The 95% control lines on both confirmed cases and days of infection with COVID-19 were applied to compare the impact on public health between 2020 and 2021 using the hT-index

Background: COVID-19, the disease caused by the novel coronavirus, is now a worldwide pandemic. The number of infected people has continually increased, and currently, this pandemic continues to present challenges to public health. Scatter plots are frequently used to interpret the impact in relation to confirmed cases. However, the 95% confidence intervals are rarely given to the scatter plot. The objective of this study was to;Develop 95% control lines on daily confirmed cases and infected days for countries/regions in COVID-19 (DCCIDC) and;Examine their impacts on public health (IPH) using the hT-index. Methods: All relevant COVID-19 data were downloaded from GitHub. The hT-index, taking all DCCIDCs into account, was applied to measure the IPHs for counties/regions. The 95% control lines were proposed to highlight the outliers of entities in COVID-19. The hT-based IPHs were compared among counties/regions between 2020 and 2021 using the choropleth map and the forest plot. The features of the hT-index were explained using the line chart and the box plot. Results: The top 2 countries measured by hT-based IPHs were India and Brazil in 2020 and 2021. The outliers beyond the 95% confidence intervals were Hubei (China), with a lower hT-index favoring 2021 ( = 6.4 in 2021 vs 15.55 in 2020) and higher hT indices favoring 2021 in Thailand (28.34 vs 14,77) and Vietnam (27.05 vs 10.88). Only 3 continents of Africa, Asia, and Europe had statistically and significantly fewer DCCIDCs (denoted by the hT-index) in 2021. The hT-index generalizes the h-index and overcomes the disadvantage without taking all elements (e.g., DCCIDCs) into account in features. Conclusions: The scatter plot combined with the 95% control lines was applied to compare the IPHs hit by COVID-19 and suggested for use with the hT-index in future studies, not limited to the field of public health as we did in this research.

Share and embrace demographic and location diversity: Creating an Instagram-based inclusive online learning community

It is critical to create an inclusive online learning environment for students with diverse demographic information studying in different environments, especially during the COVID-19 pandemic when they are disconnected from peers. Guided to create an inclusive online learning community by situated learning theory and community of practice, both of which advocate learning in context and community, we invited 115 undergraduate students to post photos related to environmental psychology concepts and their surrounding environments and discussed their postings on Instagram over eight weeks. To understand the inclusiveness of the community and students' perception, we collected their posts by searching designated hashtags and interviewed representatives of participants using a stratified sampling strategy. Through network analysis of 272 posts and qualitative analysis of 22 in-depth interviews, we found that when participants shared and discussed their surroundings and environmental psychology concepts on Instagram, their learning community was inclusive regarding gender, ethnicity, and program. Student participants' centrality and influence were more relevant to whether and how they expressed their identities in the community through posts. We further discuss how our findings could inform to create inclusive and active communities in the future. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

The use of the time-to-event index (Tevent) to compare the negative impact of COVID-19 on public health among continents/regions in 2020 and 2021: An observational study.

BACKGROUND: During the COVID-19 pandemic, how to measure the negative impact caused by COVID-19 on public health (ImpactCOV) is an important issue. However, few studies have applied the bibliometric index, taking both infected days (quantity) and impact (damage) into account for evaluating ImpactCOV thus far. This study aims to verify the proposed the time-to-event index (Tevent) that is viable and applicable in comparison with 11 other indicators, apply the Tevent to compare the ImpactCOVs among groups in continents/countries in 2020 and 2021, and develop an online algorithm to compute the Tevent-index and draw the survival analysis. METHODS: We downloaded COVID-19 outbreak data of daily confirmed cases (DCCs) for all countries/regions. The Tevent-index was computed for each country and region. The impactCOVs among continents/countries were compared using the Tevemt indices for groups in 2020 and 2021. Three visualizations (i.e., choropleth maps, forest plot, and time-to-event, a.k.a. survival analysis) were performed. Online algorithms of Tevent as a composite score to denote the ImpactCOV and comparisons of Tevents for groups on Google Maps were programmed. RESULTS: We observed that the top 3 countries affected by COVID-19 in 2020 and 2021 were (India, Brazil, Russia) and (Brazil, India, and the UK), respectively; statistically significant differences in ImpactCOV were found among continents; and an online time-event analysis showed Hubei Province (China) with a Tevent of 100.88 and 6.93, respectively, in 2020 and 2021. CONCLUSION: The Tevent-index is viable and applicable to evaluate ImpactCOV. The time-to-event analysis as a branch of statistics for analyzing the expected duration of time until 1 event occurs is recommended to compare the difference in Tevent between groups in future research, not merely limited to ImpactCOV.

