Kruskal-Wallis H test were used to compare the differences between groups. The unconditional Logistic regression model was applied to analyze the factors influencing the interval between isolation and diagnosis. Results The outbreak of COVID-19 in Shaanxi Province started on December 9, 2021 and ended on January 20, 2022. The overall change trend of the outbreak showed an "inverted V" shape. A total of 2,080 confirmed local cases were reported, and the main type of disease was mild, with an incidence rate of 5.26/100,000. Xi'an had the most cases, accounting for 98. 69% of the total. The reported cases were mainly concentrated in people aged 21 to 55 years old, with a male-to-female sex ratio of 1.19:1. The median interval from isolation to diagnosis was 3 days, the shortest interval being 0 day and the longest interval being 21 days. Unconditional Logistic regression model analysis showed that the way of finding cases was the factor influencing the interval from isolation to diagnosis. Compared with the way of isolation of the key population, the way of the nucleic acid screening could reduce the risk of late detection of confirmed cases by 89% (OR = 0.11, 95% CI: 0. 07 - 0.16). Conclusion The way of finding cases is the factor influencing the interval from isolation to diagnosis. In the face of the recent intensification of the spread of Omicron variant in mainland China, accurate and rapid identification and detection of confirmed cases can not only reduce the risk of the spread of the epidemic, but also endeavor more time and initiative for the treatment of patients, which is the key to curbing the spread of the epidemic.Copyright © 2023 Xi'an Medical University. All rights reserved.

Developing and Validating a Scale for University Teacher's Caring Behavior in Online Teaching

Teacher caring behavior in higher education has been frequently studied in the context of face-to-face instruction. The COVID-19 pandemic has transformed the territory of high education such that synchronous or asynchronous online instruction has become an important component of college students' learning experience. The lack of valid and reliable scales makes it difficult to quantitatively examine teachers' caring behavior in online contexts. Building on existing literature, we designed and implemented a three-stage study that aimed to develop and validate a scale for measuring Chinese university teachers' online caring behavior from students' perspectives. Results from data analysis have shown that the scale has construct validity and internal consistency reliability. The scale has revealed that teacher caring behavior in an online context consists of three latent factors, namely, inclusiveness, support, and conscientiousness. This is consistent with the existing conceptualization of teacher care as a three-dimensional construct. The scale made targeted improvement of existing scales and can be used to quantitatively examine the relationship between teachers' caring behavior and students' academic performance, learning motivation, learning engagement, learning self-efficacy, sense of belonging, and mental health. © 2023 by the authors.

Relational Ethics of Globalization under the COVID-19 Pandemic

Globalization has been going on for a long process, although controversial, never stopping the pace of development. Since the outbreak of COVID-19 which profoundly changed human society and human life, globalization has been facing unprecedented resistance and challenges. Returning to various debates on globalization ethics, analyzing various problems that occur in the process of globalization development, this article starts from relational ethics, aiming to demonstrate the rationality of the sustainable development of globalization in the post-pandemic era. It will argue that although globalization will have new forms and contents under the new situation, the overall trend will not be reversed. It stresses the significance and urgency to explore the discourse construction of the human community with a shared future and the relational ethics of globalization in the post-pandemic era from the perspectives of history, reality, and methodology.

Impacts of COVID-19 on financial markets: from the perspective of financial stress

This research analyzes the impacts of COVID-19 on financial markets from the perspective of financial stress. Effects on financial stress and its cross-border spillovers are investigated, respectively. The results show that the stress of a country's financial system is positively correlated with the severity of COVID-19 pandemic the country experiences. Although the volatilities of financial stress increased slightly, the cross-border spillovers have increased significantly after the COVID-19 outbreak. We provide a new insight to the impacts of the COVID-19 and suggest a more prudent look at financial risk management. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

Effects of members' response styles in an online depression community based on text mining and empirical analysis

The online depression community (ODC) has become a popular resource for people with depression to manage their mental health during the COVID-19 pandemic. This study proposed a novel perspective based on response style theory to investigate whether depression individuals' distractive and ruminative behaviors in ODC were related to social support received and co-rumination. Furthermore, we explored the influences of social support and co-rumination on suicidal behaviors using panel data set. We collected text data from 22,286 depressed users of a large ODC in China from March 2020 to July 2021, and conducted text mining and econometrics analyses to test our research questions. The results showed that depression users' online ruminative behaviors had a positive relationship with the co-rumination and had a negative relationship with social support received. Besides, constructive distractive behaviors (i.e., providing social support to others) increased the support users received from others but had a negative relationship with co-rumination. Depression users' future suicidal behaviors are influenced by past received social support and co-rumination. The received social supports and co-rumination have a negative and positive influence on depression users' future suicidal behaviors, respectively. Our results enrich the application of response style theory in online medicine. They provide meaningful insights into behaviors that influence the acquisition of online social support and the incidence of online co-rumination in ODCs. This study helps relevant institutions to conduct more targeted online suicide interventions for depression patients. © 2022 Elsevier Ltd

