Project transition to the digital Middle Ages via COVID-19 pattern

The analysis of current trends in the implementation of effective socio-economic solutions and their development under the influence of COVID-19 is made. The prospects of using innovative and telecommunication technologies, robotics, big data processing methods and knowledge management methods in the formation and management of global economic clusters were noted. The clustering of delivery robots under pandemic conditions by methods of machine learning was carried out. The peculiarities of COVID-19 assessment as the main formative factor influencing socio-economic decision-making on a global scale are disclosed. The necessity and possible consequences of adopting and implementing new decisions designed to minimize the negative effects of COVID-19 on Russian and global economies are discussed. It is noted that the design and development of innovations in the system of management and transfer of knowledge is an indispensable condition for the successful development of future socio-economic relations. On the basis of the obtained results conclusions are made about the background of the applied solutions, about the vector of their direction and makes it clear what should be paid special attention to when assessing the current situation in society and determine which solutions are most effective and how the social order should be transformed to successfully withstand the new challenges. © 2023 SPIE.

The Relationship of SEDS, Food Intake and Work Engagement among Telecommuting Employees of the Education Sector during the COVID-19 Pandemic: A Mixed Method Study

Due to COVID-19, the shift to telecommuting became a widely used work set-up to maintain economic balance. This work set up is associated with risks to employees' wellness. As prevention to the risks, employees must be provided with ways to understand the telecommuting attributes. In relation, this study targets in understanding the links between the socio-economic demographic status, work engagement, and food intake of the education sector's tele-employees. The 110 samples are gathered from the Senior High school Department using convenience sampling, an online survey, and the mixed method. ANOVA and multi-linear regression are used as statistical treatments. The study found that the older generation with higher Income is more likely linked with higher work engagement. The younger generation, low-income earners, and males are inclined more toward unhealthy foods as compared to their counterparts. Low-income earners perceived that their work engagement falls under the category that energy to work is at a bare minimum level. The participants' education attainment revealed significance with energy-giving or carbohydrate-source foods. The qualitative data highlighted job position was perceived with a link to food intake and work engagement. Unhealthy food consumption is perceived with a beneficial association with work engagement, although it is suggested for further investigation. With these findings, the education sector's stakeholders, nutrition, mental health professionals, and future researchers would mainly benefit from this study for intervention generation. © 2023 ACM.

Towards a smart pervasive conversational agent for COVID-19 psychological assistance based on NLP

Diverse countries throughout the world were quar-antined due to the novel pandemic known as COVID-19, even after vaccination,. As a result of this grim circumstance, most socioeconomic and political spheres have encountered deep crisis and from there people have experienced stress, anxiety, depression, and even suicide, In this paper, we propose a smart pervasive conversational agent for psychological assistance during and after COVID-19 quarantine, which could converse with a regular citizen to raise awareness of the genuine threat of the outbreak and the importance of vaccination. Our proposed conversational agent could be able to recognize and manage stress and anxiety using natural language understanding (NLU) and international stress and anxiety scales. The messages given by our agent and its mode of communication may help to alleviate anxiety following the world's lockdown. Our agent's comment threads and management styles may be able to soothe people's worry during the world's lockdown. Our proposed approach is a mobile healthcare service with three interdependent units: an input processing (IP) that performs natural language understanding (NL), a Storage that stores every interaction, and a response manager (RM) that controls the responses of our conversational agent. © 2022 IEEE.

When do data visualizations persuade? The impact of prior attitudes on learning about correlations from scatterplot visualizations

Data visualizations are vital to scientific communication on critical issues such as public health, climate change, and socioeconomic policy. They are often designed not just to inform, but to persuade people to make consequential decisions (e.g., to get vaccinated). Are such visualizations persuasive, especially when audiences have beliefs and attitudes that the data contradict? In this paper we examine the impact of existing attitudes (e.g., positive or negative attitudes toward COVID-19 vaccination) on changes in beliefs about statistical correlations when viewing scatterplot visualizations with different representations of statistical uncertainty. We find that strong prior attitudes are associated with smaller belief changes when presented with data that contradicts existing views, and that visual uncertainty representations may amplify this effect. Finally, even when participants' beliefs about correlations shifted their attitudes remained unchanged, highlighting the need for further research on whether data visualizations can drive longer-term changes in views and behavior. © 2023 ACM.

Comparing COVID spread to mask-wearing rates in public transportation using AI detection

The spread of COVID-19 has resulted in several changes worldwide. In particular, border closures and economic stagnation have significantly affected societies. Although the implementation of preventive measures has improved the pandemic scenario in several countries, the effectiveness of vaccines has decreased with the emergence of mutant viruses. With this background, the use of masks is considered the best method for preventing the spread of the virus. Notably, public transportation is closely related to socioeconomic activities, and the spread of infectious diseases is more likely in closed, dense, and congested areas. Moreover, the probability of infection during public transportation also depends on the proportion of commuters wearing masks. Based on the closed-circuit television footage of various public transportation spaces, the number of mask wearers can be analysed using artificial intelligence deep learning, and the probability of COVID-19 spread can be predicted by determining the proportion of mask wearers among the commuters. With this background, in this study, the importance of masks in controlling the spread of the virus is confirmed. In conclusion, appropriate measures can be implemented by determining the probability of infection according to the mask-wearing rate in public transportation spaces. © 2023 ICE Publishing: All rights reserved.

