Research progress on influence of environmental and meteorological parameters on transmission of coronavirus disease 2019

Novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) is spreading rapidly around the world and has become a global pandemic. Meteorological factors have been recognized as one of the critical factors that influence the epidemiology and transmission of infectious diseases. In this context, the World Meteorological Organization and scholars at home and abroad have paid extensive attention to the relationships of environment and meteorology with COVID-19. This paper systematically collected and sorted out relevant domestic and foreign studies, and reviewed the latest research progress on the impact of environmental and meteorological factors on COVID-19, classifying them into typical meteorological factors (such as temperature, humidity, and wind speed), local environmental factors (such as indoor enclosed environment, ventilation, disinfection, and air conditioning), and air pollution. Current research evidence suggests that typical meteorological factors, local environmental factors, and air pollutants are closely related to the transmission of COVID-19. However, the results of different studies are still divergent due to uncertainty about the influencing mechanism, and differences in research areas and methods. This review elucidated the importance of environmental and meteorological factors to the spread of COVID-19, and provided useful implications for the control of further large-scale transmission of COVID-19 and the development of prevention and control strategies under different environmental and meteorological conditions.Copyright © 2022, Shanghai Municipal Center for Disease Control and Prevention. All rights reserved.

Real estate investment decisions in COVID-19 crisis: the effect of perception and behavioral biases

Purpose: This study aims to examine the interconnectedness between investors' perceptions of assets and their behavioral factors with investment decisions during the COVID-19 pandemic in the real estate business in Pakistan. In this regard, this study predicted investment decisions using individuals' perceptions of the asset (perceived asset quality, perceived asset price and perceived asset value [PAV]),and behavioral biases (overconfidence [OC], herding [HD], disposition effect [DE] and risk aversion [RA]). Design/methodology/approach: This study used a survey-based instrument to gather a total of 189 usable samples. The sample data were analyzed using partial least square structural equation modeling. Findings: The findings of this study indicated that PAV, OC and HD significantly predicted the investment decision, whereas DE and RA had an insignificant impact on investment decisions in the real estate business. In addition, this study found that PAV is the most important factor to predict investment decisions in real estate during the COVID-19 crisis. Originality/value: The authors are certain that the study findings reinforce policy implications for regulators, policymakers and financial institutions. The study findings are also useful and relevant if the real estate sector experiences a crisis in the future. © 2023, Emerald Publishing Limited.

Risk signals mining of ribavirin and interferon-alpha based on the US FDA Adverse Event Reporting System database.

Objective: To identify which induced the symptoms/signs and laboratory abnormal findings occurred in patients with novel coronavirus pneumonia, by disease itself or by ribavirin and interferon-alpha treatments, through mining the adverse events (AEs) signals of the 2 antivirus agents. Method(s): According to the symptoms/signs and laboratory abnormal findings of novel coronavirus pneumonia mentioned in the literature and "Diagnosis and Treatment scheme of Novel Coronavirus Pneumonia (trial version 5)", AEs in this study were selected. Related data were collected from the U.S. FDA Adverse Events Reporting System (FARES) from Jan 1, 2004 to Dec 31, 2019, and the reporting odds ratio (ROR) method was used for signals detection for the above-mentioned 2 drugs. Result(s): A total of 7 582 463 AEs related to drugs were reported in the FAERS database, of which 31 775 related to ribavirin and 2 345 related to interferon-alpha. The results showed that AEs related to ribavirin in respiratory, thoracic, and mediastinal disorders were nasal congestion, cough, laryngeal pain, pharyngeal oedema, productive cough, and dyspnoea;AEs related to interferon-alpha were laryngeal pain and haemoptysis. In other system organ class, AEs related to above 2 drugs were pyrexia, feeling cold, pyrexia, nausea, vomiting, diarrhoea, headache, arthralgia, myalgia, and rash. AEs of laboratory abnormal results related to ribavirin were white blood cell/platelet count decrease and aspartate/alanine aminotransferase increase;AEs related to interferon-alpha were white blood cell/platelet count decrease, aspartate/alanine aminotransferase increase, and lymphocyte count decrease. Conclusion(s): Some AEs induced by ribavirin and interferon-alpha were similar to symptoms/signs and laboratory abnormal findings of novel coronavirus pneumonia, which should be distinguished in the clinical practice.Copyright © 2020 by the Chinese Medical Association.

