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The Corona Virus Disease 2019 (COVID-19) pandemic is still imposing a devastating impact on public health, the economy, and society. Predicting the development of epidemics and exploring the effects of various mitigation strategies have been a research focus in recent years. However, the spread simulation of COVID-19 in the dynamic social system is relatively unexplored. To address this issue, considering the outbreak of COVID-19 at Nanjing Lukou Airport in 2021, we constructed an artificial society of Nanjing Lukou Airport based on the Artificial societies, Computational experiments, and Parallel execution (ACP) approach. Specifically, the artificial society includes an environmental model, population model, contact networks model, disease spread model, and intervention strategy model. To reveal the dynamic variation of individuals in the airport, we first modeled the movement of passengers and designed an algorithm to generate the moving traces. Then, the mobile contact networks were constructed and aggregated with the static networks of staff and passengers. Finally, the complex dynamical network of contacts between individuals was generated. Based on the artificial society, we conducted large-scale computational experiments to study the spread characteristics of COVID-19 in an airport and to investigate the effects of different intervention strategies. Learned from the reproduction of the outbreak, it is found that the increase in cumulative incidence exhibits a linear growth mode, different from that (an exponential growth mode) in a static network. In terms of mitigation measures, promoting unmanned security checks and boarding in an airport is recommended, as to reduce contact behaviors between individuals and staff. © 2021 TUP.
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Background. The COVID-19 pandemic has been marked by long-term persistence of SARS-CoV-2 in immunocompromised patients receiving B-cell-depleting therapies, with many individuals experiencing fatal COVID-19. Methods. We report an individual treated with rituximab who survived persistent COVID-19 over 9 months. SARS-CoV-2 positive RNA samples were sequenced using targeted amplicon NGS sequencing with backup sequencing on a nanopore platform. The resulting sequences were analyzed for genomic variance over time at the consensus and sub-consensus level. Results. An individual with rheumatoid arthritis (RA) treated with azathioprine and rituximab (last dose in May 2020) was diagnosed with COVID-19 in July 2020 and admitted with pneumonia. After initial incomplete recovery, the patient had persistent infection through March 2021 and received both remdesivir and convalescent plasma (January 2021). The patient received three doses of mRNA vaccine (Pfizer BioNTech) in December 2020, April 2021, and November 2021, but was seronegative for nucleocapsid IgG in both January 2021 and March 2021;positive spike IgG developed by September 2021 (512 AU/ml) and December 2021 (621 AU/ml) (Figure 1). The patient recovered with new oxygen dependence (2-3L) and manages RA off B-cell depletion;they required an extended corticosteroid taper to manage organizing pneumonia and treatment for several opportunistic infections. Viral sequencing over the course of illness indicated a persistent infection with a lineage B.1.585.3 virus that accumulated 14 mutations throughout the infection. Two mutations (S494P, S D737Y) are associated with therapy resistance and are similar to those found in other immunocompromised individuals with persistent COVID-19. Additional mutations were of unknown consequence Conclusion. SARS-CoV-2 was able to establish persistent infection and accumulated mutations associated with therapeutic resistance;repeated vaccination was associated with successful resolution following repeated vaccination after stopping rituximab. Cessation of B-cell-depleting therapy was likely the critical factor in the patient's recovery, but repeated vaccination was associated with a delayed seroconversion in this patient with reversibly immunosuppression.
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Objectives Nursing homes (NH) confronted tremendous difficulties considering confirmed residents Covid-19 cases and deaths in the U.S. The Centers for Medicare & Medicaid Services (CMS) applies the Five-Star Quality Rating (FSQR) to indicate the quality of care in nursing homes based on health inspection surveys, staffing as well as care process and resident outcomes during the COVID-19 pandemic. This study aims to examine whether FSQR was related to the total number of NH resident Covid-19 cases and deaths.Design This study analyzed 6,978 nursing homes across the country with data from CMS Nursing Home Compare, CMS COVID-19 Nursing Home Public File, Long-term Care Focus, Payroll Based Journal, Rural-Urban Commuting Area. Negative binomial regressions were used to investigate associations between FSQR and NH COIVD-19 outcomes controlling for state fixed effects and clustering of nursing homes within counties. The characteristics of facility, residents, payer-mix, nursing staff, and geographic location were also controlled. ResultsComparing to NH with 1-star in Health Inspection, Staffing, or Overall ratings, NH with better performance have lower risk of having increased number of COVID cases and deaths among residents. Further, nursing home Quality Measures rating is not significantly associated with residents' COVID-19 deaths. ConclusionOverall, the FSQR is a useful measure of quality in part when investigating NH's performance during the COVID-19 pandemic. Future policymakers should pay special attention to providers performing poorly in FSQR when improving the quality of nursing homes, particularly regarding infection control.
