Detect misinformation of COVID-19 using deep learning: A comparative study based on word embedding

Since its emergence in December 2019, there have been numerous news of COVID-19 pandemic shared on social media, which contain information from both reliable and unreliable medical sources. News and misleading information spread quickly on social media, which can lead to anxiety, unwanted exposure to medical remedies, etc. Rapid detection of fake news can reduce their spread. In this paper, we aim to create an intelligent system to detect misleading information about COVID-19 using deep learning techniques based on LSTM and BLSTM architectures. Data used to construct the DL models are text type and need to be transformed to numbers. We test, in this paper the efficiency of three vectorization techniques: Bag of words, Word2Vec and Bert. The experimental study showed that the best performance was given by LSTM model with BERT by achieving an accuracy of 91% of the test set. © 2023 IEEE.

Predicting User Satisfaction of Mobile Healthcare Services Using Machine Learning: Confronting the COVID-19 Pandemic

The outbreak of COVID-19 led to rapid development of the mobile healthcare services. Given that user satisfaction is of great significance in inducing marketing success in competition markets, this research explores and predicts user satisfaction with mobile healthcare services. Specifically, the current research aimed to design a machine learning model that predicts user satisfaction with healthcare services using big data from Google Play Store reviews and satisfaction ratings. By dealing with the sentimental features in online reviews with five classifiers, the authors find that logistic regression with term frequency-inverse document frequency (TF-IDF) and XGBoost with bag of words (BoW) have superior performances in predicting user satisfaction for healthcare services. Based on these results, the authors conclude that such user-generated texts as online reviews can be used to predict user satisfaction, and logistic regression with TF-IDF and XGBoost with BoW can be prioritized for developing online review analysis platforms for healthcare service providers.

Research on Development of Sponsorship Effect Analysis Module Using Text Mining Technique

To expand the sponsorship market, sponsorship activities based on sponsorship effect analysis data through scientific and systematic analysis must be carried out. As the development of the media industry and the COVID-19 pandemic have caused many changes in the method of broadcasting professional sports, it is necessary to upgrade the analysis of sponsorship effects. In a crisis situation where the sponsorship effect analysis market is shrinking due to the COVID-19 pandemic, the development of brand exposure analysis programs that can be used based on online platforms will expand the sponsorship market and promote changes in the domestic analysis market that relies on overseas analysis programs. In this study, more than 200,000 online sports media data were collected to analyze the sponsorship effect by minimizing the omission of sponsor brands exposed through online media and detecting sophisticated data. A commercial dictionary in the sports field was established and a sponsorship effect analysis module was developed by quantifying text data using morpheme analysis and TF-IDF. A module based on UI was implemented to analyze the results of the custom morpheme analyzer and Elasticsearch Term Vectors AP. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Development of practice support system for violin bowing

Recently, there has been an increase in the demand for playing musical instruments at home, primarily because of the restrictions enforced due to the COVID-19 pandemic. We propose a system that can automatically estimate the angle between the bow and the string of a violin during playing from the playing sound and provide feedback to the player to realize a violin bowing practice support system. In this study, the automatic estimation of the angle performed using harmonics structure of the violin tone. The angle between the bow and the string were estimated using the ratio values between the harmonics level in the low frequency band and the level in the total frequency band. It was confirmed that there was a significant difference in the ratio between the sound played at a right angle and the sound played at an acute angle of approximately 20-30 degrees from the right angle. We are developing a practice support system using this method. The system judges the bowing angle to be good or bad and provides feedback to the player using LEDs. In full paper, we will describe the bowing skill identification method on the system in detail and report the evaluation results of the system. © 2022 IEEE.

Pandemic Risk Management. Protecting people while ensuring business continuity

The COVID-19 pandemic swept across the globe in the latter half of 2019, throughout 2020 and into 2021. In response, many companies implemented work from home policies, while others stopped operations entirely in an effort to limit the spread throughout their workforce and supporting communities. This containment strategy was not universally viable;long-term shutdowns impacted the economic viability of companies, and some industries were designated as an 'essential service' and thus continued operations. These employers faced the proposition of balancing the needs of the business and the community with a continued responsibility to provide a safe workplace for employees. This paper, and accompanying presentation, will demonstrate how the application of bowtie analysis, a commonly adopted methodology in high hazard industries, can help the risks associated with continued operation in a pandemic to be better understood and managed, thus ensuring the safety of both your personnel and business. © 36th Center for Chemical Process Safety International Conference, CCPS 2021 - Topical Conference at the 2021 AIChE Spring Meeting and 17th Global Congress on Process Safety.

