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The massive reductions in anthropogenic emissions resulting from the COVID-19 lockdown provided a unique opportunity to evaluate the effect of mitigation measures aiming to abate air pollution. In Mexico, the total lockdown period took place during the dry-hot season when biomass burning activity is enhanced. Here, we investigate the role of biomass burning emissions on regional ozone levels in the Megalopolis of Central Mexico. The studied period covers the lockdown phases 2 and 3, and the first month of the New Normal. We applied a factor separation technique and process analysis to estimate the pure and synergistic contributions of emission reductions under lockdown and that from biomass burning to daily ozone maximum concentrations in 7 metropolitan areas of different states in the Megalopolis. The results revealed that biomass burning plumes likely masked the effect of massive reductions from mobile emissions, impacted the PBL development during phase 3 and favored transition and mixed NO x -limited and VOC-limited regional regimes. This contributed to increased ozone production in the middle to lower PBL by changing the regional background levels which potentially could bias high ozone production efficiency estimations. Given the Megalopolis contribution to economic and societal development at national scale, our study suggests that ozone mitigation measures during the dry-hot season targeting mainly mobile emissions will likely be offset by biomass burning plumes. A regional and synergic policy aiming to control biomass burning would help to reduce the occurrence of high ozone levels in Central Mexico with the co-benefit of tackling short-lived climate pollutants., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Effectively identifying deviations in real-world medical time-series data is a critical endeavor, essential for early surveillance of disease outbreaks. This paper demonstrates the integration of time-series anomaly detection techniques to develop surveillance systems for disease outbreaks. Utilizing data from Sweden's telephone counseling service (1177), we first illustrate the trends in physical and mental symptoms recorded as contact reasons, offering valuable insights for outbreak detection. Subsequently, an advanced anomaly detection technique is applied incrementally to these time-series symptoms as univariate and multivariate approaches to assess the effectiveness of a machine learning-based method on early detection of the COVID-19 outbreak.

Long COVID is a disease that makes it hard for patients to get an official diagnosis while it impacts their quality of life. Many people are turning to social networks such as Facebook, WhatsApp, Twitter (now X) to express their opinions and feelings regarding Long COVID. In this paper, positive (or neutral) and negative text messages in the Greek language, posted on the Twitter platform in 2022, regarding Long COVID are analyzed and popular discussion topics are extracted. Analysis revealed that when topic modelling follows sentiment analysis more coherent topics are created. Furthermore, ChatGPT is used to assign a label to each topic that, in turn, is assessed by a human expert.

This paper explores the prevalent themes across multiple threads on the popular Swedish discussion forum Flashback. Among its diverse array of topics, the forum actively engages users in addressing and debating questions pertaining to COVID-19 vaccines and vaccination. Through distinguishing between positive and negative perspectives within posts across 14 relevant thread discussions, we employ BERTopic, a modular topic modeling framework, which utilizes pre-trained language models and applies clustering techniques to identify prevailing topics. This enables us to conduct a nuanced exploration of overarching themes, offering valuable insights into the multifaceted nature of the discussions regarding COVID-19 vaccines and vaccination in Sweden.

In this study, a hybrid model, the convolutional neural network-support vector regression model, was adopted to achieve prediction of the NO 2 profile in Nanjing from January 2019 to March 2021. Given the sudden decline in NO 2 in February 2020, the contribution of the Coronavirus Disease-19 (COVID-19) lockdown, Chinese New Year (CNY), and meteorological conditions to the reduction of NO 2 was evaluated. NO 2 vertical column densities (VCDs) from January to March 2020 decreased by 59.05% and 32.81%, relative to the same period in 2019 and 2021, respectively. During the period of 2020 COVID-19, the average NO 2 VCDs were 50.50% and 29.96% lower than those during the pre-lockdown and post-lockdown periods, respectively. The NO 2 volume mixing ratios (VMRs) during the 2020 COVID-19 lockdown significantly decreased below 400 m. The NO 2 VMRs under the different wind fields were significantly lower during the lockdown period than during the pre-lockdown period. This phenomenon could be attributed to the 2020 COVID-19 lockdown. The NO 2 VMRs before and after the CNY were significantly lower in 2020 than in 2019 and 2021 in the same period, which further proves that the decrease in NO 2 in February 2020 was attributed to the COVID-19 lockdown. Pollution source analysis of an NO 2 pollution episode during the lockdown period showed that the polluted air mass in the Beijing-Tianjin-Hebei was transported southwards under the action of the north wind, and the subsequent unfavorable meteorological conditions (local wind speed of < 2.0 m/sec) resulted in the accumulation of pollutants., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

BACKGROUND: According to Gallup survey report, 69% of U.S. workers moved to working remotely in April 2020. Due to COVID-19, work is changing rapidly, including where individuals work. OBJECTIVE: Research in this field has drawn much attention, and in the last ten years, there is a constant augment in the number of publications on the same. Several works of literature on Remote Working (RW) culture have been reported. Nevertheless, only less research is present on bibliometric reviews of RW culture. Therefore, Bibliometric research is wielded in this work and a comprehensive review of relevant publications was taken from Scopus, Web of Science (WOS), together with the Social Science Citation Index Expanded. METHODOLOGY: At first, meticulous bibliometric analyses of 430 published articles are done and descriptive information about a piece of work, namely (a) author, (b) title, (c) date of publication, et cetera, is provided. RESULTS: Mapping the scope along with the structure of the discipline is enabled by these analyzes, which discovers the instituted collaboration patterns amongst countries and organizations, and identifies authoritative papers together with authors. Therefore, the publication's evolution over time is illustrated by these analyses, and the current research interests and potential directions for future study are identified. CONCLUSIONS: This paper incorporates insights on remote working culture and enables mapping the scope and structure of the discipline, discovering the established collaboration patterns among countries and organizations. Thus, these analyses illustrate the publication's evolution over time and identified the current research interests and potential directions for the research. [ABSTRACT FROM AUTHOR]