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The COVID-19 pandemic created a strong urgency for the application of artificial intelligence (AI) in the tourism and hospitality industry. This paper was set to develop a scale of AI needs in health tourism. A total of 556 valid data were collected, and both exploratory and confirmatory factor analyses were employed to analyze the data. Six constructs containing 18 items were identified, and the reliability and validity were examined to reach satisfactory levels. The measurement scale developed may serve as a foundation for future research, and shed light on tourism managers, marketers, AI designers and policymakers.
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Persistent air pollution in the Beijing-Tianjin-Hebei region (BTH) has become an extremely complex challenge due to the combined effects of industrial structure, regional characteristics, weather and climate, and development. Although China's air pollution levels have reduced significantly since the Airborne Pollution Prevention and Control Action Plan and the Blue Sky Protection Campaign were implemented, the BTH remains a sensitive and vulnerable area. Such large decrease in primary pollution was mainly attributed to the substantial reductions in economic activities and associated emissions during the 2019 novel coronavirus (COVID-19) lockdown, i.e., around the Chinese New Year of 2020. Yet two consecutive severely polluting weather processes occurred in the BTH around the Chinese New Year of 2020, which have seeded doubt among the Chinese public and policymakers regarding the current scientific understanding of the mechanisms of haze pollution. The causes of formation and maintenance of pollution processes can differ significantly. The formation and maintenance of heavy pollution weather is caused by various factors, which is a complex process. Thus, it is crucial to distinguish the contribution of emissions and meteorological conditions on polluting weather, as well as distinguish the contribution of various meteorological factors on the formation and maintenance of polluting weather, for conducting effective attribution diagnostic analysis in actually environmental and meteorological impact assessment operation systems, especially in areas that are sensitive or vulnerable to air pollution. Identifying the specific meteorological conditions formed by polluting weather and establishing a comprehensive model for discriminating atmospheric dynamics and thermodynamics can provide a scientific basis for improving numerical models for air pollution potential forecasting in the future. Therefore, in this study, we focused on two consecutive severely polluting weather processes in BTH around the 2020 Chinese New Year (January to February 2020), as an ideal and unique field experiment for the prevention and control of current severe air pollution. We explored the reasons for the formation and maintenance of continuous severely polluting weather in the context of "continuous emissions reduction" and "relatively low social activity levels" from the perspective of the abnormal structure of the high-low atmospheric circulation system. Based on comprehensive diagnostic analyses, we quantified the relative contribution of each key meteorological factor to the continuous severely polluting weather in BTH by using the standardized multiple linear regression method. The results indicated that stable maintenance of low-level coastal high-pressure systems led to higher relative humidity at ground level compared with normal years and blocking systems, which are two key meteorological factors that induced persistent polluting weather in BTH. The abnormally stable blocking situation provided a special circulation background for the occurrence and maintenance of persistent heavy air pollution in BTH. The continuous and stable easterly and southerly water vapor transportation structure provided the BTH with more moisture than normal years, and it was conducive to increased moisture absorption by aerosols, especially under blocking. The "subsidence warming" effect of the high-level blocking high-pressure system facilitated the production of a "warm cover" structure in the middle of the troposphere. The presence of the anomalous warm cover structure in the troposphere facilitated the establishment of stable and high humidity weather, which was conducive to the accumulation of pollutants and continued air pollution. Dynamic systems (blocking systems) and water vapor transportation factors directly explained 46.8% of the meteorological causes of persistent heavy air polluting weather events around the 2020 Chinese New Year in BTH.
