COVID-19 Data Prediction and Visualization in Mainland China

Contemporarily, COVID-19 shows a sign of recurrence in Mainland China. To better understand the situation, this paper investigates the growth pattern of COVID-19 based on the research of past data through regression models. The proposed work collects the data on COVID-19 in Mainland China from January 21st, 2020, to April 30th, 2020, including confirmed, recovered, and death cases. Based on polynomial regression and support vector machine regressor, it predicts the further trend of COVID-19. The paper uses root mean squared error to evaluate the performance of both models and concludes that there is no best model due to the high frequency of daily changes. According to the analysis, support vector machine regressors fit the growth of COVID-19 confirmed case better than polynomial regression does. The best solution is to utilize different types of models to generate a range of prediction result. These results shed light on guiding further exploration of the growth of COVID-19. © 2023 SPIE.

Analysis of Attack Patterns and Cyber Threats in Healthcare Sector

Information Technology (IT) has become the integral part of majority of businesses. Healthcare sector is also one such sector where IT adoption is increased in recent times. This adoption of IT has increased the internet exposure and hence increased the attack surface of the organisations working in healthcare sector. During covid outbreak, we have observed various cyber-attack and threats on organisations operating in healthcare sector. This paper focuses on cyber threat pattern in healthcare sector during covid-19 outbreak and post-covid-19 period. This research paper also aims to generate basic cyber awareness through generic cyber sanity checks to secure healthcare sector from malicious threat actors. The adaptation of proactive measures required to enhance the cyber hygiene of organisations becomes very essential in this sector. © 2023 IEEE.

PATTERNS OF MEDICINE USE FOR COVID-19 PATIENTS AT UNDATA HOSPITAL PALU

Background: Covid-19 cases had drastically increased. Little therapy has been formulated to respond to the situation. Aims: This study aims to illustrate the pattern of drug use in Covid-19 patients at Undata Palu Hospital. Methods: This study is a type of cross-sectional descriptive study using a cross-sectional design and collecting data retrospectively from medical records at Undata Hospital Palu in 2020. Results: In 2020, 186 patients were confirmed positive for Covid-19. There were 95 female patients (51.9%) and 50 patients at the age of 46-55 years (27.3%). The severe symptoms happened to 109 patients (59.6%). The most common clinical manifestation was cough in 127 patients (23.3%). The most common comorbidity was pneumonia (30.8%). The most widely used primary therapy was the antibiotic azithromycin applied to 155 patients (30.0%), and the most widely used supportive therapy was vitamin C among 141 patients (20.1%). Oseltamivir antiviral therapy was administered to 132 patients (25.6%) and remdesivir to 34 patients (6.6%). Conclusion: Covid-19 patients were mostly treated with antibiotic therapy (41.5%), antiviral therapy (32.2%), antimalarial therapy (15.7%), and corticosteroid therapy (10.7%). As many as 132 patients took oseltamivir, and 34 patients took remdesivir. However, for now, oseltamivir is no longer used. © 2022, Airlangga University. All rights reserved.

A Review on the analysis of Behavioral pattern of Students during pandemic COVID-19

The widespread of (covid-19) has become the major reason for many physical illnesses in addition to psychological encounters to the whole world. The psychological challenges brought in due to the Covid-19 pandemic have resulted in decrease in the learning curve of students to a very large extent risking the academic ability of students due to psychological/mental health. Hence it is a challenge to identify valid cues for disorientation in the learning ability of the student at the right time and to suggest necessary support and guidance. This paper aims to describe about the work done so far and analyzes the future challenges to be addressed based on the learning curve of a student and gives an insight of how a student can be identified to be psychologically disturbed. © 2023 IEEE.