BACKGROUND: In March 2020, with the scope to reduce the spread of COVID-19, most national governments around the world canceled in-person education and moved to online learning. Therefore, teachers and students had to adapt a new way of teaching. Most of Italian teachers never had such an experience before and encountered difficulties in effectively carrying out this process on their own. Difficulties that can naturally increase anxiety and stress, leading, in situations perceived as extreme, to burnout syndrome. OBJECTIVES: This paper endeavored to verify levels of job stress and burnout of Italian teachers caused by the COVID-19 pandemic using the validated Maslach Burnout Inventory-General. This study aimed to measure the association among the three main dimensions of burnout and the variables of teachers' personal and working lives that changed due to COVID-19. METHOD: The aim of this paper was to verify burnout state and to measure the association among the three dimensions of burnout and the personal and working lives of Italian teachers using structural equation model analysis. The analysis was conducted in December 2021 and considered the situation in which the Italian teachers (from primary to middle and upper school) are working since March 2020. RESULTS: The results showed that teachers were emotionally exhausted; they did not feel able to fully fulfill their task towards the students. This involved a high absenteeism, a lower quality of work performance and the impossibility of making an objective evaluation of the students with an inevitable flattening of the class level. In contrast, the study shows that teachers who experienced few problems had relatively low levels of burnout. CONCLUSION: The findings brought out some proposals to reduce the risk of burnout and increase the individual well-being of schoolwork organization with positive effects on the lives of students: to strengthen social identity, to avoid a full-time online connection, to promote a psychological support service and to promote resilience training. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB) remains a significant global health challenge. Indeed, according to the World Health Organization (WHO), TB is classified as the second most common cause of death worldwide due to a single infectious agent in 2022, following COVID-19. To effectively manage tuberculosis patients, it is necessary to ensure accurate diagnosis, prompt treatment initiation, and vigilant monitoring of patients' progress. In 2017, the TB Ge network was implemented and launched in two primary hospitals within the Liguria Region in Italy, with the main purpose to manage tuberculosis infections. This system, organized as a web-based tool, simplifies the manual input of patient's data and therapies, while automating the integration of test results from hospitals' Laboratory Information Systems (LIS), without requiring human intervention. The goal of this paper is to highlight the outcomes achieved through the implementation of the TB Ge network in a period seriously affected by the COVID-19 pandemia and outline future directions. More specifically, the aim is to extend its adoption to all hospitals in the Liguria Region, thus improving the management of tuberculosis infections across healthcare facilities.

The Covid-19 pandemic spurred an unprecedented shift towards digitalization, prompting a surge in telehealth practices. This paper explores the impact of the pandemic on telemedicine through a comprehensive analysis of scientific publications. Utilizing a bibliometric approach, the study examines trends in telemedicine research before and after the onset of Covid-19. The systematic search in PubMed yielded 8,454 pre-Covid-19 publications (2016-2019) and 16,633 post-Covid-19 publications (2020-2023). A total of 21,989 distinct keywords were extracted. Co-occurrence maps reveal evolving thematic clusters, with "mhealth" and "ehealth" dominating pre-Covid-19, while "Covid-19" emerges as a top keyword post-pandemic. The Top-10 keywords shift post-Covid-19, reflecting dynamic research priorities. The bibliometric approach illuminates a heightened exploration of telehealth solutions post-pandemic, emphasizing the enduring impact of the crisis on academic discourse. Changes in key terms and shifts in key term ranking indicate dynamic research priorities and a broader consideration of multidimensional healthcare challenges. Acknowledging study limitations, the analysis offers a high-level perspective, focusing on authors' keywords. Despite challenges, the study provides a systematic overview, revealing the emergence of new telemedicine application domains and the need for further in-depth analyses. Future research directions may explore the ecological impact of telemedicine applications and other intriguing aspects, contributing to a comprehensive understanding of telemedicine's scholarly trajectory.

Stock market forecasting remains a difficult problem in the economics industry due to its incredible stochastic nature. The creation of such an expert system aids investors in making investment decisions about a certain company. Due to the complexity of the stock market, using a single data source is insufficient to accurately reflect all of the variables that influence stock fluctuations. However, predicting stock market movement is a challenging undertaking that requires extensive data analysis, particularly from a big data perspective. In order to address these problems and produce a feasible solution, appropriate statistical models and artificially intelligent algorithms are needed. This paper aims to propose a novel stock market prediction by the following four stages; they are, preprocessing, feature extraction, improved feature level fusion and prediction. The input data is first put through a preparation step in which stock, news, and Twitter data (related to the COVID-19 epidemic) are processed. Under the big data perspective, the input data is taken into account. These pre-processed data are then put through the feature extraction, The improved aspect-based lexicon generation, PMI, and n-gram-based features in this case are derived from the news and Twitter data, while technical indicator-based features are derived from the stock data. The improved feature-level fusion phase is then applied to the extracted features. The ensemble classifiers, which include DBN, CNN, and DRN, were proposed during the prediction phase. Additionally, a SI-MRFO model is suggested to enhance the efficiency of the prediction model by adjusting the best classifier weights. Finally, SI-MRFO model's effectiveness compared to the existing models with regard to MAE, MAPE, MSE and MSLE. The SI-MRFO accomplished the minimal MAE rate for the 90th learning percentage is approximately 0.015 while other models acquire maximum ratings. [ABSTRACT FROM AUTHOR]