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To efficiently fight against the COVID-19 pandemic, a sterilisation module using 265 nm UVC LED packages was developed. In this paper, the performance of the sterilisation module in terms of irradiance uniformity, junction temperature increase and sterilisation efficiency were characterised. The irradiance uniformity fluctuation across the four corners and the centre point in a 130 mm × 130 mm area was below 10%, exhibiting good uniformity. Uniform irradiance was important to achieve consistent sterilisation, which was the primary difference between the UVC LED package developed and commercial UVC LED packages. Key to achieving uniform irradiance was the structure, consisting of a stacked silicon reflector and a secondary optical lens designed by ray tracing simulation. The junction temperature increase of the 265 nm UVC LED package driving at 200 mA was only 28°C, sufficiently low to exhibit better reliability and performance. A 99.99% sterilisation efficiency on E. coli bacteria was achieved within one minute with UV dosage of 2.7 mJ/cm2 at 200 mA driving current. From the results, the novel 265 nm UVC LED package was a time-efficient solution for disinfection purposes. © 2021 The Hong Kong Institution of Engineers.
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Introduction and Objective. From March 2022, China has been in the rapid progressing stage of the Omicron outbreak. However, the mental status of clinical nurses against infection by the Omicron variant of COVID-19 has been not explored. Therefore, a nationwide online investigation with a larger sample size was conducted to explore the mental status of Chinese frontline clinical nurses, and its influencing factors using sound validated and reliable measurements. Materials and method. A cross-sectional study was conducted, and 1,204 clinical nurses fighting the Omicron outbreak were recruited across various provinces of China. Results. The mean age of the nurses was 30.43 (SD=6.59) years. The majority were female. The rates of these nurses with depression, anxiety, stress, and insomnia symptoms were 29.2%, 37.2%, 19.1%, and 48.8%, respectively. The variables of department, insomnia, fear with COVID-19, turnover intention, job burnout, work coping style, and public recognition of nursing, were significant factors influencing the mental status of clinical nurses. Conclusions. The Chinese clinical nurses fighting the Omicron variant of COVID-19 had a similar level of depression and anxiety, a higher level of insomnia, and a lower level of stress, in comparison with nurses globally who experienced the initial period of the pandemic. Targeted investigation and interventions are urgently needed for Chinese frontline clinical nurses with high levels of anxiety, depression and insomnia, who fought against infection by Omicron. Much more professional advocacy is strongly recommended during control of the pandemic and recovery to consolidate the role and influence of nurses. The contribution and visibility of nursing should be recognized not only by the medical professionals, but by the public in general. Copyright © 2022, Institute of Agricultural Medicine. All rights reserved.
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The turn of the decade introduced a new era of global pandemics to the world through the appearance of COVID-19, which is still an active crisis at the time of this paper. As a countermeasure, the phenomena of home office and online education became not only widely available, but also mandatory in many countries. However, the performance, reliability and general usability of such real-time activities may be severely affected by unfavorable network conditions. In both contexts, content sharing is now a common practice, and the success of the related use cases may fundamentally depend on it. In this paper, we present our surveys and subjective studies on the Quality of Experience of content sharing in online education and online meetings. A total of 6 surveys and 5 experiments are detailed, addressing topics of student experience, user interface settings, sharing options of lecturers and employees of the private sector, the perceivable effects of network impairments and the related long-term adaptation, the rubber band effect of slide sharing, the overall perceived quality and the separate quality aspects of media loading times, and the preference between visual quality, average frame rate and frame rale uniformity. The findings of the subjective studies do not characterize the use cases of the investigated topics on a general, widely-applicable level, as only a single online platform is involved throughout the experiments. However, their experimental configurations are reinforced by comprehensive surveys and many results indicate statistically significant differences between the selected lest conditions.
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During the period of COVID-19, the national universities support the policy of "continuing teaching and learning while stopping the class", and fully apply the teaching advantage of "Internet +"to conduct the online teaching activity. "Technical Fundamentals of Web Front-end", as the main course of the major of financial science and technology, plays a significant role in cultivating professionals and improving the core competitiveness of students. During the epidemic period, the online teaching mode based on SPOC and WeChat, which coordinates the pre-class guidance, in-class research, after-class supervision and development, has been applied to complete the online teaching of 2019 and 2020 students. This paper not only shares the online teaching experience of the course, but also summarizes and discusses the advantages and disadvantages of online education, in order to provide reference for improving the quality of online teaching of financial technology courses. © 2022 ACM.