Change in departure time for a train trip to avoid crowding during the COVID-19 pandemic: A latent class study in the Netherlands

The energy-efficient plate heat exchanger (PHE) and refrigerant R1234yf, which has a low global warming potential (GWP), can be used to realize an energy efficient heat pump (HP) system for electric vehicles (EV), extending their driving range. Therefore, the characteristics of R1234yf in an offset-fin strip (OSF) flowstructured PHE are critical for heat-exchanger design. This study investigates the condensation heat transfer coefficient (C-HTC) and two-phase frictional pressure drop (2P-FPD) of R1234yf during condensation in an OSF flow-structured PHE under various operating conditions. First, a modified Wilson plot method was used to determine the multiplier (C) and Reynolds number exponential (n) for the coolant side as -0.426 and 0.494, respectively. When the heat flux (q), average vapor quality (xa), and mass flux (G) increased, the C-HTC increased, whereas it decreased with saturation temperature (Tsat). Despite the force-convective condensation flow regime, the C-HTC increment was minimal with G at lower xa owing to the lesser significance of the shear effect. Additionally, the 2P-FPD was unaffected by q but increased considerably with an increase in xa and G and a decrease in Tsat. Based on the current experimental database, empirical correlations for forecasting friction factor and Nusselt number were developed with a 91% predictability.

Uncovering the interplay between hypertension and the inflammatory response for the patient affected by COVID-19 via mathematical modeling and computer-based analysis

We explore here the systems-based regulatory mechanisms that determine human blood pressure patterns. This in the context of the reported negative association between hypertension and COVID-19 disease. We are particularly interested in the key role that plays angiotensin converting enzyme 2 (ACE2), one of the first identified receptors that enable the entry of the SARS-CoV-2 virus into a cell. Taking into account the two main systems involved in the regulation of blood pressure, that is, the Renin-Angiotensin system and the Kallikrein-Kinin system, we follow a Bottom-Up systems biology modeling approach in order to built the discrete Boolean model of the gene regulatory network that underlies both the typical hypertensive phenotype and the hypotensive/normotensive phenotype. These phenotypes correspond to the dynamic attractors of the regulatory network modeled on the basis of publicly available experimental information. Our model recovers the observed phenotypes and shows the key role played by the inflammatory response in the emergence of hypertension. Source code go to the next url: https://github.com/cxro-cc/red_ras_kks © 2023, EasyChair. All rights reserved.

Tracking the Transit Divide: A Multilevel Modelling Approach of Urban Inequalities and Train Ridership Disparities in Chicago

Using a multilevel modelling approach, this study investigates the impact of urban inequalities on changes to rail ridership across Chicago's "L” stations during the pandemic, the mass vaccination rollout, and the full reopening of the city. Initially believed to have an equal impact, COVID-19 disproportionally impacted the ability of lower socioeconomic status (SES) neighbourhoods' to adhere to non-pharmaceutical interventions: working-from-home and social distancing. We find that "L” stations in predominately Black or African American and Hispanic or Latino neighbourhoods with high industrial land-use recorded the smallest behavioural change. The maintenance of higher public transport use at these stations is likely to have exacerbated existing health inequalities, worsening disparities in users' risk of exposure, infection rates, and mortality rates. This study also finds that the vaccination rollout and city reopening did not significantly increase the number of users at stations in higher vaccinated, higher private vehicle ownership neighbourhoods, even after a year into the pandemic. A better understanding of the spatial and socioeconomic determinants of changes in ridership behaviour is crucial for policymakers in adjusting service routes and frequencies that will sustain reliant neighbourhoods' access to essential services, and to encourage trips at stations which are the most impacted to revert the trend of declining public transport use.