Clinical characteristics of children with omicron SARS-CoV-2 infection in Changchun, China from march to april 2022: A retrospective study.

Background: Recently, there was an outbreak in China of the Omicron (B.1.1.529) variant, the corresponding clinical characteristics of Chinese children with the Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection were then reviewed and summarized retrospectively. Methods: From March to April 2022, a total of 134 children infected with the Omicron variant were included in the study. Data such as sex, age, clinical symptoms, laboratory examinations, and imaging features were collected for further analyses. Results: Half of the children were male and the median age was 5.67 years. The most SARS-CoV-2 Omicron variant was identified in mild (122, 91%), and the most three frequent symptoms were as cough (108, 80.6%), fever (75, 56%), and sore throat (38, 28.4%). Among age groups, no significant difference was observed in the distribution of symptoms, and no statistical difference was found in different clinical types among sex or age groups. Laboratory examinations revealed that white blood cells, neutrophils, and hemoglobin decreased; and monocytes, C-reactive protein (CRP), and aspartate aminotransferase (AST) increased. Further analyses showed that neutrophils, hemoglobin, CRP, and AST exhibited significant differences among age groups. Radiological abnormalities were found in nine cases, with small patchy high-density shadows. Of the 76 cured cases discharged from the hospital, the median hospital stay was 13 days (mean, 12 days). Conclusions: In China, most children with Omicron SARS-CoV-2 infection have mild presentation. The findings of this study may help other districts improve the management of children with Omicron SARS-CoV-2 infection in China.

Psychological research of the children with chronic kidney disease and their guardians during the COVID-19 pandemic.

Background: There is great mental stress due to the coronavirus disease 2019 (COVID-19) pandemic. However, there are no detailed psychological studies of the children with chronic kidney disease (CKD) and their guardians during the COVID-19 pandemic. Objective: This study explores the psychological pressure on children with CKD and their guardians. Methods: An online survey was conducted at 20 of the largest pediatric nephropathy departments in China, including the Rutter Parent Questionnaire, Self-rating Anxiety Scale (SAS), and Self-rating Depression Scale (SDS). Overall, 885 children (589 children with CKD associated with 296 children of the control group) completed the survey together with their guardians. Results: There was no statistical difference between CKD children and control children regarding their Rutter behavior scores and abnormal behaviors. Nevertheless, the abnormal behavior of children might aggravate the anxiety and depression of guardians in both CKD and control groups (p < 0.05). We confirmed that the anxiety and depression of guardians in the CKD group were both significantly higher than those in the control group (p < 0.05). The guardians in the CKD group with lower annual income were more likely to experience anxiety (p < 0.05). Furthermore, the guardians whose children were older than 11 years old might be more anxious than those who were 6-11 years old. Besides, the guardians in the CKD group who watched the news for 30-60 min daily were less likely to have depression than those who watched < 10 min (p < 0.05). The subgroup results showed that the gender, the time of watching the news, the annual income of guardians, and children's age might be the most critical factors influencing guardians' psychological burden. Conclusion: The guardians in the CKD group have more severe anxiety and depression during the pandemic. The children's abnormal behavior, adolescents' pressure, low household income, and the panic about the pandemic may be the main reasons for the anxiety and depression of guardians.