The desire to access personal and high-quality health information electronically is increasing, not only in Canada, but globally. With the advent of the COVID - 19 pandemic the desire and demand for telemedicine and timely access to personal health data such as online laboratory (lab) results has increased substantially. This study examines citizens' perspectives of being provided with high-quality information about a specific lab test (i.e., potassium) in the same display as a trend graph. Therefore, the objective of this study is to test how participants managed this additional information about the context of the test, understood, and applied it. The researchers analyzed the responses of semi-structured interviews with Canadian participants (N=24) using conventional content analysis. This paper examined four themes related to providing complementary information concurrently with lab results in the same display: 1) Benefits of Collocated Information, 2) Information Overload, 3) Misinterpretation, 4) Confusion. This study provided examples of some of the difficulties that the participants faced accessing their lab values online, while navigating and discerning complimentary high-quality health information available in their patient portal.

As COVID-19 ravages the world, social media analytics could augment traditional surveys in assessing how the pandemic evolves and capturing consumer chatter that could help healthcare agencies in addressing it. This typically involves mining disclosure events that mention testing positive for the disease or discussions surrounding perceptions and beliefs in preventative or treatment options. The 2020 shared task on COVID-19 event extraction (conducted as part of the W-NUT workshop during the EMNLP conference) introduced a new Twitter dataset for benchmarking event extraction from COVID-19 tweets. In this paper, we cast the problem of event extraction as extractive question answering using recent advances in continuous prompting in language models. On the shared task test dataset, our approach leads to over 5% absolute micro-averaged F1-score improvement over prior best results, across all COVID-19 event slots. Our ablation study shows that continuous prompts have a major impact on the eventual performance.

Natural disasters and health emergencies disproportionally affect vulnerable populations causing disruptions to usual care and increasing chronic disease burden. Data and digital technologies are important tools to identify and mitigate indirect effects of emergencies. In this paper, we describe the methods used in the development of a series of digital emergency preparedness interventions to mitigate the direct and indirect consequences of the COVID-19 pandemic in the veteran community in Australia. The case studies demonstrate the use of data for surveillance, patient phenotyping, data-driven decision support and stakeholder communication in primary care. The intervention successfully increased appropriate healthcare use by vulnerable individuals and could be expanded to other populations.

This study was aimed to identify knowledge structure and trends in severe COVID-19 risk factor using text network analysis. The 22,628 papers published during from January 2020 to December 2021. We analyzed and visualized using Text Rank analyzer and Gephi software. They were grouped into 5 central themes - biomedical factors, occupational environmental factors, demographic factors, health behavior factors, and complications. The emerging topics were identified to the chronological trends. This study can promote a systematic understanding of severe COVID-19 risk factors.

To understand and handle the COVID-19 pandemic, digital tools and infrastructures were built in very short timeframes, resulting in stand-alone and non-interoperable solutions. To shape an interoperable, sustainable, and extensible ecosystem to advance biomedical research and healthcare during the pandemic and beyond, a short-term project called "Collaborative Data Exchange and Usage" (CODEX+) was initiated to integrate and connect multiple COVID-19 projects into a common organizational and technical framework. In this paper, we present the conceptual design, provide an overview of the results, and discuss the impact of such a project for the trade-off between innovation and sustainable infrastructures.

The aim of this paper was to report patient valuation of usability with our telemedicine system and to explore the effect of pandemic in its behavior. We conducted a cross-sectional study based on the prospective recollection of the results of the Spanish abbreviated version of the Telehealth Usability Questionnaire (TUQ), from October 2019 to July 2020. We observed an inflection point of growth of answers during the pandemic era and a trend of decrease in usability valuations coinciding with the massive and forced implementation of the system after lockdown. This effect was transitory, evidencing an improvement over time. These results might be explained with a sociotechnical approach that includes considering the learning curve and suggest the importance of a telemedicine usability tool to guide decision-making. In conclusion, tools to assess telemedicine services may identify facilitators and barriers to its use in a highly changing social and technological context.

Background: Utilisation of coworking spaces (CWS) was rising sharply prior to the COVID-19 pandemic. The transition to new work arrangements which involve a hybrid, work and home model, is likely to involve the use of alternative workspaces. Understanding the impacts of CWS on employees is timely to examine the benefits of utilisation and how these might be incorporated into new ways of working. This scoping review aims to explore the relationship between CWS, mental and physical health, and workers' performance, and provide insights into future considerations for design., Objective: The main objective was to map the current literature on CWS, focusing on identification of relevant modifiable factors to improve worker's mental and physical health, and performance., Method: Three databases, Embase, PsycInfo, and Proquest, were systematically reviewed, to identify studies from 2005 onwards. Data was extracted and analysed using diagrammatic mapping. Only studies published in English were included., Results: Eleven relevant papers were included which covered the three outcomes of interest: worker's performance (5), mental health (4) and physical health (2). Environmental factors influencing the three outcomes were categorised into physical environment (12 factors) and the psychosocial environment (6 factors). Overall, CWS environmental factors had a positive influence on workers (23 positive relationships and 11 negative relationships)., Conclusion: Coworking spaces offer potential benefits for tele-workers, including opportunities for collaboration/networking and productivity gains. However, attention to the CWS physical design is important to optimise the experience for workers and mitigate risk of adverse mental and physical health effects.