Mobility and Recovery in Europe: Impact of the Covid-19 Crisis A European Comparative Analysis

Further to a first benchmark study covering new mobility behaviours and their impact on the road infrastructure, carried out by the European Union Road Federation (ERF), the Confederation of International Contractor's Associations (CICA), the French Federation of Public Works (FNTP), the European Construction Industry federation (FIEC) and Routes de France in 2019-2020, the same group published a second study in September 2021. The objective of that second study was to give a picture of the impact of the crisis caused by the pandemic on the mobility and transport sectors in 11 European countries (Belgium, Croatia, Czech Republic, France, Germany, Italy, Netherlands, Poland, Spain, Sweden, United Kingdom) The approach was to compare the evolution of mobility before and after the emergence of the health crisis. In addition, it would analyse the way in which European countries have adapted their support for transport infrastructures, further to an analysis of National Recovery and Resilience Plans, based on the European Recovery Plan ("Next Generation EU"). Beyond the main trends observed and their impact on mobility patterns and habits, the group also made recommendations on the role of road in the global mobility framework and the necessary adaptation of the road transport infrastructure. © 2023 The Authors. Published by ELSEVIER B.V.

College Students' Stress Responses and Anxiety during the 2020 COVID-19 Pandemic

The purpose of this current study was twofold: first, to identify the potential ecological risk and resiliency factors that contribute to emerging adult college students' generalized anxiety, as well as physiological and depressive responses to stress during the onset of the COVID-19 pandemic;second, to compare domestic and international college students' sources of stress, social supports, stress responses, and generalized anxiety. Results indicated elevated levels of generalized anxiety and depressive symptoms. Significant differences between international and domestic students were found in generalized anxiety, dating frustrations, and physiological responses to stress. Three separate multiple regressions on physiological responses to stress, depression, and generalized anxiety were conducted. Results and implications will be discussed.

A New Approach to Extracting Tourism Focus Points from Chinese Inbound Tourist Reviews after COVID-19

The number of inbound tourists in Japan has been increasing steadily in recent years. However, due to the COVID-19 pandemic, the number of inbound tourists decreased in 2020. This is particularly worrisome for Japan, as the number of inbound tourists is expected to reach 60 million per year by 2030. In order to help Japan's tourism industry to recover from the pandemic, we propose a method of identifying elements that attract the attention of inbound tourists (focus points) by analyzing reviews on tourist sites. We focus on Hokkaido, a popular area in Japan for tourists from China. Our proposed method extracts high-frequency n-gram patterns from reviews written by Chinese inbound tourists, showing which aspects are mentioned most often. We then use seven types of motivational factors for tourists and principal component analysis to quantify the focus points of each tourist destination. Finally, we estimate the focus points by clustering the n-gram patterns extracted from the tourists' reviews. The results show that our method successfully identifies the features and focus points of each tourist spot.

STAD: State-Transition-Aware Anomaly Detection Under Concept Drifts

The detection of temporal abnormal patterns over streaming data is challenging due to volatile data properties and lacking real-time labels. The abnormal patterns are usually hidden in the temporal context, which can not be detected by evaluating single points. Furthermore, the normal state evolves over time due to concept drift. A single model does not fit all data over time. Autoencoders are recently applied for unsupervised anomaly detection. However, they usually get expired and invalid after distributional drifts in the data stream. In this paper, we propose an autoencoder-based approach (STAD) for anomaly detection under concept drift. In particular, we use a state-transition-based model to map different data distributions in each period of the data stream into states, thereby addressing the model adaptation problem in an interpretable way. We empirically demonstrate the state transition process and evaluate the anomaly detection performance on the Covid-19 dataset of Germany. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)

Policy, Practice, and Parenthood: A Study of Teacher Mothers

This phenomenological study extends the current research on working mothers to teacher mothers. Themes highlighted include work/life enrichment, support for motherhood role, challenge to find balance, challenging cultural norms, financial challenges, and strategies for managing multiple roles. Findings reveal and highlight challenges and opportunities that exist at the intersection of the field of education and motherhood. Also provided are suggestions for advocacy efforts for norms and policies that support teacher mothers. Implications of this work are particularly relevant in the contemporary era, wherein the roles of motherhood and teacher are intensified by "the shift to online learning" as a result of the pandemic.

