Wall Street QE vs. Main Street Lending.

Monetary and fiscal authorities reacted swiftly to the COVID-19 pandemic by purchasing assets (or "Wall Street QE") and lending directly to non-financial firms (or "Main Street Lending"). Our paper develops a new framework to compare and contrast these different policies. For the Great Recession, characterized by impaired balance sheets of financial intermediaries, Main Street Lending and Wall Street QE are perfect substitutes and both stimulate aggregate demand. In contrast, for the COVID-19 recession, where non-financial firms faced significant cash flow shortages, Wall Street QE is almost completely ineffective, whereas Main Street Lending can be highly stimulative.

Can multi-source heterogeneous data improve the forecasting performance of tourist arrivals amid COVID-19? Mixed-data sampling approach.

The coronavirus disease (COVID-19) pandemic has already caused enormous damage to the global economy and various industries worldwide, especially the tourism industry. In the post-pandemic era, accurate tourism demand recovery forecasting is a vital requirement for a thriving tourism industry. Therefore, this study mainly focuses on forecasting tourist arrivals from mainland China to Hong Kong. A new direction in tourism demand recovery forecasting employs multi-source heterogeneous data comprising economy-related variables, search query data, and online news data to motivate the tourism destination forecasting system. The experimental results confirm that incorporating multi-source heterogeneous data can substantially strengthen the forecasting accuracy. Specifically, mixed data sampling (MIDAS) models with different data frequencies outperformed the benchmark models.

Strategies and effectiveness of the chinese government debunking covid-19 rumors on sina weibo: Evaluating from emotions

The Chinese government refuted rumors on social media for infodemic management when COVID-19 outbroke. This study selected 80 government accounts on Sina Weibo and collected 501 valid anti-rumor posts with comments from 18 January to 29 February 2020. This paper evaluated the effectiveness of rumor debunking from the public emotions reflected in the comments. This study also examined the influence of different anti-rumor strategies, such as fact-checking, rumor response modes, and presentation forms, on the effectiveness of rumor debunking. The findings revealed that fact-checking, combined response mode and text presentation could improve the effectiveness of rumor debunking to some extent. Further analysis of the public emotions indicated a correlation between the trust in government and the effectiveness of rumor debunking. These findings suggested building a multiparticipant response mechanism with medical institutions and media to mitigate the COVID-19 infodemic through targeted strategies, thus further increasing the government's credibility via information governance. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Corrigendum to "Comprehensive Humoral and Cellular Immune Responses to SARS-CoV-2 Variants in Diverse Chinese Population".

[This corrects the article DOI: 10.34133/2022/9873831.].

Clinical Diagnostic Value of Serum 25-Hydroxyvitamin D in Severe Fever with Thrombocytopenia Syndrome.

Purpose: Severe Fever with Thrombocytopenia Syndrome (SFTS) is an infectious disease with rapid onset and high case fatality rate. The study was to explore the clinical value by examining the serum level of 25-hydroxyvitamin D (25 (OH) D) in SFTS patients. Methods: One hundred and five patients and 156 healthy controls were included. Univariate and multivariate regression analyses were performed to identify independent risk factors for disease progression. Subject operating characteristics (ROC) curves were drawn, and the corresponding area under the curve (AUC) was calculated to assess the sensitivity and specificity of the diagnostic disease. Results: The 25 (OH) D level of disease group was lower than that of healthy control group (22.12 (18.43, 25.86) ng/mL vs 27.36 (23.20, 32.71) ng/mL; P<0.05). The 25 (OH) D level of severe disease group was lower than that of mild disease group (20.55(16.30, 24.44) ng/mL vs 24.94(20.89, 31.91) ng/mL; P<0.05). And there was no significant difference of 25 (OH) D level between the survival group and death group in severe disease group. Multivariate Logistic regression analysis showed that the 25 (OH) D level under 19.665 ng/mL was an independent risk factor for the development of SFTS (OR = 0.901, P=0.040). Furthermore, age more than 68.5 years old and lactate dehydrogenase (LDH) more than 1023.5U/L were independent risk factors for death in severe patients with SFTS. Conclusion: Patients with SFTS have reduced 25 (OH) D level, and 25 (OH) D is a risk factor for disease severity in patients with SFTS. Vitamin D supplementation may be an effective measure to reduce the risk of infection and improve the prognosis.