Lipid-Based Poly(I:C) Adjuvants Strongly Enhance the Immunogenicity of SARS-CoV-2 Receptor-Binding Domain Vaccine

Background The outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has greatly threatened public health. Recent studies have revealed that the spike receptor-binding domain (RBD) of SARS-CoV-2 is a potent target for vaccine development. However, adjuvants are usually required to strengthen the immunogenicity of recombinant antigens. Different types of adjuvants can elicit different immune responses. Methods We developed an RBD recombinant protein vaccine with a polyriboinosinic acid-polyribocytidylic acid [poly(I:C)] adjuvant to evoke a strong immune response. The delivery of poly(I:C) was optimized in two steps. First, poly(I:C) was complexed with a cationic polymer, poly-l-lysine (PLL), to form poly(I:C)-PLL, a polyplex core. Thereafter, it was loaded into five different lipid shells (group II, III-1,2-distearoyl-sn-glycero-3-phosphocholine [DSPC], III-1,2-dioleoyl-sn-glycero-3-phosphoethanolamine [DOPE], IV-DOPE, and IV-DSPC). We performed an enzyme-linked immunosorbent assay and enzyme-linked immunosorbent spot assay to compare the ability of the five lipopolyplex adjuvants to enhance the immunogenicity of the SARS-CoV-2 RBD protein, including humoral and cellular immune responses. Finally, the adjuvant with the highest immunogenicity was selected to verify the protective immunity of the vaccine through animal challenge experiments. Results Recombinant RBD protein has low immunogenicity. The different adjuvants we developed enhanced the immunogenicity of the RBD protein in different ways. Among the lipopolyplexes, those containing DOPE (III-DOPE and IV-DOPE) elicited RBD-specific immunoglobulin G antibody responses, and adjuvants with four components elicited better RBD-specific immunoglobulin G antibody responses than those containing three components (P < 0.05). The IC50 and IC90 titers indicated that the IV-DOPE lipopolyplex had the greatest neutralization ability, with IC50 titers of 1/117,490. Furthermore, in the challenge study, IV-DOPE lipopolyplex protected mice from SARS-CoV-2 infection. On the fourth day after infection, the average animal body weights were reduced by 18.56% (24.164 ± 0.665 g vs. 19.678 ± 0.455 g) and 0.06% (24.249 ± 0.683 g vs. 24.235 ± 0.681 g) in the MOCK and vaccine groups, respectively. In addition, the relative expression of viral RNA in the vaccinated group was significantly lower than that in the MOCK group (P < 0.05). Interstitial inflammatory cell infiltration was observed in the MOCK group, whereas no obvious damage was observed in the vaccinated group. Conclusions The IV-DOPE-adjuvanted SARS-CoV-2 recombinant RBD protein vaccine efficiently protected mice from SARS-CoV-2 in the animal challenge study. Therefore, IV-DOPE is considered an exceptional adjuvant for SARS-CoV-2 recombinant RBD protein-based vaccines and has the potential to be further developed into a SARS-CoV-2 recombinant RBD protein-based vaccine. © Wolters Kluwer Health, Inc. All rights reserved.

Geospatial Network Analysis and Origin-Destination Clustering of Bike-Sharing Activities during the COVID-19 Pandemic

Bike-sharing data are an important data source to study urban mobility in the context of the coronavirus disease 2019 (COVID-19). However, studies that focus on different bike-sharing activities including both riding and rebalancing are sparse. This limits the comprehensiveness of the analysis of the impact of the pandemic on bike-sharing. In this study, we combine geospatial network analysis and origin-destination (OD) clustering methods to explore the spatiotemporal change patterns hidden in the bike-sharing data during the pandemic. Different from previous research that mostly focuses on the analysis of riding behaviors, we also extract and analyze the rebalancing data of a bike-sharing system. In this study, we propose a framework including three components: (1) a geospatial network analysis component for a statistical and spatiotemporal description of the overall riding flows and behaviors, (2) an origin-destination clustering component that compensates the network analysis by identifying large flow groups in which individual edges start from and end at nearby stations, and (3) a rebalancing data analysis component for the understanding of the rebalancing patterns during the pandemic. We test our framework using bike-sharing data collected in New York City. The results show that the spatial distribution of the main riding flows changed significantly in the pandemic compared to pre-pandemic time. For example, many riding trips seemed to expand the purposes of riding for work-home commuting to more leisure activities. Furthermore, we found that the changes in the riding flow patterns led to changes in the spatiotemporal distributions of bike rebalancing, such as the shifting of the rebalancing peak time and the increased ratio between the number of rebalancing and the total number of rides. Policy implications are also discussed based on our findings.