Public Health Risk Assessment and Prevention Based on Big Data.

In order to improve the ability of public health risk assessment in the context of community collaborative prevention and control, a mathematical model of public health risk assessment in the context of community collaborative prevention and control based on the integration and balanced allocation of big data features in the prevention horizon is proposed. The constraint parameter model of public health risk assessment under the background of community collaborative prevention and control is constructed, the method of dynamic feature analysis of joint prevention and control is adopted to realize the dynamic risk point detection of public health risk assessment data and the integration of constraint mechanism related feature points, and the fuzzy dynamic statistical feature matching method is adopted to carry out the adaptive dynamic statistics and resource balanced allocation analysis of public health risk assessment set under the background of community collaborative prevention and control. A public health risk parameter fusion model is established under the background of community collaborative prevention and control, the methods of balanced resource allocation and joint management and control are combined to realize balanced scheduling and prevention area block matching in the process of dynamic parameter estimation of public health risk evaluation data under the background of community collaborative prevention and control, the correlation distribution of public health risk under the background of community collaborative prevention and control is taken as the cost function, and balanced allocation is realized according to the statistical information sampling results of public health risk evaluation data under the background of community collaborative prevention and control. Combined with differential clustering analysis, the data clustering and attribute merging of public health risk assessment under the background of community collaborative prevention and control are realized, and the mathematical modeling optimization of public health risk assessment under the background of community collaborative prevention and control is realized. The simulation results show that this method has good adaptability, high degree of parameter fusion, and strong ability of matching risk prevention areas and balancing resource allocation in the context of community collaborative prevention and control.

Share and embrace demographic and location diversity: Creating an Instagram‐based inclusive online learning community

It is critical to create an inclusive online learning environment for students with diverse demographic information studying in different environments, especially during the COVID‐19 pandemic when they are disconnected from peers. Guided to create an inclusive online learning community by situated learning theory and community of practice, both of which advocate learning in context and community, we invited 115 undergraduate students to post photos related to environmental psychology concepts and their surrounding environments and discussed their postings on Instagram over eight weeks. To understand the inclusiveness of the community and students' perception, we collected their posts by searching designated hashtags and interviewed representatives of participants using a stratified sampling strategy. Through network analysis of 272 posts and qualitative analysis of 22 in‐depth interviews, we found that when participants shared and discussed their surroundings and environmental psychology concepts on Instagram, their learning community was inclusive regarding gender, ethnicity, and program. Student participants' centrality and influence were more relevant to whether and how they expressed their identities in the community through posts. We further discuss how our findings could inform to create inclusive and active communities in the future. Practitioner notes What is already known about this topic? The definition of inclusive education extends to diversity and accessibility. Social media can support online learning communities. What this paper adds? It explores the inclusiveness of an Instagram‐based learning community using network analysis. It suggests expressing identities in a learning community helps promote inclusiveness. Implications of this study for practice and/or policy It provides information to education practitioners that will help them create inclusive and active communities through social media. It explores the possibility of analysing the inclusiveness of a learning community through social network analysis. What is already known about this topic? The definition of inclusive education extends to diversity and accessibility. Social media can support online learning communities. What this paper adds? It explores the inclusiveness of an Instagram‐based learning community using network analysis. It suggests expressing identities in a learning community helps promote inclusiveness. Implications of this study for practice and/or policy It provides information to education practitioners that will help them create inclusive and active communities through social media. It explores the possibility of analysing the inclusiveness of a learning community through social network analysis. [ FROM AUTHOR] Copyright of British Journal of Educational Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Comparison of prediction accuracies between two mathematical models for the assessment of COVID-19 damage at the early stage and throughout 2020.