Influence of medical epidemic control on the sports events development based on machine learning and prediction model (preprint)

In year 2020, a large-scale outbreak of pneumonia caused by new coronavirus has affected the development of many industries and enterprises in China. Under the strong leadership of the Chinese government, the development of the epidemic situation in China has been well controlled. The development of various industries also began to show a good situation, many large-scale sports competitions also need to be restored. In order to ensure the normal development of large-scale sports events, we need to consider the development of epidemic situation to determine the time of sports events. Based on the study of FPGA theory, this paper designs a specific scheme of programming and system debugging, which includes a variety of program operations. In order to better predict the situation of the epidemic situation, this paper also uses the basic knowledge of machine learning to establish a relevant model to evaluate the situation of large-scale sports events under the development of the epidemic situation, and provide feasible suggestions for the recovery of large-scale sports events under the epidemic situation.

Caregiver Perceptions of Children's and Adolescents' Psychosocial Functioning During the Stringent COVID-19 Lockdown Restrictions in Shanghai: Cross-sectional Study.

BACKGROUND: The COVID-19 pandemic represents a global health crisis. The Shanghai municipal government in China implemented strict and comprehensive pandemic control strategies in the first half of 2022 to eliminate a wave of COVID-19 infection. The pandemic and the resulting government responses have led to abrupt changes to families' daily lives, including the mental health of children and adolescents. OBJECTIVE: The aim of this paper is to examine the impact of COVID-19 exposure and the stringent lockdown measures on the daily life and mental health of children and adolescents and to provide suggestions on maintaining their mental health when similar public health emergencies occur in the future. METHODS: In this cross-sectional study, an anonymous survey was distributed online in May 1-15, 2022, in Shanghai. Individuals were eligible to participate if they were currently the caregiver of a child or adolescent (aged 4-17 years). Outcomes were psychosocial functioning of children and adolescents, as reported by parents, using the Pediatric Symptom Checklist-17. COVID-19 exposure and life changes were also reported. Multivariate logistic regression was used to analyze risk factors for poor psychosocial functioning. RESULTS: In total, 2493 valid questionnaires were analyzed. The rate of positive scores on the global Pediatric Symptom Checklist-17 scale was 16.5% (n=411). Internalizing, attention, and externalizing problem subscale positivity rates were 17.3% (n=431), 10.9% (n=272), and 8.9% (n=221), respectively. Caregivers reported that 64.2% (n=1601) and 20.7% (n=516) of the children's interactions with friends or peers and parents deteriorated, respectively. Compared with male caregivers, female caregivers were less likely to report psychosocial problems in children and adolescents (adjusted odds ratio [aOR] 0.68; 95% CI 0.53-0.88). Older children and those with lower COVID-19 Exposure and Family Impact Scales scores were less likely to have psychological problems (aOR 1.15; 95% CI 1.10-1.21). Compared with children with screen times <1 hour per day for recreation, those using screens for >3 hours had higher odds of psychological distress (aOR 2.09; 95% CI 1.47-1.97). Children who spent 1-2 hours exercising and had better interactions with friends or peers and parents showed a trend toward lower odds of psychological problems. Children and adolescents with worse sleep compared with preclosure were more likely to have psychological problems. CONCLUSIONS: The prevalence of psychosocial problems among children and adolescents is relatively high. Being young, having more COVID-19 exposure, and having more screen times (>3 h/day), less exercise time (<30 min), worse sleep, and deteriorated interactions with friends or peers and parents were risk factors for poor psychosocial functioning. It is necessary for governments, communities, schools, and families to take appropriate countermeasures to reduce the negative impact of the stringent control measures on caregivers' parenting and psychosocial functioning of children and adolescents.

Trends in respiratory diseases before and after the COVID-19 pandemic in China from 2010 to 2021.