Analysis of the Serologic Response to Covid-19 Vaccination in Paediatric Inflammatory Bowel Disease

Introduction: Paediatric Inflammatory Bowel Disease (pIBD) is a chronic disease that often requires immunosuppressive drugs such as glucocorticoids, thiopurines or biologic therapy, which may attenuate the response to certain vaccines. The SARS-CoV2 pandemic in 2020 prompted the rapid development of multiple vaccines and, although there are not many studies regarding their response in patients with IBD, it seems that there are differences in adults patients in relation to the treatment they receive. To the best of our knowledge, there is no literature on paediatric patients with IBD. In July 2021, vaccination against COVID 19 was authorised for adolescent patients from 12 years old. Aims & Methods: The aim of the present study is to assess the response to COVID-19 vaccination in pIBD patients. A prospective study was conducted in a tertiary hospital from July to December 2021 including pIBD patients from 12 to 18 years of age who agreed to be vaccinated. We determined baseline COVID-19 serostatus and analysed the serologic response after the complete vaccination regimen: 1 dose (patients with previous COVID- 19 infection) or 2 doses (those with no previous infection) of mRNA vaccine. During this period, three different immunoassay tests have been used for the semi-quantitative and qualitative determination of IgG antibodies against SARS-CoV2, which use different units of measurement and are not comparable with each other. We recorded clinical and epidemiological data. Statistical analysis was performed using SPSS software. Result(s): We included a total of 33 patients, 19 (56%) were male. The median age was 14.85 years (age range from 12 to 17.7). A total of 26 (79%) were diagnosed with Crohn's Disease, five (15%) with Ulcerative Colitis and two (6%) with unclassified IBD. Up to 23 patients (70%) were receiving biologic treatment and 20 (61%) had immunosuppressive treatment. Eight participants (24%) have undergone a COVID-19 infection, and in all cases reported mild or non-existent symptoms: seven of them (88%) were infected before the vaccination and only one (12%) after it. A total of 32 patients (97%) received the BioNTech/Pfizer vaccine (COMIRNATY) and one received MODERNA. Only five participants (15%) reported side effects after the vaccination, and these were in all cases mild (myalgia, headache, and low-grade fever, lasting less than 24 hours). Both the baseline and the post-vaccination serologic status were determined in 22 patients, and in seven patients only the post-vaccination status was carried out. All of them showed an adequate serologic response after the complete vaccination regimen. The development of adverse effects was independent of having suffered COVID-19 (p=0.17) and independent of treatment (p= 0.12). We found no statistical differences between patients receiving thiopurines or biologic treatment versus those without this kind of treatment (p=0.253 and p=0.521 respectively). Conclusion(s): The present preliminary study suggests that the pIBD population show an adequate response to the recommended vaccination regimen and the approved vaccines seem to be safe in this group of patients. Receiving thiopurines or biologictreatment did not seem to influence the serologic response. However, the small number of patients and the impossibility to compare antibody levels with different tests, limits the drawing of conclusions. Further studies are needed to stablish the duration and efficacy of the protective effect against COVID-19, and the potential need of a booster dose of the vaccine for pIBD patients.

Assessment of COVID-19 barrier effectiveness using process safety techniques.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes a respiratory illness called the novel coronavirus 2019 (COVID-19). COVID-19 was declared a pandemic on March 11, 2020. Bow tie analysis (BTA) was applied to analyze the hazard of SARS-CoV-2 for three receptor groups: patient or family member at the IWK Health Centre in acute care, staff member at a British Columbia Forest Safety Council (BCFSC) wood pellet facility, and staff member at the Suncor refinery in Sarnia, Ontario. An inherently safer design (ISD) protocol for BTA was used as a guide for evaluating COVID-19 barriers, and additional COVID-19 controls were recommended. Two communication tools were developed from the IWK bow tie diagram to disseminate the research findings. This research provides lessons learned about the barriers implemented to protect people from contracting COVID-19, and about the use of bow tie diagrams as communication tools. This research has also developed additional example-based guidance that can be used for the COVID-19 pandemic or future respiratory illness pandemics. Recommended future work is the application of BTA to additional industries, the consideration of ISD principles in other control types in the hierarchy of controls (HOC), and further consideration of human and organizational factors (HOF) in BTA.

Risk and Crisis Management – Semiconductor Shortages in the Automotive Industry

As the epidemic situation became more widespread in the wake of the Covid-19 pandemic, the economic impact on a wide variety of industries began to grow steadily. One sector particularly affected by this issue is the automotive indus-try, which relies on a smooth supply chain largely because of the large number of electrical and mechanical components. One of these components are so-called semiconductors. The following article presents an analysis of the semiconductor shortage in the automotive industry in the context of risk and crisis management and derives recommendations for action. In addition to outlining the causes, the article focuses primarily on the question of how solution strategies for preventing a global semiconductor shortage could look in the future. The central question for this consideration is: could the current situation have been prevented by more effective risk and crisis management?. © 2022 Walter de Gruyter GmbH, Berlin/Boston, Germany.