A Structural Examination and Exploration of Chinese University Students' Satisfaction with MOOCs

Due to the impact of the COVID-19 pandemic, Chinese universities are suspending offline teaching in favor of online education, with MOOCs as one of the main forms of education, and more and more students are attending MOOCs. Satisfaction surveys can reasonably explore the effectiveness of MOOCs, promoting their high-quality development and improving education reform in Chinese colleges and universities. In this paper, we have designed a questionnaire on the satisfaction of Chinese university students with MOOCs during the COVID-19 pandemic and found that three factors, specifically the quality of MOOCs, the teaching method, and the student's self-adjustment ability of learning, have a positive and significant impact on students' satisfaction with MOOCs. © 2022 ACM.

[Progress in research of 2019-nCoV infection in children and adolescents].

As 2019-nCoV vaccine is widely used in the adult population, children and adolescents have gradually become an important susceptible population to 2019-nCoV due to their low coverage of the vaccine and high contact degree. Understanding the characteristics of 2019-nCoV infection in minors is vital. This paper summarized the progress in the research of 2019-nCoV in minors by using the data from the retrieval of recently published literature. Furthermore, the key characteristics of 2019-nCoV infection in minors, including the susceptibility, transmissibility, prognosis and immune response were analyzed and the progress in child and adolescent vaccine development was introduced.

Temporal and spatial analysis of the impact of epidemic situation on tourists' emotional fluctuation in China based on text mining

After the outbreak of COVID in Wuhan, it has had an impact on all aspects of tourism industry. Tourists' sentiment is an important factor for people to make tourism decisions. The implementation of tourism decisions affects the development of tourism to a certain extent. In order to explore the impact of the COVID-19 on the tourism industry from the micro level of tourist sentiment. Firstly, the text mining algorithm is used to analyze the emotion of tourism microblog text, and the tourism emotion index TSI is constructed. Then combined with the tourism heat index THI, the tourist sentiment TS comprehensive index is constructed. The temporal and spatial differences of the impact of the epidemic on tourists' emotion are analyzed by comparing the tourists' emotion and epidemic data in different regions and stages. From the temporal and spatial distribution of tourist sentiment and epidemic situation, they are not completely parallel related, and there is spatial heterogeneity. Tourist sentiment is affected by multiple factors such as economic level and geographical location. The change of tourists' mood does not only depend on the change of epidemic data, but also related to many factors such as economic level and geographical location. © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.

[Epidemiological characteristics of 2019-nCoV Delta variant in Gansu province].