Background: The COVID-19 pandemic has undeniably brought up the issue of Occupational Health and Safety (OHS) for businesses in Turkey. In this global pandemic, strategies developed to effectively address OHS risks and eliminate them with reliable, high-quality data have gained importance., Objective: This study aimed to determine the perceptions of employees in the forest products industry in Turkey regarding the OHS during the COVID-19 pandemic., Method: The sample of the descriptive and analytical study consisted of a total of 371 employees in forest product businesses located in Turkey. The sample of the study was determined by the purpose-based sampling method. Data were collected both online and face-to-face from June 2022 to October 2022. The data were obtained using the Questionnaire of Employee and Business Characteristics and the Scale of Employees' Perceptions on the OHS (S-POHS) adapted by Özden (2022). Statistical analysis was performed using SPSS and calculated by descriptive analyses, pairwise comparison tests and multivariate regression analyses., Results: 39.1% of the employees with a mean age of 34.12±8.78 years are high school graduates and 61.5% are workers. Significant differences were found in the S-POHS average scores of the employees in the businesses that produce paper-cardboard, have operated for less than 20 years, have a partnership structure with foreign capital and have an OHS Management System Quality Certificate (p < 0.05)., Conclusion: While a positive OHS perception level has already been achieved in the forest products industry in business administrations and by employees, it is clear that this should not be lost and it should be prepared for the next crises by adding new applications.

The outbreak of COVID-19 has caused concerns globally. To reduce the rapid transmission of the virus, strict city lockdown measures were conducted in different regions. China is the country that takes the earliest home-based quarantine for people. Although normal industrial and social activities were suspended, the spread of virus was efficiently controlled. Simultaneously, another merit of the city lockdown measure was noticed, which is the improvement of the air quality. Contamination levels of multiple atmospheric pollutants were decreased. However, in this work, 24 and 14 air fine particulate matter (PM 2.5 ) samples were continuously collected before and during COVID-19 city lockdown in Linfen (a typical heavy industrial city in China), and intriguingly, the unreduced concentration was found for environmentally persistent free radicals (EPFRs) in PM 2.5 after normal life suspension. The primary non-stopped coal combustion source and secondary Cu-related atmospheric reaction may have impacts on this phenomenon. The cigarette-based assessment model also indicated possible exposure risks of PM 2.5 -bound EPFRs during lockdown of Linfen. This study revealed not all the contaminants in the atmosphere had an apparent concentration decrease during city lockdown, suggesting the pollutants with complicated sources and formation mechanisms, like EPFRs in PM 2.5 , still should not be ignored., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier B.V.)

Background: During an outbreak such as COVID-19, hospital staff needs to be in close contact with all types of patients visiting the hospital and the risk of cross-infection is extremely high. Payment and medication pickup is a mandatory part of a patient's hospital visit, with direct contact between healthcare workers and patients, and long waiting times in the hospital area, which can easily lead to the spread of disease infection., Objective: This paper designed the prototype of a contactless smart medicine cabinet based on RFID technology and optimized the patient consultation and medication pickup process to address these problems., Methods: We conducted a 50-day field observation of patients for consultation and medication pickup at the First Hospital in H city, Jiangsu Province, China, and randomly timed 1600 sets of data from Surgery (ophthalmology) and Internal patients, then we designed the prototype of a contactless smart medicine cabinet based on RFID technology, optimized the patient consultation and medication pickup process, comparing the traditional and intelligent models using AnyLogic., Results: The results show that this contactless medicine cabinet was able to reduce the time taken by patients in consultation and medicine pickup by 18.74 minutes, increasing the overall efficiency of the consultation by 32.20%. The simulation revealed that this contactless intelligent medication pickup model was able to reduce the time taken by patients in consultation and medicine pickup, increasing the overall efficiency of the consultation, effectively reducing the frequency of contact between healthcare workers and patients, and reducing the risk of disease infection., Conclusion: The proposed technical model provides a new idea to solve the problems of long queues, low efficiency and high risk of infection for patients to consult and get medicine during epidemics. Especially within hospitals it has important theoretical and practical implications for epidemic prevention and control as well as future hospital management.

Perinatal death, a global health problem, can be prevented with simple resuscitation interventions that help the baby breathe immediately at birth. Latter-day Saint Charities (LDSC) and Safa Sunaulo Nepal (SSN) implemented a program to scale-up Helping Babies Breathe (HBB) training in Karnali Province, Nepal from January 2020-February 2021. The interventions were implemented using a hybrid approach with on-site mentoring in the pre/post COVID period combined with remote support and monitoring during the COVID period. This paper reports overall changes in newborn outcomes in relation to the unique implementation approach used. A prospective cohort design was used to compare outcomes of birth cohorts in 16 public health facilities in the first and last three months of program implementation. Results showed significant decreases in intrapartum stillbirths (23%), and neonatal deaths within (27%) and after (41.3%) 24 hours of life. The scale-up of HBB training resulted in 557 providers receiving training and mentoring support during the program period, half trained during the COVID period. Increased practice sessions, review meetings and debriefing meetings were reported during the COVID period compared to pre/post COVID period. The evaluation is suggestive of the potential of a hybrid approach for improved perinatal outcomes and scaling-up of newborn resuscitation trainings in health system facing disruptions.