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Since the outbreak of the coronavirus disease 2019 (COVID-19), the issue of how to maintain economic development while containing the epidemic has become a significant concern for decision-makers. Though lockdown measures are verified to be very effective in containing the epidemic, its economic costs and other influences have not been fully explored. As a result, decision-makers in many countries are still hesitant to include the lockdown measure in an intervention strategy in response to COVID-19. To address this issue, we propose a universal computational experiment approach for policy evaluation and adjustment based on the Artificial societies, Computational experiments, Parallel execution (ACP) concept. First, we innovatively construct a model via observable CO<inline-formula> <tex-math notation="LaTeX">$_2$</tex-math> </inline-formula> emissions, which is able to estimate the economic costs affected by nonpharmaceutical interventions. Furthermore, based on the population movement data, a risk source model is proposed to estimate the local transmission risk for any prefectures outside the epicenter. Finally, we integrate the data models in a high-resolution agent-based artificial society and carry out large-scale computational experiments supported by the Tianhe supercomputer. Policy adjustments and evaluations are carried out in four cities: Wenzhou, Guangzhou, Beijing, and Wuhan. Our research findings show important implications for policy-making: 1) the local transmission of a city can be almost contained if lockdowns are adopted immediately when the risk index is larger than 1.645, 1.960, or 2.576 at the 90%, 95%, or 99% confidence interval, respectively;2) if lockdowns are required, in-advance lockdown measures facilitate mitigation efficacy and reduce economic loss;and 3) lockdowns lasting for 7–14 days in a prefecture would be effective in controlling the spread of the epidemic. The duration of the measure should be prolonged with the increment of the initial transmission risk. IEEE
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In this paper, we propose a novel U-Net model based on atrous spatial pyramid pooling for medical image segmentation. In the encoder, a pre-trained ImageNet Efficientnet-B0 network is used to extract features for effective information. Atrous spatial pyramid pooling (ASPP) is used to extract the above multi-scale information between encoding and decoding. Finally, the captured information is cascaded with the information of the encoding layer, and the segmentation accuracy is improved by combining the attention mechanism. Experimental results on public data sets show that the accuracy, recall rate and Dice coefficient of the proposed algorithm are 85.35
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The term corona virus (Latin: Corona, crown) is coined due to presence of spikes glycoproteins on the surface that gives it a crown-like appearance. Coronaviruses came from the family Coronaviridae and the order Nidovirales. Novel Corona Virus Disease outbreak happened in January 2020 subsequently dispersed around the world and reason for death of several million people worldwide. Currently, no effective treatment for severe COVID-19 patients is present. Now days, patients are only treated symptomatically. Scientific community working to develop novel antiviral drugs, vaccines, immunomodulatory medications. In the recent scenario of COVID-19 pandemic, we lack any better therapeutic option for treatment of severe COVID-19 patients. MSCs may be a better option for providing emergency therapy. Vast number of studies and clinical trials are warranted regarding the safety and efficacy stem cell therapy in COVID-19 and other respiratory disorders.