Representing agency and action in the #storiesfromvaccination governmental campaign in Romania. A multimodal approach

Vaccination continues to be one of the most debated topics worldwide, especially during the COVID-19 pandemic and in countries like Romania, where the COVID-19 vaccination rate is very low. Studies showed that in public pro-vaccination campaigns, despite the tendencies towards using evidence-based content strategies focusing on factual information delivered by experts, emotional content through personal stories also triggers a positive engagement in fostering vaccine confidence. In April 2021, the Romanian Government launched a new Facebook campaign entitled #storiesfromvaccination/ #povestidelavaccinare. Drawing from two concepts (point of view and multimodality) relevant to narrative online health messages, the study has a threefold aim: (1) to investigate the online engagement of the narrative perspectives in the #storiesfromvaccination campaign;(2) to provide a comparative analysis of the multimodal cohesion in the Facebook #storiesfromvaccination multimodal texts posted by various message sources;(3) to identify the various representations of agency and action in the interplay of the three metafunctions (experiential, interpersonal and textual) in the personal stories from vaccination. © The Authors.

Food insecurity, mental health and in-hospital mortality following the COVID-19 pandemic in a socially deprived UK coastal town

BackgroundPublic health interventions are essential to prevent a long tail of costly, avoidable and worsening ill health in coastal communities following the COVID-19 pandemic, yet no research exists to guide policy and practice as to which groups within coastal communities are vulnerable and most in need of such interventions. Within this aim, we explore engrained and emerging vulnerabilities of food insecurity, health and well-being for different demographic groups within the deprived coastal community of Fleetwood, Lancashire, UK, before and after the pandemic.MethodsRoutinely collected data of free school meal eligibility, community mental health referrals and hospital admissions between 28 March 2016 and 31 December 2021 were aggregated by locality and deprivation within Fleetwood. Temporal autoregressive models, generalised linear mixed models and survival analyses were employed to compare trends and associations in food insecurity, health and well-being indicators against deprivation indices, demographics, comorbidities (including COVID-19), the COVID-19 pandemic period and locality.ResultsAreas with better housing and income, but higher health and disability deprivation, showed increased levels of free school meal eligibility following the pandemic. Mental health was insensitive to the first 14 months of pandemic yet is worsened by unemployment deprivation and cardiovascular and respiratory comorbidities, with a greater predisposition to poor mental health in adolescents and young adults. After accounting for the effect of COVID-19, hospital mortality risk increased with demographic influences in fitting with the typology of coastal communities having an older population, struggling healthcare and a greater prevalence of comorbidities.ConclusionsPublic health managers and policy makers seeking to prevent worsening health and well-being within coastal communities following the pandemic should focus on broader-scale patterns reflecting entrenched poor health typical of coastal communities, and emerging food insecurity within specific demographic and deprivation groups at finer scales.

Metabolic risks and prognosis of COVID-19: are dietary patterns important? (Special Issue: The relationship between nutrition and COVID-19.)