Ecological studies suggested a link between air pollution and severe COVID-19 outcomes, while studies accounting for individual-level characteristics are limited. In the present study, we aimed to investigate the impact of short-term ambient air pollution exposure on disease severity among a cohort of 569 laboratory confirmed COVID-19 patients admitted to designated hospitals in Zhejiang province, China, from January 17 to March 3, 2020, and elucidate the possible biological processes involved using transcriptomics. Compared with mild cases, severe cases had higher proportion of medical conditions as well as unfavorable results in most of the laboratory tests, and manifested higher air pollution exposure levels. Higher exposure to air pollutants was associated with increased risk of severe COVID-19 with odds ratio (OR) of 1.89 (95% confidence interval (CI): 1.01, 3.53), 2.35 (95% CI: 1.20, 4.61), 2.87 (95% CI: 1.68, 4.91), and 2.01 (95% CI: 1.10, 3.69) for PM 2.5 , PM 10 , NO 2 and CO, respectively. OR for NO 2 remained significant in two-pollutant models after adjusting for other pollutants. Transcriptional analysis showed 884 differentially expressed genes which mainly were enriched in virus clearance related biological processes between patients with high and low NO 2 exposure levels, indicating that compromised immune response might be a potential underlying mechanistic pathway. These findings highlight the impact of short-term air pollution exposure, particularly for NO 2 , on COVID-19 severity, and emphasize the significance in mitigating the COVID-19 burden of commitments to improve air quality., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier B.V.)

Background: It has not yet been fully established that there is coronavirus disease 2019 (COVID-19) involvement in the synovial fluid and it remains a topic of debate., Objective: The aim of this study was to evaluate the presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in knee joint synovial fluid of patients with COVID-19., Methods: This retrospective study was conducted with an initial screening of patients who were admitted to a tertiary pandemic hospital due to COVID-19 symptoms, and underwent treatment for COVID-19 between March and June 2020., Results: A total of 2476 patients were hospitalized or received treatment for a possible diagnosis of COVID-19. While the RT-PCR test was positive in 318 patients (12.8%), 2158 (87.2%) were computed tomography positive but reverse transcription-polymerase chain reaction (RT-PCR) negative. Twelve patients were consulted due to acute joint effusion. Of five patients with knee joint effusion and a positive RT-PCR test, the synovial tissue RT-PCR test was positive in only one patient., Conclusion: This paper is the first to show the presence of SARS-CoV-2 in synovial fluid. This can be considered of importance for the determination and elimination of the route of transmission, thereby preventing further development and spread of the disease.

Although natural language processing (NLP) refers to a process involving the development of algorithms or computational models that empower machines to understand, interpret, and generate human language, machines are still unable to fully grasp the meanings behind words. Specifically, they cannot assist humans in categorizing words with general or technical purposes without predefined standards or baselines. Empirically, prior researches have relied on inefficient manual tasks to exclude these words when extracting technical words (i.e., terminology or terms used within a specific field or domain of expertise) for obtaining domain information from the target corpus. Therefore, to enhance the efficiency of extracting domain-oriented technical words in corpus analysis, this paper proposes a machine-based corpus optimization method that compiles an advanced general-purpose word list (AGWL) to serve as the exclusion baseline for the machine to extract domain-oriented technical words. To validate the proposed method, this paper utilizes 52 COVID-19 research articles as the target corpus and an empirical example. After compared to traditional methods, the proposed method offers significant contributions: (1) it can automatically eliminate the most common function words in corpus data; (2) through a machine-driven process, it removes general-purpose words with high frequency and dispersion rates –57% of word types belonging to general-purpose words, constituting 90% of the total words in the target corpus. This results in 43% of word types representing domain-oriented technical words that makes up 10% of the total words in the target corpus are able to be extracted. This allows future researchers to focus exclusively on the remaining 43% of word types in the optimized word list (OWL), enhancing the efficiency of corpus analysis for extracting domain knowledge. (3) The proposed method establishes a set of standard operation procedure (SOP) that can be duplicated and generally applied to optimize any corpus data. [ABSTRACT FROM AUTHOR]

Since the outbreak of COVID-19 (Corona Virus Disease 2019), the Chinese government has taken strict measures to prevent and control the epidemic. Although the spread of the virus has been controlled, people's daily life and work have been affected and restricted to varying degrees. Thus people have different sentiments, these may affect people's implementation and compliance with the policies, thus affecting the effectiveness of epidemic prevention and control. At present, few pieces of literature have analyzed the relationships between people's feelings, policies, and epidemic trends. The object of this paper is to analyze the text content on social media, to find out the impact of the epidemic blockade policy on the public mood and the concerns expressed by the public about policies changes, and the interaction between policies and epidemic states at different stages of the epidemic. In this paper, we collected the posts of two cities where the epidemic occurred at the same time for analysis and comparative study. On the one hand, we revealed the changes in public attention and attitudes in the two regions during the epidemic, the other hand, it also reflects the differences in public sentiment between the two regions, as well as the correlation between emotions and policies and epidemic trends when different policies are adopted under different circumstances. The obtained results have a certain guiding significance for public health departments to formulate reasonable epidemic prevention policies. [ABSTRACT FROM AUTHOR]

In this paper, we critically reevaluate Koch and Okamura's (2020) conclusions on the conformity of Chinese COVID-19 data with Benford's Law. Building on Figueiredo et al. (2022), we adopt a framework that combines multiple tests, including Chi-square, Kolmogorov-Smirnov, Euclidean Distance, Mean Absolute Deviation, Distortion Factor, and Mantissa Distribution. The primary rationale behind employing multiple tests is to enhance the robustness of our inference. The main finding of the study indicates that COVID-19 infections in China do not adhere to the distribution expected under Benford's Law, nor does it align with the figures observed in the U.S. and Italy. The usefulness of deviations from Benford's Law in detecting misreported or fraudulent data remains controversial. However, addressing this question requires a more careful statistical analysis than what is presented in the Koch and Okamura (2020) paper. By employing a combination of several tests using fully transparent procedures, we establish a more reliable approach to evaluating conformity to the Newcomb-Benford Law in applied research. [ABSTRACT FROM AUTHOR]