Objective: To analyze the epidemiological characteristics of COVID-19 caused by 2019-nCoV Delta variant (B.1.617.2) in Gansu province, and provide evidence for the prevention and control of COVID-19. Methods: The information of COVID-19 cases, including demographic characteristics, epidemiological history, onset date, diagnosis date, exposure place, detection way and infection source, in Gansu from 17 October to 25 November, 2021 were collected. Software Excel 2016,SPSS 22 and ArcGIS 10.7 were used for data process and analysis. Results: As of November 25, 2021, a total of 146 COVID-19 cases had been reported in Gansu and the epidemic affected 10 counties (districts) in 5 cities. The epidemic of COVID-19 in Gansu had three stages: imported case stage,imported-local case stage and local case stage. The age of cases ranged from 1 to 87 years,and the cases in age group 18-59 years accounted for 59.59% (87/146). The male to female ratio of the cases was 1∶1.12 (69∶77). The cases were mainly people engaged in business services (17.12%, 25/146),retirees (15.75%, 23/146),students (13.70%, 20/146),the jobless and unemployed (12.33%, 18/146). In 3 epidemic stages, the cases aged 18-59 years accounted for 44.44%,54.41% and 70.00% respectively,showing an upward trend,and there were differences among different populations (trend χ2=23.24, P<0.001). Also, the incubation period of the cases tended to decrease, and severe cases accounted for 33.33% (6/18), 19.12% (13/68) and 3.33% (2/60) respectively, showing a downward trend. Community screening (25.34%, 37/146) and close contact screening were the main ways to detect cases,the cases detected by close contact screening in 3 epidemic stages accounted for 50.00% (9/18), 66.18% (45/68) and 86.67% (52/60) respectively. The epidemic had obvious case clustering in confined places,and the main exposure modes were living together (24.66%), working/studying together (11.64%), taking same transportation (9.59%) and dining together (9.59%). Conclusions: The COVID-19 epidemic in Gansu was caused by 2019-nCoV Delta variant from imported cases. The virus was mainly transmitted through travel, sharing transportation, dining together and home contact. The characteristics of COVID-19 epidemic in Gansu changed with time, the case's clinical symptoms were not obvious and the incubation period became shorter. The infections mainly occurred in group aged 18 years and above.

An AI-based Mask-Wearing Status Recognition and Person Identification System

In the tough COVID-19 pandemic, wearing a mask in daily life becomes an important habit. However, sometimes people forget to wear a mask or wear a mask incorrectly in careless. Hence, alarming the problem of protecting ourselves from COVID-19 becomes a key challenge. Unfortunately, at home security or oce security systems, wearing a mask lets the person identication lost its function. Hence, masked-person identication becomes an essential issue. This paper proposes an AI-based mask-wearing status recognition and person identication system for solving the above problems. The system consists of three stages, face detection based on MTCNN, mask-wearing status recognition, and person identication using MobileNetV2. Masked-person identication is one of the functions of the proposed system. The experimental results show that the face detector reaches almost 100% accuracy among 3000 images. The mask-wearing status recognition has a 96.1% test accuracy in 300 test images, and person identication achieves a 98% recognition rate. In summary, the eectiveness of the proposed system is proved by the high accuracy recognition rate. © 2021 CEUR-WS. All rights reserved.

Risk assessment of public health emergencies concerned in the mainland of China, January 2022

Objective: To assess the risk of public health emergencies, both the indigenous ones and the imported ones, which might occur in the mainland of China in January 2022.

Quality Evaluation of Image Dataset based on Label File

The quality of the dataset affects the accuracy of the artificial intelligence model, but it is a lot of work to manually detect errors related to the quality evaluation of the dataset, and it may not be possible to perform quality evaluation through simple viewing. Therefore, we propose an image dataset quality measurement model, including nine evaluation metrics, and analyze the evaluation metrics from three aspects: definition, calculation formula and description. Based on the label file, the quality of the dataset file and the content of the dataset is evaluated, and the evaluation standard is given to judge whether the quality of the dataset is qualified. The measurement model and evaluation criteria proposed in this article were verified against the Cifar-10 dataset and the COVID-CT dataset, and the problems of label accuracy and label category imbalance were found, which proved the effectiveness of the method in this paper. © 2021 IEEE.

Lift the veil of rumors: the impact of the characteristics of information sources on the effectiveness of rumors spreading

Purpose This paper aims to investigate the impacts of rumors' information characteristics on people's believing and spreading of rumors online. Design/methodology/approach This study employed a mixed-methods approach by combining qualitative and quantitative methods. In study 1, the authors explored different types of rumors and their information source characteristics through qualitative research. In study 2, the authors utilized the findings from study 1 to develop an empirical model to verify the impact of these characteristics on the public's behaviors of believing and spreading rumors by content analysis and quantitative research. Findings The results show that five information source characteristics - credibility, professionalism, attractiveness, mystery and concreteness - influence the spreading effect of different types of rumors. Research limitations/implications This study contributes to rumor spreading research by deepening the theory of information source characteristics and adding to the emerging literature on the COVID-19 pandemic. Practical implications Insights from this research offer important practical implications for policymakers and online-platform operators by highlighting how to suppress the spread of rumors, particularly those associated with COVID-19. Originality/value This research introduces the theory of information source characteristics into the field of rumor spreading and adopts a mixed-methods approach, taking COVID-19 rumors as a typical case, which provides a unique perspective for a deeper understanding of rumor spreading's antecedences.