Research Progress and Implication of Wastewater Surveillance of SARS-CoV-2

Since the outbreak of the coronavirus disease 2019 (COVID-19), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been found in wastewater frequently worldwide. Based on the wastewater-based epidemiology (WBE), wastewater surveillance of SARS-CoV-2 can complement population surveillance for COVID-19. Quantification of viral load and genome sequencing of SARS-CoV-2 can help early warning of COVID-19 outbreaks, early identification of asymptomatic cases, assessment of infection scale, prediction of pandemic trend status, and identification of virus sources to provide scientific basis for polices for the prevention and control. Accordingly, here, the sources of SARS-CoV-2 in wastewater at home and abroad and the major factors affecting the survival of virus were reviewed. Common methods to concentrate, detect and quantify SARS-CoV-2 were reviewed, with an overview of global surveillance projects, progresses, and remaining scientific issues. Some shortcomings of the current procedures, including the lack of sufficient information on distribution characteristics and infectivity of SARS-CoV-2 in wastewater and limited development and application of prediction models were also discussed. WBE can provide insight into the scientific prevention and control of COVID-19 in the face of current or future pandemics in China, and enhance China′s ability to deal with the surveillance and early warning, epidemic scale assessment, and accurate policy-making for the infectious and non-infectious diseases. © 2022 Editorial Board, Research of Environmental Sciences. All rights reserved.

Impacts of COVID-19 pandemic on preterm birth: a systematic review and meta-analysis.

OBJECTIVES: The COVID-19 pandemic has significantly affected healthcare systems and daily well-being. However, the reports of the indirect impacts of the pandemic on preterm birth remain conflicting. We performed a meta-analysis to examine whether the pandemic altered the risk of preterm birth. STUDY DESIGN: This was a systematic review and meta-analysis of the previous literature. METHODS: We searched MEDLINE and Embase databases until March 2022 using appropriate keywords and extracted 63 eligible studies that compared preterm between the COVID-19 pandemic period and the prepandemic period. A random effects model was used to obtain the pooled odds of each outcome. The study protocol was registered with PROSPERO (No. CRD42022326717). RESULTS: The search identified 3827 studies, of which 63 reports were included. A total of 3,220,370 pregnancies during the COVID-19 pandemic period and 6,122,615 pregnancies during the prepandemic period were studied. Compared with the prepandemic period, we identified a significant decreased odds of preterm birth (PTB; <37 weeks' gestation; pooled odds ratio [OR; 95% confidence interval (CI)] = 0.96 [0.94, 0.98]; I2 = 78.7%; 62 studies) and extremely PTB (<28 weeks' gestation; pooled OR [95% CI] = 0.92 [0.87, 0.97]; I2 = 26.4%; 25 studies) during the pandemic, whereas there was only a borderline significant reduction in the odds of very PTB (<32 weeks' gestation; pooled OR [95% CI] = 0.93 [0.86, 1.01]; I2 = 90.1%; 33 studies) between the two periods. There was significant publication bias for PTB. CONCLUSION: Pooled results suggested the COVID-19 pandemic was associated with preterm birth, although there was only a borderline significant reduction for very PTB during the pandemic compared with the prepandemic period. Large studies showed conflicting results, and further research on whether the change is related to pandemic mitigation measures was warranted.

A Model of the Impacts of Online Product Review Features on Trust in Ecommerce

The pandemic of COVID-19 makes consumers rely on e-commerce to an even higher extent. Since consumers utilize online product reviews (OPRs) for decision making, it is vital to study how various OPR features impact consumer behavior. This research proposes a model for understanding effects of OPR features on consumers’ trust based on HSM. For data collection, we carry out lab experiments and use PLS-SEM analysis to test the model. The results indicate that OPR features influence trust via usefulness of OPRs and attitude toward website. We contribute to the OPR literature by applying the HSM model to the context of OPRs. We also examine the interactions between heuristic mode and systematic mode. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