This paper proposes a new epidemic prediction model that hybridizes several models, such as the autoregressive integrated moving average model (ARIMA), random forest (RF), and response surface method (RSM). The modeling process based on ensemble empirical mode decomposition (EEMD) is particularly suitable for dealing with non-stationary and nonlinear data. ARIMA's timeliness and difference have strong deterministic information extraction ability. RF is robust and stable, with fast speed, and strong generalization ability. Under the adjustability and correspondence of the response surface, the comprehensiveness of the model is well demonstrated. Taking the United States as an example, the proposed ARIMA-RF-RSM model is used to explore the development mechanism of the early epidemic according to the data of the early epidemic of coronavirus disease 2019 (COVID-19). The proposed model has high prediction accuracy (mean absolute percentage error (MAPE) is 1.97% and root mean square error (RSME) is 7.24%). It helps to take effective prevention and control measures in time. In addition, the model has universal applicability to the analysis of disease transmission in relevant areas. [ABSTRACT FROM AUTHOR]

A threat to governments and the medical community globally are infectious illness outbreaks such as COVID-19, Spanish flu, and Ebola, which kill millions of people. Furthermore, because people are afraid to go to their employment, infectious diseases hinder economic progress. Therefore, it is imperative to implement interventions to lessen the spread of infectious diseases. From this angle, the practice of tracking contacts might be viewed as a mitigation strategy to stop the spread of contagious diseases. As this is going on, users' trace data sets are growing exponentially larger over time, making it difficult to conduct contact tracing queries over them. In this paper, a novel Spark contact-tracing query processing method is proposed. This method determines if users are suspected cases by analyzing their paths based on two variables: nearby social distance and exposure time. Additionally, the developed method makes full use of the Spark framework to address scalability issues and effectively respond to contact tracing inquiries over a wide range of trajectories. [ABSTRACT FROM AUTHOR]

This paper investigates the potential of COVID-19 detection using cough, breathing, and voice patterns. Speech-based features, such as MFCC, zero crossing rate, spectral centroid, spectral bandwidth, and chroma STFT are extracted from audio recordings and evaluated for their effectiveness in identifying COVID-19 cases from Coswara dataset. The explainable AI SHAP tool is employed which identified MFCC, zero crossing rate, and spectral bandwidth as the most influential features. Data augmentation techniques like random sampling, SMOTE, Tomek, and Edited Nearest Neighbours (ENN), are applied to improve the performance of various machine learning models used viz. Naive Bayes, K-nearest neighbours, support vector machines, XGBoost, and Random Forest. Selecting the top 20 features achieves an accuracy of 73%, a precision of 74%, a recall of 94%, and an F1-score of 83% using the Random Forest model with the Tomek sampling technique. These findings demonstrate that a carefully selected subset of features can achieve comparable performance to the entire feature set while maintaining a high recall rate. The success of the Tomek undersampling technique highlights the ability of model to handle sparse clinical data and predict COVID-19 and associated diseases using speech-based features. [ABSTRACT FROM AUTHOR]

This paper performs an analysis comparing two evolutionary explainable fuzzy models that make inferences in a pipeline with a blood test data set for COVID-19 classification. Firstly, data is preprocessed by the following stages: cleaning, imputation and ranking feature selection. Later, we perform a comparative analysis between several clustering methods used in an Evolutionary Clustering-Structured Fuzzy Classifier (ECSFC) to solve this classification problem using the Differential Evolution (DE) algorithm. Complementarily, we find that the Fuzzy Decision Tree model produces similar performance when is tuned with the DE algorithm (EFDT). The obtained results show that, simpler models are easier to explain qualitatively, i.e., increasing the number of clusters in ECSFC model or the maximum depth of the tree in EFDT model, does not necessarily help to obtain simplified and accurate models. In addition, although the EFDT model is by itself an intuitively explainable model, the ECSFC, with the help of the proposed Weighted Stacked Features Plot, generates more intuitive models that allow not only highlighting the features and the linguistic terms that defines a patient with COVID-19, but also allows users to visualize in a single graph and in specific colors the analyzed classes. [ABSTRACT FROM AUTHOR]

Background: The association between COVID-19 infection and the onset of dementia among adults 65 years and older has the potential to increase the burden of dementia worldwide significantly. Our research, which focuses on understanding the likely increase in the burden of dementia due to COVID-19 infection in the USA, has crucial public policy implications. By providing these insights, we aim to empower policymakers, healthcare professionals, researchers, and public health officials to make informed decisions and plan for the future. Objective: Project the prevalence of dementia in the United States while accounting for the impact of COVID-19 infection on the onset of dementia. Methods: A dynamic multi-state population model was developed. The model was initialized with USA demographic data and estimates of age, gender, and race-specific transition rates from the Health and Retirement Study (HRS). Results: The projected increase in the burden of dementia among Americans 65 years and older is a staggering 14.838 million by 2050. However, due to the COVID-19 pandemic, we anticipate an additional 265,000 to 677,000 older adults 65 years and older will be affected by dementia. This will escalate the burden of dementia to a potential 15.103 million to 15.515 million by 2050, a significant human toll that we must be prepared for. Conclusions: The projected dementia numbers underscore the urgent need for policy and intervention in social care services and healthcare needs planning. This includes providing robust support systems for caregivers and ensuring the healthcare staff is adequately trained to meet the healthcare needs of dementia patients and their families. [ABSTRACT FROM AUTHOR]