The influence of the corporate social responsibility disclosures on consumer brand attitudes under the impact of COVID-19

This study focuses on the use of we-media by small- and medium-sized enterprises (SMEs) to disclose internal corporate social responsibility (ICSR) under the impact of the 2019 novel coronavirus disease (COVID-19). Study 1 interprets the catalyst effect of COVID-19 on the externalization of SMEs’ ICSR. The fuzzy grading evaluation method is initially verified. Under the impact of COVID-19, SMEs fulfilling their ICSR can enhance consumer brand attitudes. Study 2 uses a structural equation model and empirical analysis of 946 effective samples and finds that consumers perceive the self-sacrifice of corporations during the coronavirus disease period. SMEs can fulfill their ICSR to enhance the internal explanation mechanism of consumer brand attitudes and the moderating role of enterprise losses. © 2020, The Author(s).

Progress of artificial intelligence diagnosis and prognosis technology for COVID-19 medical imaging

In March 2020, the World Health Organization(WHO) declared the new corona virus pneumonia (COVID-19) as a world pandemic, which means that the epidemic has broken out worldwide. The outbreak of COVID-19 threatens the lives and property safety of countless people and brings great pressure to medical systems. The main clinical symptoms of COVID-19 are fever, cough, and fatigue, which may lead to a fatal complication: acute respiratory distress syndrome. The main challenge in inhibiting the spread of this disease is the lack of efficient detection methods. Although reverse transcription-polymerase chain reaction (RT-PCR) is the gold standard for confirming COVID-19, it takes 4-6 h to obtain the results, and the false-negative rate of RT-PCR detection is as high as 17%-25.5%. Therefore, multiple RT-PCR detections at intervals of several days must be performed to confirm the diagnostic result. In addition, RT-PCR reagents are lacking in many severe epidemic areas. By contrast, X-ray and CT(computed tomography) examination equipment have been widely popularized in hospitals. In clinical practice, by combining clinical symptoms and travel history, CT is an efficient and safe method to diagnose COVID-19. Compared with CT, X-ray examination has faster scanning speed and lower radiation amount. Moreover, X-ray and CT images are important tools for doctors to track and observe the condition and evaluate the efficacy. In summary, medical imaging plays a vital role in limiting the spread of viruses and treating COVID-19. During the outbreak of the epidemic, medical imaging-based AI-assisted diagnostic technology has become a popular research direction. Computer-aided diagnostic technology improves the sensitivity and specificity of doctors' diagnosis and is accurate and efficient, which helps rapid diagnosis of a large number of suspicious cases. For example, the out preformed AI-assisted diagnosis system can achieve an accuracy rate comparable to that of radiologists, and it take less than 1 second to perform a diagnosis. The system has been used in 16 hospitals, with more than 1 300 diagnoses performed daily. This article reviews the latest research works on AI-assisted diagnosis of COVID-19 and analyzes and summarizes them on the basis of four aspects: data preparation, image segmentation, diagnosis, and prognosis. First, this article organizes some public data sets to support the AI-assisted diagnostic technology of COVID-19 and provides several solutions to insufficient datasets, such as the human-in-the-loop strategy, which improves the efficiency of data set production. Using transfer learning, weakly supervised or unsupervised learning can reduce model's dependence on the COVID-19 dataset. Second, the semantic segmentation network is also an indispensable part of the intelligent diagnosis of COVID-19. Segmenting the lung region from the original image is a key pre-processing step, which can reduce the calculation amount of subsequent algorithms. The lesion area helps the doctor to track the condition of the disease, and the infection rate can be calculated according to the size of the infected area. U-Net, U-Net++, and attention U-Net are suitable for the segmentation of medical images because of the small number of parameters, which is not easy to overfit. Furthermore, training the semantic segmentation network with the idea of the generative adversarial network (GAN) can improve the Dice coefficient. Third, this article introduces the AI diagnostic system from two aspects of CT images and X-rays. Comparing different diagnostic schemes, the method of diagnosis based on the segmentation images is better than that based on the original images. Among the classification networks, ResNet and VGG19(visual geometry group 19-layer net) perform better. Methods such as GAN, location attention mechanisms, transfer learning, and combining 2D and 3D features can be used to improve accuracy. In addition, clinical information (travel and contact history, white blood cell count, fever, cough, sputum, patient age, and patient gender) c n be used as a basis for diagnosis. For example, algorithm D_FF_Conic uses clinical information as a diagnostic basis and has reached an accuracy rate of 90%. Clinicians will consider medical imaging and clinical information in the process of diagnosis, but the current AI diagnostic system cannot integrate multiple types of data for diagnosis. Although some algorithms can fuse the diagnostic results of medical images with the diagnostic results of clinical information, the simple fine-tuned algorithm haven't learned the deep internal connection between different types of data. Fourth, AI technology can also predict high-risk patients on the basis of infection rates and clinical information. Some research predicted the survival rate of COVID-19 patients on the basis of age, syndrome, and infection rate. Such algorithms can help doctors find and treat high-risk patients early, thereby reducing mortality, which is of great significance. This article shows the latest progress of COVID-19's medical imaging-based AI diagnosis. Although some AI-assisted diagnostic systems have been deployed in hospitals to play a practical role, these algorithms still have some problems, such as insufficient training data, a single diagnostic basis, and the ability to distinguish between non-COVID-19 pneumonia and COVID-19. © 2020, Editorial and Publishing Board of Journal of Image and Graphics. All right reserved.