BACKGROUND: The negative impacts of COVID-19 (ImpactCOVID) on public health are commonly assessed using the cumulative numbers of confirmed cases (CNCCs). However, whether different mathematical models yield disparate results based on varying time frames remains unclear. This study aimed to compare the differences in prediction accuracy between 2 proposed COVID-19 models, develop an angle index that can be objectively used to evaluate ImpactCOVID, compare the differences in angle indexes across countries/regions worldwide, and examine the difference in determining the inflection point (IP) on the CNCCs between the 2 models. METHODS: Data were downloaded from the GitHub website. Two mathematical models were examined in 2 time-frame scenarios during the COVID-19 pandemic (the early 20-day stage and the entire year of 2020). Angle index was determined by the ratio (=CNCCs at IP÷IP days). The R2 model and mean absolute percentage error (MAPE) were used to evaluate the model's prediction accuracy in the 2 time-frame scenarios. Comparisons were made using 3 visualizations: line-chart plots, choropleth maps, and forest plots. RESULTS: Exponential growth (EXPO) and item response theory (IRT) models had identical prediction power at the earlier outbreak stage. The IRT model had a higher model R2 and smaller MAPE than the EXPO model in 2020. Hubei Province in China had the highest angle index at the early stage, and India, California (US), and the United Kingdom had the highest angle indexes in 2020. The IRT model was superior to the EXPO model in determining the IP on an Ogive curve. CONCLUSION: Both proposed models can be used to measure ImpactCOVID. However, the IRT model (superior to EXPO in the long-term and Ogive-type data) is recommended for epidemiologists and policymakers to measure ImpactCOVID in the future.

Study workplace space occupancy: a review of measures and technologies

Purpose>The purpose of this paper is to review the use of technologies for measuring space occupancy to guide the selection of appropriate tools for workplace post-occupancy evaluation (POE) studies. The authors focus on how actual space occupancy was measured in previous studies and the pros and cons of the different technologies and tools. This paper also addresses research gaps and directions for future research.Design/methodology/approach>The space occupancy measures/tools are categorized based on the three types of technologies: environmental/ambient sensors, wearable sensors/smartphones and computer vision. A total of 50 studies are reviewed to identify the capabilities and limitations of these measurements.Findings>Based on review results, the authors propose that although sensor technology can be a useful addition to the measures/tools list, a comprehensive review of the research goal, the occupants' behavior, and the environmental settings' characteristics should be conducted beforehand. Selecting appropriate technology is critical for collecting the proper behavioral data type, with a lower level of surveillance and increased validity.Originality/value>This paper urges critical thinking about existing occupancy measures/tools across various fields, to inform the adoption and creation of new building occupancy measures. The knowledge of emerging sensor technology allows researchers to better study the temporal patterns of occupant behavior over extended periods and in a wide range of settings.

Traditional Chinese medicine network pharmacology study on exploring the mechanism of Xuebijing Injection in the treatment of coronavirus disease 2019.

As a representative drug for the treatment of severe community-acquired pneumonia and sepsis, Xuebijing (XBJ) injection is also one of the recommended drugs for the prevention and treatment of coronavirus disease 2019 (COVID-19), but its treatment mechanism for COVID-19 is still unclear. Therefore, this study aims to explore the potential mechanism of XBJ injection in the treatment of COVID-19 employing network pharmacology and molecular docking methods. The corresponding target genes of 45 main active ingredients in XBJ injection and COVID-19 were obtained by using multiple database retrieval and literature mining. 102 overlapping targets of them were screened as the core targets for analysis. Then built the PPI network, TCM-compound-target-disease, and disease-target-pathway networks with the help of Cytoscape 3.6.1 software. After that, utilized DAVID to perform gene ontology (GO) function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to predict the action mechanism of overlapping targets. Finally, by applying molecular docking technology, all compounds were docked with COVID-19 3 CL protease(3CLpro), spike protein (S protein), and angiotensin-converting enzyme II (ACE2). The results indicated that quercetin, luteolin, apigenin and other compounds in XBJ injection could affect TNF, MAPK1, IL6 and other overlapping targets. Meanwhile, anhydrosafflor yellow B (AHSYB), salvianolic acid B (SAB), and rutin could combine with COVID-19 crucial proteins, and then played the role of anti-inflammatory, antiviral and immune response to treat COVID-19. This study revealed the multiple active components, multiple targets, and multiple pathways of XBJ injection in the treatment of COVID-19, which provided a new perspective for the study of the mechanism of traditional Chinese medicine (TCM) in the treatment of COVID-19.