Pandemic Risk Management;Protecting people while ensuring business continuity

The COVID-19 pandemic swept across the globe in the latter half of 2019,throughout 2020 and into 2021. In response,many companies implemented work from home policies,while others stopped operations entirely in an effort to limit the spread throughout their workforce and supporting communities. This containment strategy was not universally viable;long-term shutdowns impacted the economic viability of companies,and some industries were designated as anessential service and thus continued operations. These employers faced the proposition of balancing the needs of the business and the community with a continued responsibility to provide a safe workplace for employees. This paper,and accompanying presentation,will demonstrate how the application of bowtie analysis,a commonly adopted methodology in high hazard industries,can help the risks associated with continued operation in a pandemic to be better understood and managed,thus ensuring the safety of both your personnel and business. Copyright © American Institute of Chemical Engineers. All rights reserved.

Integrating Risk Assessment and Decision-Making Methods in Analyzing the Dynamics of COVID-19 Epidemics in Davao City, Mindanao Island, Philippines.

The COVID-19 pandemic has become a public health crisis in the Philippines and the attention of national and local health authorities is focused on managing the fluctuating COVID-19 cases. This study presents a method that integrates risk management tools into health care decision-making processes to enhance the understanding and utilization of risk-based thinking in public health decision making. The risk assessment consists of the identification of the key risk factors of the COVID-19 contagion via bow-tie diagrams. Second, the safety controls for each risk factor relevant to the Davao City context are taken into account and are identified as barriers in the bow-tie. After which, the prioritization of the identified COVID-19 risks, as well as the effectiveness of the proposed interventions, is performed using the analytic hierarchy process. Consequently, the dynamics of COVID-19 management initiatives were explored using these priorities and a system of ordinary differential equations. Our results show that reducing the number of COVID-19 fatalities should be the top priority of the health authorities. In turn, we predict that the COVID-19 contagion can be controlled and eliminated in Davao city in three-month time after prioritizing the fatalities. In order to reduce the COVID-19 fatalities, health authorities should ensure an adequate number of COVID-ready ICU facilities. The general public, on the other hand, should follow medical and science-based advice and suspected and confirmed COVID-19 patients should strictly follow isolation protocols. Overall, an informed decision-making is necessary to avoid the unwanted consequences of an uncontrolled contagion.

Predicting User Satisfaction of Mobile Healthcare Services Using Machine Learning: Confronting the COVID-19 Pandemic

Outbreak of the COVID-19 leads to rapid development of the mobile healthcare services. Given that user satisfaction is of great significance in inducing marketing success in competition markets, this research explores and predicts user satisfaction with mobile healthcare services. Specifically, the current research aimed to design a machine learning model that predicts user satisfaction with healthcare services using big data from Google Play Store reviews and satisfaction ratings. By dealing with the sentimental features in online reviews with five classifiers, the authors find that Logistic regression with term frequency-inverse document frequency (TF-IDF) and XGBoost with Bag of words (BoW) have superior performances in predicting user satisfaction for healthcare services. Based on these results, the authors conclude that such user-generated texts as online reviews can be used to predict user satisfaction, and Logistic regression with TF-IDF and XGBoost with BoW can be prioritized for developing online review analysis platforms for healthcare service providers.

Exploration of Public Emotion Dynamics in Japan from Twitter Data during COVID-19

The COVID-19 has affected human lives in many ways throughout the globe. Several recent studies indicated that it has greatly impacted people's income. Many people have become jobless and many business entities have already closed especially in the travelling, tourism, entertainment, and restaurant sectors. Anecdotal evidences suggest that the pandemic has severely affected mental health issues. However, systematic studies for tracking public worries towards health and economy due COVID-19, regarded as hWorry and eWorry, over time are still lacking. In this study, several supervised machine learning models have been applied to a collection of public tweets to explore the mentioned worries. Experimental analysis with a set of 4072 tweets spanning six-month from January 2020 to June 2020 have discriminated tweets into hWorry and eWorry classes with 61% accuracy. By applying a lexicon-based approach to the classified tweets, our approach reveals how the four selected emotions, i.e., fear, anger, sadness, and trust propagated over time. While the fear and trust emotions showed dominant temporal patterns in both classes, the average anger and sadness emotions were stronger in the e Worry class as compared to those in the h Worry class suggesting the necessity of more viable economic policies to overcome corona calamity. © 2021 IEEE.