Editorial – responsible tourism: A call to action for turbulent times

The magnitude in which global crises and the ongoing societal challenges affects our life has called for attentions to be more socially responsible, environmentally friendly and caring for the wider community. A lot has been said recently about tourism restart or recovery as a consequence of COVID-19, highlighting the need for greater pragmatism and responsibility in the midst of turbulence, further emphasising the relevance for responsible tourism practice and scholarship. The present editorial serves as a foundation that provides an overview of the development of responsible tourism, offering a synthesis of key literature and sources regarding responsible tourism that can help guide future investigations. A bibliometric analysis of responsible tourism was conducted in Web of Science (WOS) database and subsequently 94 articles were used in the final review. While the results show the relevance of sustainable practices, behavioural factors and value creation by multiple stakeholders, they underscore the need for more and better efforts to delve into and realise responsible tourism in the contemporary environment. © 2020, Asia Business Research Corporation. All rights reserved.

Epidemiological Investigation of OHCWs with COVID-19.

During the coronavirus disease 2019 (COVID-19) pandemic, there is an important risk of infection in health care workers, including oral health care workers (OHCWs), due to the characteristics of dental practice. In this retrospective study, data pertaining to the 31 OHCWs diagnosed with COVID-19 in Wuhan, Hubei Province, were retrieved and analyzed. Questionnaires were administered to the subjects over the Internet and telephone. Clinical and epidemiological characteristics and information on the use of personal protective equipment (PPE) were collected. There were 22 females and 9 males, with a median age of 39 y. Although the severity of most cases of COVID-19 (93.5%) was mild or moderate, 1 case was severe, and another case was critical, resulting in death. Fever was the main first symptom of COVID-19, followed by fatigue and myalgia. Most of the OHCWs routinely used PPE such as medical masks, gloves, caps, and work clothes while performing clinical work. N95 or KN95 masks were rarely available because of the scarcity of PPE during the outbreak. Nineteen OHCWs reported a contact history, among whom 7 worked in a fever clinic, 5 reported contact with dental patients suspected of having COVID-19, and 7 reported contact with family members with COVID-19-related symptoms at least 1 d earlier. Our findings indicated that there were few clusters of COVID-19 in dental settings. Since the outbreak, the Hospital of Stomatology, Wuhan University, has provided emergency dental treatment, and none of their staff were infected while providing dental service, which indicates that comprehensive measures such as the use of advanced PPE and environmental disinfection can prevent cross-infection in dental practice. The analysis of the procedures followed during the emergency treatments indicated that OHCWs preferred to recommend conservative treatment to patients, suggesting that attention should be paid to the psychological impact of COVID-19 on dental practitioners.