BACKGROUND: The ongoing benefits of coronavirus disease 2019 (COVID-19) nonpharmaceutical interventions (NPIs) for respiratory infectious diseases in China are still unclear. We aimed to explore the changes in seven respiratory infectious diseases before, during, and after COVID-19 in China from 2010 to 2021. METHODS: The monthly case numbers of seven respiratory infectious diseases were extracted to construct autoregressive integrated moving average (ARIMA) models. Eight indicators of NPIs were chosen from the COVID-19 Government Response Tracker system. The monthly case numbers of the respiratory diseases and the eight indicators were used to establish the Multivariable generalized linear model (GLM) to calculate the incidence rate ratios (IRRs). RESULTS: Compared with the year 2019, the percentage changes in 2020 and 2021 were all below 100% ranging from 3.81 to 84.71%. Pertussis and Scarlet fever started to increase in 2021 compared with 2020, with a percentage change of 183.46 and 171.49%. The ARIMA model showed a good fit, and the predicted data fitted well with the actual data from 2010 to 2019, but the predicted data was bigger than the actual number in 2020 and 2021. All eight indicators could negatively affect the incidence of respiratory diseases. The seven respiratory diseases were significantly reduced during the COVID-19 pandemic in 2020 and 2021 compared with 2019, with significant estimated IRRs ranging from 0.06 to 0.85. In the GLM using data for the year 2020 and 2021, the IRRs were not significant after adjusting for the eight indicators in multivariate analysis. CONCLUSION: Our study demonstrated the incidence of the seven respiratory diseases decreased rapidly during the COVID-19 pandemic in 2020 and 2021. At the end of 2021, we did see a rising trend for the seven respiratory diseases compared to the year 2020 when the NPIs relaxed in China, but the rising trend was not significant after adjusting for the NPIs indicators. Our study showed that NPIs have an effect on respiratory diseases, but Relaxation of NPIs might lead to the resurgence of respiratory diseases.

Latent profile analysis of security among patients with COVID-19 infection in mobile cabin hospitals and its relationship with psychological capital.

Aim: COVID-19 patients' security is related to their mental health. However, the classification of this group's sense of security is still unclear. The aim of our research is to clarify the subtypes of security of patients infected with COVID-19, explore the factors affecting profile membership, and examine the relationship between security and psychological capital for the purpose of providing a reference for improving patients' sense of security and mental health. Methods: A total of 650 COVID-19 patients in a mobile cabin hospital were selected for a cross-sectional survey from April to May 2022. They completed online self-report questionnaires that included a demographic questionnaire, security scale, and psychological capital scale. Data analysis included latent profile analysis, variance analysis, the Chi-square test, multiple comparisons, multivariate logistical regression, and hierarchical regression analysis. Results: Three latent profiles were identified-low security (Class 1), moderate security (Class 2), and high security (Class 3)-accounting for 12.00, 49.51, and 38.49% of the total surveyed patients, respectively. In terms of the score of security and its two dimensions, Class 3 was higher than Class 2, and Class 2 was higher than Class 1 (all P < 0.001). Patients with difficulty falling asleep, sleep quality as usual, and lower tenacity were more likely to be grouped into Class 1 rather than Class 3; Patients from families with a per capita monthly household income <3,000 and lower self-efficacy and hope were more likely to be grouped into Classes 1 and 2 than into Class 3. Psychological capital was an important predictor of security, which could independently explain 18.70% of the variation in the patients' security. Conclusions: Security has different classification features among patients with COVID-19 infection in mobile cabin hospitals. The security of over half of the patients surveyed is at the lower or middle level, and psychological capital is an important predictor of the patients' security. Medical staff should actively pay attention to patients with low security and help them to improve their security level and psychological capital.

SARS-CoV-2 intra-host single-nucleotide variants associated with disease severity.

Variants of severe acute respiratory syndrome coronavirus 2 frequently arise within infected individuals. Here, we explored the level and pattern of intra-host viral diversity in association with disease severity. Then, we analyzed information underlying these nucleotide changes to infer the impetus including mutational signatures and immune selection from neutralizing antibody or T-cell recognition. From 23 January to 31 March 2020, a set of cross-sectional samples were collected from individuals with homogeneous founder virus regardless of disease severity. Intra-host single-nucleotide variants (iSNVs) were enumerated using deep sequencing. Human leukocyte antigen (HLA) alleles were genotyped by Sanger sequencing. Medical records were collected and reviewed by attending physicians. A total of 836 iSNVs (3-106 per sample) were identified and distributed in a highly individualized pattern. The number of iSNVs paced with infection duration peaked within days and declined thereafter. These iSNVs did not stochastically arise due to a strong bias toward C > U/G > A and U > C/A > G substitutions in reciprocal proportion with escalating disease severity. Eight nonsynonymous iSNVs in the receptor-binding domain could escape from neutralization, and eighteen iSNVs were significantly associated with specific HLA alleles. The level and pattern of iSNVs reflect the in vivo viral-host interaction and the disease pathogenesis.