Identification of Maritime Education and Training Institutions (METIs) risk in pandemic restrictions

The unprecedented COVID-19 crisis apparently has questioned our systems' survivability nationally or even in a global context. The pandemic has proven the indispensable role of international shipping in our societies’ sustainability. Still, one of the main challenges for the shipping industry is to secure the supply of competent seafarers. Typically, Maritime Education and Training Institutions' (METIs’) core mission revolves around keeping such demand supplied, however in restrictive situations, METIs' capability to achieve their mission is still questionable. During the pandemic restrictions, METIs are likely exposed to many uncertainties that directly threaten their role and may lead to hazardous consequences. In such scenarios, many questions arise to challenge whether the institution/organizational levels of control are sufficient or additional barriers to keep the risk as low as reasonably practicable are needed. Consequently, this research investigates the possible threats exposed to METIs under such conditions, the potential consequences if they lose control of their operations, and the required barriers to prevent, detect, or protect the METIs from such a failure. To achieve this aim, a survey was designed to capture the expertise of a group of Maritime Education and Training (MET) experts. The survey responses have been quantified and statistically analysed to comprehensively identify these risk factors, their contribution, and their effectiveness. © 2021 21st Annual General Assembly, IAMU AGA 2021 - Proceedings of the International Association of Maritime Universities ,IAMU Conference. All rights reserved.

Application of bow tie analysis and inherently safer design to the novel coronavirus hazard.

This work involves the application of process safety concepts to other fields, specifically bow tie analysis and inherently safer design (ISD) to COVID-19. An analysis framework was designed for stakeholders to develop COVID-19 risk management plans for specific scenarios and receptor groups. This tool is based on the incorporation of the hierarchy of controls (HOC) within bow tie analysis to identify priority barriers. The analysis framework incorporates inherently safer design (ISD) principles allowing stakeholders to assess the adequacy of controls along with the consideration of degradation factors and controls. A checklist has also been developed to help stakeholders identify opportunities to apply the ISD principles of minimization, substitution, moderation, and simplification. This work also considers barrier effectiveness with respect to human and organization factors (HOF) in degradation factors and controls. This paper includes a collection of bow tie elements to develop bow tie diagrams for specific receptor groups and scenarios in Nova Scotia, Canada. The pandemic stage (At-Peak or Post-Peak) and its influence on different scenarios or settings is also considered in this work. Bow tie diagrams were developed for numerous receptor groups; bow tie diagrams modelling a generally healthy individual, a paramedic and a hair salon patron contracting COVID-19 are presented in this work.

Classification of the Disposition of Patients Hospitalized with COVID-19: Reading Discharge Summaries Using Natural Language Processing.

BACKGROUND: Medical notes are a rich source of patient data; however, the nature of unstructured text has largely precluded the use of these data for large retrospective analyses. Transforming clinical text into structured data can enable large-scale research studies with electronic health records (EHR) data. Natural language processing (NLP) can be used for text information retrieval, reducing the need for labor-intensive chart review. Here we present an application of NLP to large-scale analysis of medical records at 2 large hospitals for patients hospitalized with COVID-19. OBJECTIVE: Our study goal was to develop an NLP pipeline to classify the discharge disposition (home, inpatient rehabilitation, skilled nursing inpatient facility [SNIF], and death) of patients hospitalized with COVID-19 based on hospital discharge summary notes. METHODS: Text mining and feature engineering were applied to unstructured text from hospital discharge summaries. The study included patients with COVID-19 discharged from 2 hospitals in the Boston, Massachusetts area (Massachusetts General Hospital and Brigham and Women's Hospital) between March 10, 2020, and June 30, 2020. The data were divided into a training set (70%) and hold-out test set (30%). Discharge summaries were represented as bags-of-words consisting of single words (unigrams), bigrams, and trigrams. The number of features was reduced during training by excluding n-grams that occurred in fewer than 10% of discharge summaries, and further reduced using least absolute shrinkage and selection operator (LASSO) regularization while training a multiclass logistic regression model. Model performance was evaluated using the hold-out test set. RESULTS: The study cohort included 1737 adult patients (median age 61 [SD 18] years; 55% men; 45% White and 16% Black; 14% nonsurvivors and 61% discharged home). The model selected 179 from a vocabulary of 1056 engineered features, consisting of combinations of unigrams, bigrams, and trigrams. The top features contributing most to the classification by the model (for each outcome) were the following: "appointments specialty," "home health," and "home care" (home); "intubate" and "ARDS" (inpatient rehabilitation); "service" (SNIF); "brief assessment" and "covid" (death). The model achieved a micro-average area under the receiver operating characteristic curve value of 0.98 (95% CI 0.97-0.98) and average precision of 0.81 (95% CI 0.75-0.84) in the testing set for prediction of discharge disposition. CONCLUSIONS: A supervised learning-based NLP approach is able to classify the discharge disposition of patients hospitalized with COVID-19. This approach has the potential to accelerate and increase the scale of research on patients' discharge disposition that is possible with EHR data.