Network Pharmacology Approach to Investigate the Preventive Mechanism of Hunan Expert Group Recommended Chinese Medicine Prevention No. 2 Prescription Against COVID-19

OBJECTIVE: To explore the possible preventive mechanism of Hunan expert group recommended Chinese medicine prescription of No. 2 (Pre-No. 2) against coronavirus disease 2019 (COVID-19) by network pharmacology method. METHODS: The target proteins of effective components and active compounds in Pre-No. 2 were screened by searching the Tradi-tional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). A component-target-disease interac-tion network of Pre-No. 2 was constructed by Cytoscape 3.7.2, gene ontology (GO) analysis, and Kyoto encyclopedia of genes and genomes (KEGG) analysis of target protein pathway by DAVID. RESULTS: A total of 163 compounds and 278 target protein targets in Pre-No. 2 were collected from the TCMSP database. Kaempferol, wogonin, 7-methoxy-2-methyl isoflavone, formononetin, isorhamnetin, and licochalcone A were the most frequent targets in the regulatory network. GO enrichment analysis showed that Pre-No. 2 regulated response to virus, viral processes, humoral immune responses, defense responses to virus and viral entry into host cells. KEGG enrichment analysis showed that the formula regulated the NF-κB signaling pathway, B cell receptor signaling pathway, viral carcinogenesis, T cell signaling pathway and FcγR-mediated phagocytosis signaling pathway. CONCLUSIONS: Pre-No. 2 may play a preventive role against COVID-19 through regulation of the Toll-like signaling, T cell signaling, B cell signaling and other signaling pathways. It may re-gulate the immune system to protect against anti-influenza virus.

Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-Cov-2) outside of Wuhan, China: retrospective case series.

OBJECTIVE: To study the clinical characteristics of patients in Zhejiang province, China, infected with the 2019 severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) responsible for coronavirus disease 2019 (covid-2019). DESIGN: Retrospective case series. SETTING: Seven hospitals in Zhejiang province, China. PARTICIPANTS: 62 patients admitted to hospital with laboratory confirmed SARS-Cov-2 infection. Data were collected from 10 January 2020 to 26 January 2020. MAIN OUTCOME MEASURES: Clinical data, collected using a standardised case report form, such as temperature, history of exposure, incubation period. If information was not clear, the working group in Hangzhou contacted the doctor responsible for treating the patient for clarification. RESULTS: Of the 62 patients studied (median age 41 years), only one was admitted to an intensive care unit, and no patients died during the study. According to research, none of the infected patients in Zhejiang province were ever exposed to the Huanan seafood market, the original source of the virus; all studied cases were infected by human to human transmission. The most common symptoms at onset of illness were fever in 48 (77%) patients, cough in 50 (81%), expectoration in 35 (56%), headache in 21 (34%), myalgia or fatigue in 32 (52%), diarrhoea in 3 (8%), and haemoptysis in 2 (3%). Only two patients (3%) developed shortness of breath on admission. The median time from exposure to onset of illness was 4 days (interquartile range 3-5 days), and from onset of symptoms to first hospital admission was 2 (1-4) days. CONCLUSION: As of early February 2020, compared with patients initially infected with SARS-Cov-2 in Wuhan, the symptoms of patients in Zhejiang province are relatively mild.