Purpose: This study aims to identify the dietary patterns of two groups of subjects (with and without COVID-19), and to assess the relationship of findings with the prognosis of COVID-19 and metabolic risk parameters. Design/methodology/approach: This study included 100 individuals in the age range of 19-65 years. The medical history, and data on biochemical, hematological and inflammatory indicators were retrieved from the files. A questionnaire for the 24-h food record and the food intake frequency was administered in face-to-face interviews, and dietary patterns of subjects were assessed. Findings: In individuals with COVID-19, the hip circumference, the waist-hip ratio and the body fat percentage were significantly higher (p < 0.05), and the muscle mass percentage was significantly lower (p < 0.05). Mediterranean diet adherence screener (MEDAS), dietary approaches to stop hypertension (DASH) and healthy eating index-2015 (HEI-2015) scores were low in the two groups. A linear correlation of DASH scores was found with the muscle mass percentage (p = 0.046) and a significant inverse correlation of with the body fat percentage (p = 0.006). HEI-2015 scores were significantly and negatively correlated with body weight, body mass index, waist circumference, hip circumference and neck circumference (p < 0.05). Every one-unit increase in MEDAS, DASH and HEI-2015 scores caused reductions in C-reactive protein levels at different magnitudes. Troponin-I was significantly and negatively correlated with fruit intake (p = 0.044), a component of a Mediterranean diet and with HEI-2015 total scores (p = 0.032). Research limitations/implications: The limitation of this study includes the small sample size and the lack of dietary interventions. Another limitation is the use of the food recall method for the assessment of dietary patterns. This way assessments were performed based on participants' memory and statements. Practical implications: Following a healthy diet pattern can help reduce the metabolic risks of COVID-19 disease. Originality/value: Despite these limitations, this study is valuable because, to the best of the authors' knowledge, it is the first study demonstrating the association of dietary patterns with disease prognosis and metabolic risks concerning COVID-19. This study suggests that dietary patterns during the COVID-19 process may be associated with several metabolic risks and inflammatory biomarkers.

Students' Listening Skills and Psychology Awareness in a Pandemic: An ESL Perspective

Whether or not listening skills can be used to measure grammatical ability has been a hot topic of controversy in the language learning community. However, existing study results aren't satisfactory in and of themselves. As a result, considering the current research gap, this study was carried out to solve the issue. To begin, the researchers used a mean listening score to assess the listening skills of 50 ESL first-year students at a public institution. Second, frequency and sentence analysis were used to identify the students' most frequent mistakes and difficulty in listening for grammatical differences. When hearing comprehension outcomes were being evaluated, a written composition activity was used to verify them. There was a strong correlation between the degree of skill of the respondents in listening for grammatical differences between the single and plural forms of subjects and predicates, as well as in the ability to distinguish between the present and past tense verbs. However, more complicated syntactic formulations were discovered to have specific issues. According to the results, the listeners get disoriented and disturbed when the test sentence contains intermediate words, phrases, or subordinate sentences. These difficulties in remembering what they learned at school may result from socio-psychological issues—the intervening words impaired their ability to concentrate and recall information when they were not in a classroom. As a result of the COVID 19 epidemic, instructors and students alike have been compelled to adapt to a new learning environment: the virtual classroom. Online testing and results processing was done under this, considering that electronic platforms are a moderating factor. EFL and ESL specialists like (Shao et al., 2019) and (Walsh & Rsquez 2020) have recognized that this alteration in the modality of teaching influences methods of learning that have not necessarily been delineated previously. While not the primary focus of the research, this work still underlines the new socio-technological component as an essential mediator of listening evaluation for greater grammatical competence © 2023, Journal for ReAttach Therapy and Developmental Diversities.All Rights Reserved.

Prescribing Trends in Psychotropic Medications Among Outpatients of a Latin American Healthcare Setting: A Five-Year Retrospective Study.