Background: The emotional impact of the coronavirus disease 2019 (COVID-19) pandemic on people with dementia has been quantified. However, little is known about the impact of change in home-care use owing to the pandemic. Objective: To determine the longitudinal association between dementia, change in home-care use, and depressive symptoms during the pandemic. Methods: We included data of 43,782 home-dwelling older adults from the English Longitudinal Study of Ageing (ELSA), Study of Health, Ageing and Retirement in Europe (SHARE), and National Health and Aging Trends Study (NHATS). This study considered the latest main wave survey prior to the pandemic as the baseline, and the COVID-19 survey as follow-up. In a series of coordinated analyses, multilevel binomial logistic regression model was used to examine the association between baseline dementia, change in home-care use at follow-up, and presence of depressive symptoms. Results: Dementia, using the ELSA, SHARE, and NHATS datasets, was identified in 2.9%, 2.3%, and 6.5% of older adults, and home-care use reduced in 1.7%, 2.8%, and 1.1% of individuals with dementia, respectively. Dementia was significantly associated with the increased risk of depressive symptoms in all three cohorts. However, the interaction between dementia and period (follow-up) was non-significant in SHARE and NHATS. Across all three cohorts, home-care use during the pandemic, regardless of change in amount, was significantly associated with increased depressive symptoms, compared to the non-use of home care. Conclusions: These results highlight the need for tailoring dementia care at home to promote independence and provide sustainable emotional support. [ABSTRACT FROM AUTHOR]

BACKGROUND: Patients with coronavirus disease 2019 (COVID-19) have been shown to die mainly due to disease-induced acute respiratory distress syndrome (ARDS). Prone position ventilation (PPV) is an important ventilation strategy in the management of patients with ARDS. OBJECTIVE: To investigate the application of PPV in ventilation strategies for patients with COVID-19. METHODS: Three hundred patients with COVID-19 admitted to the Intensive Care Unit (ICU) of Shanxi Bethune Hospital from January 2020 to June 2021 were retrospectively collected. Based on body position and conscious state, all patients were divided into three groups: intubation prone position group (n = 110), awake prone position group (n = 90) and supine position group (n = 100); The acute physiology and chronic health evaluation II (APACHE-II) scores, blood gas indicators, complications and other relevant clinical indicators were compared among the three groups. One-way ANOVA was used to compare means between multiple groups for quantitative information that conformed to a normal distribution. Repeated measures ANOVA was used for repeated measures data. Component comparisons were made using the Kruskal-Wallis H rank sum test for non-normally distributed quantitative data. RESULTS: One-way repeated-measures ANOVA main effect analysis showed different effects of different treatments on PaO2 in patients with COVID-19 (F treatment = 256.231, P < 0.05), with the order of awake prone position group > intubation prone position group > supine position group. The effects of the three different treatments on P/F in patients with COVID-19 (F treatment = 311.661, P < 0.05), with the order of awake prone position group > supine position group > intubation prone position group; Moreover, the three treatments had different effects on APACHE II scores in patients with COVID-19 (F treatment = 201.342, P < 0.05), with the order of intubation prone position group > supine position group > awake prone position group. CONCLUSION: Intubation prone position and awake prone position can improve lung function to some extent in patients with COVID-19, and should be applied as early as possible in patients with COVID-19-induced ARDS. [ABSTRACT FROM AUTHOR]

BACKGROUND: Occupational burnout is one of the most important consequences of the coronavirus disease pandemic, associated with psychological well-being, quality of care, and intention to leave the nursing profession. This is a major health problem with serious adverse consequences not only for nurses but also for patients and healthcare systems. OBJECTIVE: To assess burnout and its associated factors in nurses who worked in teaching hospitals during the COVID-19 pandemic in Iran. METHODS: A cross-sectional study was conducted on nurses of two COVID-19 referral hospitals in Tehran, Iran. Data were collected using the sociodemographic form and Maslach burnout inventory-human service survey (MBI-HSS) questionnaire. Data were evaluated using SPSS software version 26. RESULTS: A total of 264 participants, 52.7% (n = 139) were males and the mean of them age was 34.41±9.71 years. Almost 50% of nurses experienced burnout, in each dimension. Emotional exhaustion was associated significantly with job retention intention (P = 0.01) and depersonalization was significantly associated with gender (P = 0.02), age (P = 0.01), educational level (P = 0.004), work shifts (P = 0.006), and job retention intention (P = 0.02). In addition, personal accomplishment score was significantly associated with age (P = 0.002), marital status (P = 0.03), educational level (P = 0.03), work shift (P = 0.04) and job retention intention (P = 0.01). CONCLUSION: The burnout rate in the COVID-19 era is high among nurses associated with improper care. Psychosocial support is needed to improve mental well-being among health care workers during unpredictable conditions like pandemics. [ABSTRACT FROM AUTHOR]

Background: Previous studies reported the negative impact of social isolation on mental health in people with dementia (PwD) and their caregivers, butlongitudinal studies seem scarcer. Objective: To describe a one-year follow-up impact of the COVID-19 pandemic on PwD and their caregivers in both Brazil and Chile. Methods: This study analyzed the impact of the pandemic on the psychological and physical health of PwD and their family caregivers after one year of follow-up in three outpatient clinics in Brazil (n = 68) and Chile (n = 61). Results: In both countries, PwD reduced their functional capacity after one year of follow-up (p = 0.017 and p = 0.009; respectively) and caregivers reported worse physical and mental health (p = 0.028 and p = 0.039). Only in Chile, caregivers reported more sadness associated with care (p = 0.001), and reduced time sleeping (p = 0.07). Conclusions: In conclusion, the COVID-19 pandemic appears to have had a long-lasting impact on PwD and their caregivers. However, it is essential to acknowledge that the inherent progression of dementia itself may also influence changes observed over a year. [ABSTRACT FROM AUTHOR]