AdaD-FNN for Chest CT-Based COVID-19 Diagnosis

Coronavirus disease 2019 (COVID-19) generated a global public health emergency since December 2019, causing huge economic losses. To help radiologists strengthen their recognition of COVID-19 cases, we developed a computer-aided diagnosis system based on deep learning to automatically classify chest computed tomography-based COVID-19, Tuberculosis, and healthy control subjects. Our novel classification model AdaD-FNN sequentially transfers the trained knowledge of an FNN estimator to the next FNN estimator while updating the weights of the samples in the training set with a decaying learning rate. This model inhibits the network from remembering the noisy information and improves the learning of complex patterns in the hard-to-identify samples. Moreover, we designed a novel image preprocessing model F-U2MNet-C by enhancing the image features using fuzzy stacking and eliminating the interference factors using U2MNet segmentation. Extensive experiments are conducted on four publicly available datasets namely, TLDCA, UCSD-Al4H, SARS-CoV-2, TCIA, and the obtained classification accuracies are 99.52%, 92.96%, 97.86%, 91.97%. Our novel system gives out compelling performance for assisting COVID-19 detection when compared with 22 state-of-the-art methods. We hope to help link together biomedical research and artificial intelligence and to assist the diagnosis of doctors, radiologists, and inspectors at each epidemic prevention site in the real world.

Spatial and Temporal Transmission Differences between SARS and COVID-19 and Analysis of Influence Factors

The outbreaks of SARS and COVID-19 have had a serious impact on public health, social economy and so on in China, in order to reveal the common law and difference characteristics of space-time transmission of respiratory infectious diseases and the reasons behind them, using space-time statistical methods, systematically analyzed and compared the difference characteristics of space-time transmission between SARS and COVID-19, and combined with the transmission characteristics of the virus itself and temperature, traffic and other factors to analyze the causes. The study shows that, ① SARS experiences two stages, the rising period-flat phase, and the COVID-19 experiences three stages, the rising period-sharp rise-slow up period. ② In the mode of spatial transmission, the transmission intensity and range of COVID-19 is greater than that of SARS, and the overall connectivity of COVID-19 is greater and the provinces are more closely related to the outbreak of the virus. Both SARS and COVID-19 transmission have obvious spatial aggregation characteristics. They are based on proximity propagation and long-range leaps, and SARS has a secondary communication center, and COVID-19 diffusion center has not been relocated. ③ In the direction of space communication, SARS is centered in Beijing, Hong Kong and Guangdong, the direction of spatial communication is stronger, and COVID-19 is only spread outwards with Hubei as the center. ④ In terms of spatial transmission speed, the spread time of the first case in each province of SARS is relatively large, and the spread time of the first case in each province of COVID-19 is roughly divided by Hu Huanyong Line, showing a phenomenon of "fast in the east and slow in the west", and the spread time span is relatively short. ⑤ R0 is the main reason for the difference between the spatial transmission range of SARS and COVID-19 and the speed of spatial transmission. The temperature suitability of SARS and COVID-19 viruses is different, but spatial aggregation transmission and adjacent area transmission are occurring in areas with similar temperatures. Besides the virus transmission capacity and temperature impact, traffic is the main reason affecting SARS and COVID-19 space long-range leap transmission, and the spatial transmission speed of both is negatively related to the density of the road network. 2021, Science Press. All right reserved.

Influence Spread in Location-Based Social Network: An Efficient Algorithm of Epidemic Controlling

Much work has already been studied on the interrelation between the epidemic spreading and awareness spreading to prevent infections in a social network. By selecting seed users to spread awareness, we can control epidemic spreading. However, selecting seed users with the maximum influential users may not be the best solution in location-based social networks. Therefore, it is challenging to determine users to spread the information (the awareness of prevention) in these networks. The minimized epidemic infection (MEI) problem aims to find a seed set with k seed users such that the infection users can be minimized. In this article, we propose a piecewise function to measure the probability of each user being infected, which considers the distance and time. Then, we propose an algorithm called location-infected-greedy (LIG) to solve the MEI problem by finding the seed nodes that consider the probability of infection, time of check-in, location information, and influence of users. In the meantime, LIG can obtain an upper bound of the data-dependent approximate ratio, and it runs in O(kn(2)), where n is the total number of nodes and k is the number of seed nodes. Finally, extensive contrast experiments on real-world location-based social networks show that our algorithm is efficient and effective.