Introduction Mental health problems affect millions worldwide, and the prescription of psychotropic drugs is increasing globally. The World Health Organization (WHO) has emphasized the need for proper monitoring of psychotropic drug prescriptions. This study aims to characterize and find trends in the prescription of psychotropics in a Latin American General Hospital. Methods The study analyzed the dispensation of psychotropic prescriptions to outpatients at three pharmacies in the central headquarters of Hospital Clínica Bíblica in San José, Costa Rica, from 2017 to 2021. Psychotropic drugs were classified by the Anatomical Therapeutic Chemical (ATC) code, and the amount of each medication dispensed was standardized using the defined daily dose per 10,000 population per day metric. Patients' ages were categorized into four groups: under 18 years, 18 to 39 years, 40 to 64 years, and 65 years and above. The prescriptions were categorized according to medical specialty. Regression analyses were performed to determine the significance of trends observed in the data Results A total of 5793 psychotropic prescriptions were recorded. The average age of the patients was 58 years. The total consumption of psychotropics decreased by 33.94% from 2017 to 2021, with the most significant decline until 2020. However, there was an increase in consumption in 2021. Clonazepam was the most consumed medication, followed by bromazepam and alprazolam, which was the sole drug to exhibit an escalation in usage between 2017 and 2021. Regression analysis showed that only alprazolam and zopiclone had statistically significant trends. The highest number of prescriptions was dispensed to patients aged between 40 and 64 years, followed by those aged over 65 years. Anxiolytics were also the most commonly prescribed group of drugs. General medicine (20.22%), psychiatry (19.95%), and internal medicine (12.73%) were the primary specialties that prescribed psychotropic; 38.6% of prescriptions were associated with the 10th decile of patients, and 44.9% of prescriptions were issued by the 10th decile of physicians.  Conclusion The consumption of psychotropic drugs decreased from 2017 to 2020 but increased in 2021, with alprazolam being the only drug that showed an increase in consumption throughout the entire period. General practitioners and psychiatrists were found to be the specialties that most commonly prescribe these medications. The study found significant trends only for the consumption of alprazolam and zopiclone and for prescription patterns among psychiatrists and internal medicine physicians.

Data mining methodology for obtaining epidemiological data in the context of road transport systems.

Millions of people use public transport systems daily, hence their interest for the epidemiology of respiratory infectious diseases, both from a scientific and a health control point of view. This article presents a methodology for obtaining epidemiological information on these types of diseases in the context of a public road transport system. This epidemiological information is based on an estimation of interactions with risk of infection between users of the public transport system. The methodology is novel in its aim since, to the best of our knowledge, there is no previous study in the context of epidemiology and public transport systems that addresses this challenge. The information is obtained by mining the data generated from trips made by transport users who use contactless cards as a means of payment. Data mining therefore underpins the methodology. One achievement of the methodology is that it is a comprehensive approach, since, starting from a formalisation of the problem based on epidemiological concepts and the transport activity itself, all the necessary steps to obtain the required epidemiological knowledge are described and implemented. This includes the estimation of data that are generally unknown in the context of public transport systems, but that are required to generate the desired results. The outcome is useful epidemiological data based on a complete and reliable description of all estimated potentially infectious interactions between users of the transport system. The methodology can be implemented using a variety of initial specifications: epidemiological, temporal, geographic, inter alia. Another feature of the methodology is that with the information it provides, epidemiological studies can be carried out involving a large number of people, producing large samples of interactions obtained over long periods of time, thereby making it possible to carry out comparative studies. Moreover, a real use case is described, in which the methodology is applied to a road transport system that annually moves around 20 million passengers, in a period that predates the COVID-19 pandemic. The results have made it possible to identify the group of users most exposed to infection, although they are not the largest group. Finally, it is estimated that the application of a seat allocation strategy that minimises the risk of infection reduces the risk by 50%.

Big data analytics and smart cities: applications, challenges, and opportunities.

Urban environments continuously generate larger and larger volumes of data, whose analysis can provide descriptive and predictive models as valuable support to inspire and develop data-driven Smart City applications. To this aim, Big data analysis and machine learning algorithms can play a fundamental role to bring improvements in city policies and urban issues. This paper introduces how Big Data analysis can be exploited to design and develop data-driven smart city services, and provides an overview on the most important Smart City applications, grouped in several categories. Then, it presents three real-case studies showing how data analysis methodologies can provide innovative solutions to deal with smart city issues. The first one is an approach for spatio-temporal crime forecasting (tested on Chicago crime data), the second one is methodology to discover mobility hotsposts and trajectory patterns from GPS data (tested on Beijing taxi traces), the third one is an approach to discover predictive epidemic patterns from mobility and infection data (tested on real COVID-19 data). The presented real-world cases prove that data analytics models can effectively support city managers in tackling smart city challenges and improving urban applications.