Latent profile analysis of security among patients with COVID-19 infection in mobile cabin hospitals and its relationship with psychological capital

Aim COVID-19 patients' security is related to their mental health. However, the classification of this group's sense of security is still unclear. The aim of our research is to clarify the subtypes of security of patients infected with COVID-19, explore the factors affecting profile membership, and examine the relationship between security and psychological capital for the purpose of providing a reference for improving patients' sense of security and mental health. Methods A total of 650 COVID-19 patients in a mobile cabin hospital were selected for a cross-sectional survey from April to May 2022. They completed online self-report questionnaires that included a demographic questionnaire, security scale, and psychological capital scale. Data analysis included latent profile analysis, variance analysis, the Chi-square test, multiple comparisons, multivariate logistical regression, and hierarchical regression analysis. Results Three latent profiles were identified—low security (Class 1), moderate security (Class 2), and high security (Class 3)—accounting for 12.00, 49.51, and 38.49% of the total surveyed patients, respectively. In terms of the score of security and its two dimensions, Class 3 was higher than Class 2, and Class 2 was higher than Class 1 (all P < 0.001). Patients with difficulty falling asleep, sleep quality as usual, and lower tenacity were more likely to be grouped into Class 1 rather than Class 3;Patients from families with a per capita monthly household income <3,000 and lower self-efficacy and hope were more likely to be grouped into Classes 1 and 2 than into Class 3. Psychological capital was an important predictor of security, which could independently explain 18.70% of the variation in the patients' security. Conclusions Security has different classification features among patients with COVID-19 infection in mobile cabin hospitals. The security of over half of the patients surveyed is at the lower or middle level, and psychological capital is an important predictor of the patients' security. Medical staff should actively pay attention to patients with low security and help them to improve their security level and psychological capital.

Topic modeling and sentiment analysis of Chinese people's attitudes toward volunteerism amid the COVID-19 pandemic.

The COVID-19 pandemic has created an urgent need for volunteers to complement overwhelmed public health systems. This study aims to explore Chinese people's attitudes toward volunteerism amid the COVID-19 pandemic. To this end, we identify the latent topics in volunteerism-related microblogs on Weibo, the Chinese equivalent of Twitter using the topic modeling analysis via Latent Dirichlet Allocation (LDA). To further investigate the public sentiment toward the topics generated by LDA, we also conducted sentiment analysis on the sample posts using the open-source natural language processing (NLP) technique from Baidu. Through an in-depth analysis of 91,933 Weibo posts, this study captures 10 topics that are, in turn, distributed into five factors associated with volunteerism in China as motive fulfillment (n = 31,661, 34.44%), fear of COVID-19 (n = 22,597, 24.58%), individual characteristic (n = 17,688, 19.24%), government support (n = 15,482, 16.84%), and community effect (n = 4,505, 4.90%). The results show that motive fulfillment, government support, and community effect are the factors that could enhance positive attitudes toward volunteerism since the topics related to these factors report high proportions of positive emotion. Fear of COVID-19 and individual characteristic are the factors inducing negative sentiment toward volunteerism as the topics related to these factors show relatively high proportions of negative emotion. The provision of tailored strategies based on the factors could potentially enhance Chinese people's willingness to participate in volunteer activities during the COVID-19 pandemic.

US-Net: A lightweight network for simultaneous speckle suppression and texture enhancement in ultrasound images.

BACKGROUND: Numerous traditional filtering approaches and deep learning-based methods have been proposed to improve the quality of ultrasound (US) image data. However, their results tend to suffer from over-smoothing and loss of texture and fine details. Moreover, they perform poorly on images with different degradation levels and mainly focus on speckle reduction, even though texture and fine detail enhancement are of crucial importance in clinical diagnosis. METHODS: We propose an end-to-end framework termed US-Net for simultaneous speckle suppression and texture enhancement in US images. The architecture of US-Net is inspired by U-Net, whereby a feature refinement attention block (FRAB) is introduced to enable an effective learning of multi-level and multi-contextual representative features. Specifically, FRAB aims to emphasize high-frequency image information, which helps boost the restoration and preservation of fine-grained and textural details. Furthermore, our proposed US-Net is trained essentially with real US image data, whereby real US images embedded with simulated multi-level speckle noise are used as an auxiliary training set. RESULTS: Extensive quantitative and qualitative experiments indicate that although trained with only one US image data type, our proposed US-Net is capable of restoring images acquired from different body parts and scanning settings with different degradation levels, while exhibiting favorable performance against state-of-the-art image enhancement approaches. Furthermore, utilizing our proposed US-Net as a pre-processing stage for COVID-19 diagnosis results in a gain of 3.6% in diagnostic accuracy. CONCLUSIONS: The proposed framework can help improve the accuracy of ultrasound diagnosis.