Opinions on the rehabilitation therapies of coronavirus disease 2019 (COVID-19)

The COVID-19 epidemic in China has been effectively controlled, a large number of patients have been released from isolation and discharged, and the treatment and rehabilitation programs in the recovery period need to be effectively implemented. The recovery of coronavirus disease 2019 is a syndrome of deficiency and solidity. The deficiency of qi and yin is dominant, and some patients have yang deficiency;phlegm and stasis are the main pathological products. We suggest that hierarchical management is recommended for patients in the recovery period. Asymptomatic patients with normal lung imaging results should take the respiratory rehabilitation program and have no need to take drugs. Patients with clinical symptoms with or without lung shadows, and patients who have no obvious symptoms but whose lung shadows are not completely absorbed, need to be treated according to syndrome differentiation.

Do cloud-based enterprise resource planning systems affect the productivity of human resources in the COVID-19 era?

Purpose This study aims to investigate the impact of skills and knowledge of employees, economic situations of the company, current IT infrastructure, payment fashion, cloud availability, and cloud privacy and security on the productivity of the human resources in the COVID-19 era. Design/methodology/approach Over the past few years, the advent of cloud-assisted technologies has dramatically advanced the Information Technology (IT)-based industries by providing everything as a service. Cloud computing is recognized as a growing technology among companies around the world. One of the most critical cloud applications is deploying systems and organizational resources, especially systems whose deployment costs are high. Manpower is one of the basic and vital resources of the organization, and organizations need an efficient workforce to achieve their goals. But, in the COVID-19 era, human resources' productivity can be reduced due to stress, high labor force, reduced organizational performance and profits, unfavorable organizational conditions, inability to manage and lack of training. Therefore, this study tries to investigate the productivity of human resources in the COVID-19 era. Data were collected from the medium-sized companies through a questionnaire. Distributed questionnaires were conducted on the Likert scale. The model is assessed using the structural equation modeling technique to examine its reliability and validity. The study is a library method and literature review. A case study was conducted through a questionnaire and statistical analysis by SPSS 25 and SMART-PLS. Findings Based on the findings, the skills and knowledge of employees, the economic situations of the company, payment fashion, cloud availability and the current IT infrastructures of the company have a positive impact on human resource efficiency in the COVID-19 era. But cloud privacy and security have a negative effect on the productivity of human resources. The findings can be the basis for companies and organizations in the COVID-19 era. Research limitations/implications This study has some restrictions that need to be considered in evaluating the obtained results. First, due to the prevalence of Coronavirus, access to information from the companies under study was limited. Second, this research may have overlooked other variables that affect human resource productivity in the COVID-19 era. Prospective researchers can examine the impact of Customer Relationship Management (CRM) and Supply Chain Management (SCM) on the human resource's productivity in the COVID-19 era. Practical implications The results of this research are applicable for all companies, their departments and human resources in the COVID-19 era. Originality/value In this paper, human resources' productivity in the COVID-19 era is pointed out. The presented new model provides a complete framework for investigating cloud-based enterprise resource planning systems affect the productivity of human resources in the COVID-19 era.

[Discussion on health protection and disinfection strategies for the prevention and control of COVID-19 based on public opinion].

This paper summarizes the development trend and characteristics of public opinion on health protection and disinfection strategies in the COVID-19 epidemic. The experience and deficiency of the strategies are discussed from the perspective of public opinion, and suggestions on how health protection and disinfection can help prevent and control infectious diseases are also put forward: to strengthen health protection and disinfection in key places and units; to evaluate health protection and disinfection effects as well as the transmission mechanism of virus in the environment; to establish a professional health protection and disinfection emergency science popularization mechanism and information release channel; to speed up the formulation and revision of health protection and disinfection standards related to the epidemic.

Wall, Gate and Self-Other Dynamics: A Confucian Ethics of Separation and Interconnection

This paper starts with the social and moral implications of wall in history and in the contemporary world, to usher in the early Confucian discourse on wall and gate. The Confucian discourse implies that walls either actual, virtual or symbolic are there to defend and/or to separate, while gates enable the managed access to and opening-up the self-imposed insularity or moderate the self-centred exclusiveness that walls imply. By way of reinterpretation and reconstruction, we will extract from a variety of Confucian discussions the ethical awareness that however strongly built, walls must be associated with gates, and that the wall and the gate are therefore locked in mutuality to make possible the reality of interconnectedness between the inside and the outside and between the self and the other. It will be argued that by using ethical virtues as tools to moderate separation and exclusiveness, Confucian discourses highlight the dynamics of the self-other relationship, and establishes an ethics that may well be still applicable to contemporary situations and can be drawn upon to help dissolve the tension between the values of populist self-centrism and those of globalist interconnectedness.