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Background Since the first report of COVID-19 in December 2019, there have been significant concerns regarding the effects of the disease on pregnant and recently pregnant women. Quantifying prevalence, and identifying risk factors for severe COVID-19 in this population is key to planning and providing effective clinical maternal care. Objectives To identify rates of COVID-19 amongst pregnant and recently pregnant women and to identify maternal risk factors for severe COVID-19 and worsening clinical outcomes. Design To address the objectives using the developing evidence base we are using a 'Living systematic review' study design. Methods A systematic search of various databases and sources was conducted, including: Medline, Embase, Cochrane database, WHO COVID-19 database, CNKI, Wanfang databases, preprint servers, social media, reference lists of guidelines and included studies until the 6th of October 2020. Quality assessment of prevalence studies was done using the risk of bias tool by Hoy et al. and comparative cohorts using the Newcastle Ottawa Scale. Data extraction was completed with a pre-piloted form by two independent reviewers. The analysis is undertaken monthly and findings are regularly updated. Results are disseminated through our website: https://www.birmingham.ac.uk/research/who-collabora ting-centre/pregcov/index.aspx. The living systematic review process and collated database has given rise to distinct review questions, and the authors of this focused on prevalence and maternal risk factors. Random effects meta-analysis was used to determine prevalence of COVID-19 and the maternal risk factors associated with severe COVID-19. Results 192 studies were included. Overall, 10% (95% confidence interval 7% to 12%;73 studies, 67 271 women) of pregnant and recently pregnant women attending or admitted to hospital for any reason were diagnosed as having suspected or confirmed COVID-19. Increased maternal age (1.82, 1.27 to 2.63;I2 = 30.1%;7 studies;3561 women), high body mass index (2.37, 1.83 to 3.07;I2 = 0%;6 studies;3380 women), pre-existing maternal comorbidity (1.81, 1.49 to 2.20;I2 = 0%;3 studies;2634 women), chronic hypertension (2.0, 1.14 to 3.48;I2 = 0%;2 studies;858 women), pre-existing diabetes (2.12, 1.62 to 2.78;I2 = 0%;3 studies;3333 women), and pre-eclampsia (4.21, 1.26 to 14.0;I2 = 0%;4 studies;274 women) were associated with severe COVID-19 in pregnancy. Conclusions 1 in 10 pregnant or recently pregnant women attending or admitted to hospital are estimated to have COVID-19. Pre-existing co-morbidities, chronic hypertension, pre-eclampsia, pre-existing diabetes, high maternal age, and high BMI are risk factors for severe COVID-19.
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Introduction: In recent days, trend of taking online lectures is booming in medical education all around the globe. Online lectures show its greatest importance during COVID-19 outbreak and lockdown period, where it is the only possible way to teach the international students from home. However, there are several issues including lack of support from management, ignorance & resistance of other faculty members, changes in syllabus of medical education putting extra pressure on overloaded teaching faculty, low level of satisfaction in students, poor internet connection, poor software qualities, poor language skills in students,lack of communication between teacher and students. Material and method: We ran an online survey, under which we send the set of Questions (given in the Table 1) to 54 volunteer Fourth Year Medical students, who were taking online lectures during COVID-19 Pandemic. Results: In our online survey we found that 92 % students agree with idea of saving energy and time through online lectures. 46 % students face poor internet problems, 20 % students face problems to understand online lectures because of their poor language skills,22% students reported lack of concentration during online classes, 38% students reported technical issues,42% students are satisfied, 42% partially satisfied, 10 % students not satisfied. 94% students believe that these online lectures are not enough to make students more confident and independent about medical knowledge. Additionally, 37% students (maximum number of students) reported that they like to take online lectures one zoom app. Conclusion: Online teaching in medical colleges is still a new concept;however, with improved technology, it can be a fruitful deal for the coming generations. During COVID -19, these online lectures became a crucial candlelight in the dark era of lockdown worldwide. Last but not the least, online lectures are not the substitute of face-to-face classroom lectures. Contrarily, if we solve the connectivity issues, electronic device issues, software problems, language issues etc., then only online lectures can reach up to the level of face-to-face classroom lectures.
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Affected by the Coronavirus disease (COVID-19), the safety of cold chain logistics has received unprecedented attention. Under the situation that major economies are under impact, how the cold chain industry can further develop has attracted widespread attention in the academic community. In order to make up for the imperfect deficiencies in the index system of cold chain logistics influencing factors in the existing literature, while considering that existing studies rarely pay attention to the impact of the epidemic on the cold chain industry. Based on the ISM model, this article compares and analyzes factors such as the lack of construction of a cold chain logistics standardization system. Research shows that product quality and safety and information system construction are factors at level one, so they are the most critical factors affecting the development of cold chain logistics. Finally, this article enriches the research of cold chain logistics related fields, provides suggestions for the development of the industry, and has theoretical and practical guiding significance. © 2020 IEEE.