Social responsibility and subjective well-being of volunteers for COVID-19: The mediating role of job involvement.

Aim: Our study aimed to investigate the effect of social responsibility on the subjective well-being of volunteers for COVID-19 and to examine the mediating role of job involvement in this relationship. Background: Nowadays, more and more people join volunteer service activities. As we all know, volunteer work contributes to society without any return. Volunteers often have a strong sense of social responsibility and reap subjective well-being in their dedication. Although research shows that social responsibility will drive them to participate in volunteer work actively, it is less clear whether job involvement will impact their subjective well-being. Methods: The data were collected in the precaution zone in Shanghai, China, from April to May 2022. A sample of 302 volunteers for COVID-19 completed the social responsibility scale, subjective well-being scale and job involvement scale in the form of an electronic questionnaire on their mobile phones. A structural equation model was adopted to verify the research hypotheses. Results: Social responsibility was significantly and positively related to volunteers' subjective well-being and job involvement (p < 0.05). Job involvement fully mediates the relationship between volunteers' social responsibility and subjective well-being. Conclusion: Social responsibility is critical to predicting volunteers' subjective well-being. Job involvement plays an intervening mechanism in explaining how social responsibility promotes volunteers' subjective well-being.

[Changes in the disease spectrum in the pediatric intensive care units within 2 years before and after the outbreak of coronavirus disease 2019]. / æ–°åž‹å† çŠ¶ç— æ¯’è‚ºç‚Žç–«æƒ å‰åŽ2å¹´å„¿ç«¥é‡ç—‡ç›‘æŠ¤ç— æˆ¿ç–¾ç— è°±çš„å˜åŒ–.

OBJECTIVES: To investigate the changes in the disease spectrum among hospitalized children in the pediatric intensive care units (PICU) within 2 years before and after the outbreak of coronavirus disease 2019 (COVID-19). METHODS: The related data on disease diagnosis were collected from all children who were hospitalized in the PICU of Affiliated Hospital of Jining Medical College from January 2018 to December 2019 (pre-COVID-19 group) and from January 2020 to December 2021 (post-COVID-19 group). A statistical analysis was performed for the disease spectrum of the two groups. RESULTS: There were 2 368 children in the pre-COVID-19 group and 1 653 children in the post-COVID-19 group. The number of children in the post-COVID-19 group was reduced by 30.19% compared with that in the pre-COVID-19 group. There was a significant difference in age composition between the two groups (P<0.05). The top 10 diseases in the pre-COVID-19 group by number of cases were respiratory diseases, neurological diseases, sepsis, critical illness, circulatory system diseases, severe neurosurgical diseases, digestive system diseases, unintentional injuries, endocrine system diseases, and tumors. The top 10 diseases in the post-COVID-19 group by number of cases were respiratory diseases, neurological diseases, sepsis, circulatory system diseases, unintentional injuries, endocrine system diseases, severe neurosurgical diseases, acute abdomen, trauma surgical diseases, and digestive system diseases. The proportions of respiratory diseases, critical illness and severe neurosurgical diseases in the post-COVID-19 group were lower than those in the pre-COVID-19 group (P<0.05), while the proportions of unintentional injuries, acute abdomen, endocrine system diseases, trauma surgical diseases and sepsis were higher than those in the pre-COVID-19 group (P<0.05). CONCLUSIONS: COVID-19 epidemic has led to a significant reduction in the number of children admitted to the PICU, and there are significant changes in the disease spectrum within 2 years before and after the outbreak of COVID-19. Relevant prevention and control measures taken during the COVID-19 epidemic can reduce the incidence of respiratory diseases, neurological diseases, and other critical illness in children, but it is necessary to strengthen the prevention of unintentional injuries and chronic disease management during the epidemic.