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic caused employees to be mentally absent at work and decreased their motivation and effort when they work collaboratively rather than individually due to the fear of transmission. OBJECTIVES: The study aims to test the effect of fear caused by COVID-19 on levels of presenteeism and social loafing from job tasks of employees. METHODS: Data were collected from 522 white-collar and blue-collar workers in Turkey by the snowball sampling method. Before testing the hypotheses, CFA was performed. Reliability analysis was assessed via Cronbach Alpha (FCV-19S = 0.941; PS = 0.713; SLS = 0.974), AVE (FCV-19S = 0.686; PS = 0.524; SLS = 0.725), and CR (FCV-19S = 0.734; PS = 0.722; SLS = 0.856) values. The mediating effect of fear of COVID-19 within the impact on presenteeism on social loafing was evaluated through Process Macro for SPSS. The coefficients for both direct and indirect effects were calculated in the 95% confidence interval using 5,000 bootstrap replicates. RESULTS: The results show that presenteeism has a significant positive impact on social loafing. Fear of COVID-19 has a statistically significant impact on presenteeism and social loafing. Also, fear of COVID-19 partially mediates within the impact of presenteeism on social loafing in the study. CONCLUSION: Organizations should consider employees' views to increase the level of well-being and productivity. A high level of inventiveness from organizations is compulsory to explore which organizational roles and workflow are at least remotely operated during this period. [ABSTRACT FROM AUTHOR]

With the ongoing evolution of the novel coronavirus pathogen and continuous improvements in our social environment, the mortality rate of COVID-19 is on a decline. In response to this, we introduce an adaptive control strategy known as intentional control, which offers cost-efficiency and superior control effectiveness. The classical SEIR model faces limitations in accurately representing close contacts and sub-close contacts and fails to distinguish their varying levels of infectivity. To address this, our study modifies the classical model by incorporating close contact (E) and a sub-close contact (E2) while reworking the infectious mechanism. Once the model is formulated, we employ various statistical methods to identify crucial parameters, including R2, adjusted R2, and standard deviation. For disease control, we implement an intentional control program with four distinct grades. We develop and apply a scheme in MATLAB for our proposed model, generating diverse simulation results based on realistic parameter values for discussion. Additionally, we explore a range of strategy combinations to differentiate their effectiveness under various social conditions, aiming to identify an optimal approach. Comparing the intentional control strategy to random control, our findings consistently demonstrate the superiority of intentional control across all scenarios. Furthermore, the results indicate that our approach better aligns with the characteristics of the novel coronavirus, characterized by an "extremely low fatality rate and strong infectivity," while offering detailed insights into the transmission dynamics among different compartments. [ABSTRACT FROM AUTHOR]

BACKGROUND: Throughout history, the world has faced numerous health challenges and outbreaks, but through the dedication of medical professionals, solutions have been found. Despite this, the emergence of the coronavirus (COVID-19) has presented a new and dangerous threat. Nevertheless, with unwavering commitment, health experts have triumphantly discovered a remedy. OBJECTIVES: The study aimed to examine how religious workers worldwide could contribute positively to ensuring the fair distribution of COVID-19 vaccines, addressing vaccine hesitancy, encouraging people to get vaccinated, and preventing vaccine nationalism. METHOD: Our study employed a refined qualitative content analysis methodology to categorize factual information and pave the way for subsequent policymakers. We meticulously curetted the most trustworthy secondary data sources and utilized them to analyze the study. RESULTS: The content analysis reveals that religious workers played an active role in the global coronavirus vaccination drive by working towards better vaccine distribution, encouraging people to get vaccinated and countering hesitation and nationalism. CONCLUSION: In light of the discoveries, it would be prudent for policymakers to engage religious workers in advancing the cause of public health, regardless of whether the health matters are localized, national, or global in scale. This strategy has the potential to be both alluring and convincing. [ABSTRACT FROM AUTHOR]

BACKGROUND: While mental illness (e.g., depression, anxiety) has been examined frequently in the workplace, the COVID-19 pandemic has only increased the attention towards mental illness. Mental well-being views mental health as a continuum from ill health to thriving. Few studies have examined factors associated with mental well-being in the workplace. Public stigma for mental illness, the general population's negative attitude towards mental illness, and occupational burnout are examined. OBJECTIVE: The purpose of this study was to examine the relationship between burnout and public stigma on mental well-being in a sample of employees across industries in the United States. METHODS: Employees surveyed from 16 companies from various industries were assessed. Room Here, a non-profit organization whose goal is to improve employee mental fitness, gathered data from these companies located in the western mountain region of the U.S. Data were collected during the pandemic. Across this portfolio of companies, 259 employees were included in the sample. Survey assesses respondents' mental well-being, stigma towards mental illness, and occupational burnout. Ordinary least squares (OLS) regression was used in this cross-sectional study on secondary data. RESULTS: Results suggested occupational burnout was associated with a decrease in mental well-being, while public stigma was associated with an improvement in mental well-being. CONCLUSION: This study sought to examine the relationship between mental well-being, burnout, and public stigma. Employee burnout and public stigma were related to mental well-being. Implications for burnout and stigma reduction programs are discussed as well as future research. [ABSTRACT FROM AUTHOR]