Acceleration of Remote Work and Coworking Practices in Estonia During the COVID-19 Pandemic

Estonia is a country with a small economy and a high level of digitalization that was more ready for remote work during the COVID-19 pandemic than many other countries. This paper shows how a small and flexible society with its institutions reacted to turbulent times and what developments it has brought along. We use data from Statistics Estonia and other public sources, as well as previous qualitative studies on coworking spaces in Estonia. We conclude that employees' preferences towards hybridity and remote practices and the readiness of employers to meet them, supported by the high pre-pandemic level of digitalization and developed ICT sector, could improve the revitalization of rural and deprived regions and reduce the socioeconomic disparities across Estonia. © 2023, The Author(s).

Spatial and Socioeconomic Analysis of Purposeful Mobile Internet Use in US States

The digital divide in the United States has received renewed attention during the COVID-19 pandemic. As achievement of digital equity remains a high priority, this study examines spatial patterns and socioeconomic determinants of the purposeful use of mobile internet for personal and business needs in US states. Agglomerations of mobile internet use are identified using K-means clustering and the extent of agglomeration is measured using spatial autocorrelation analysis. Regression analysis reveals that mobile internet use is associated with employment in management, business, science, and arts occupations, affordability, age structure, and the extent of freedom in US states. Spatial randomness of regression residuals shows the effectiveness of the conceptual model to account for spatial bias. Implications of these findings are discussed. © 2023 IEEE Computer Society. All rights reserved.

Impacts of COVID-19 on Wives of Nepali Migrants and Future Foreign Employment Decision-making

With over 55% of households having labour migrants and over 25% of the GDP attributable to migrants' remittance, migration plays an important role in economic development of Nepal but also in overall wellbeing of the Nepali households. While there have been considerable studies on the impact of migration both from social and economic perspectives, little is known about how migrants and their households make decisions to migrate. Moreover, there is limited research on how crisis in destination countries affect migration decision-making among migrants and their left-behind household members. Taking the example of the current COVID-19 crisis, this article discusses the context within which people are taking migration decisions and how the experiences of crisis affects decision-making about pursuing foreign employment for people who have previous migration experience. This article discusses the experience of migrants' wives during the pandemic in relation to their husband's migration, alternative livelihood experience of migrants (returnees, those on a holiday and aspiring migrants) in the home country, impacts of COVID-19 ban on aspiring migrants, and aspiring migrants and their wives' perspectives towards future foreign employment. The article argues that given a high interest amongst the returnees and their spouses to work in Nepal, current employment programmes brought forward by the government should take the opportunity as a way of retaining the human resources in Nepal.

Evaluation of Various Machine Learning Based Existing Stress Prediction Support Systems (SPSSs) for COVID-19 Pandemic

COVID-19 profoundly impacts human beings in various ways, i.e., psychological, socioeconomic, fear, social isolation, etc., augmenting the prevailing inequalities in mental health. The role of machine learning (ML) can be understood through its various potential applications in Stress Prediction in mental health. This literature survey uncovered various related articles, which were utilized to determine the essential structure for analysis. The gathered information helped in providing the new ideas and the concepts, which were incorporated with the support of literature and classified under broad themes based on mental health during the pandemic COVID-19. This study emphasized assessing various existing "Stress Prediction Support Systems” based on machine learning. This article also addresses the mental health issues that were emerged due to COVID-19 pandemic, further;also analysed the previously available stress prediction Machine Learning based models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Socioeconomic Impacts of Households' Vulnerability during COVID-19 Pandemic in South Africa: Application of Tobit and Probit Models

Coronavirus is a public health issue with socioeconomic and livelihood dimensions. The World Health Organization declared the current novel coronavirus disease (COVID-19) epidemic a public health emergency of international concern on January 30, 2020, and a global pandemic on March 11, 2020. The South African government has implemented different strategies, ranging from total lockdown in certain locations and provision of palliatives in some provinces across the country. This study, therefore, investigated the correlates of vulnerability and responsiveness to the adverse impacts of COVID-19 in South Africa. The study utilized primary data collected among 477 respondents. Descriptive statistical tools, Tobit and Probit regression models, were used to analyze the data. The study found different levels of vulnerability (low, medium, and high) and responsiveness among households, including stocking up of food items, remote working, reliance on palliatives, and social grant provision, among others. Some of the correlates of responsiveness to the COVID-19 pandemic include being employed, the type of community, and the income of respondents. The study, therefore, recommends increased investments in welfare programmes (safety nets, palliative measures and economic stimulus packages) as well as capacity building of households through education to reduce vulnerability. © Authors retain all copyrights.

Decoding COVID-19 Vaccine Hesitancy Using Multiple Regression Analysis with Socioeconomic Values

With the growth and development of COVID-19 and its variants, reaching a level of herd immunity is critically important for national security in public health. To deal with COVID-19, the United States has implemented phased plans to distribute COVID-19 vaccines. As of November 2022, over 80% of Americans had received their first shot to guard against COVID-19, and 68.6% were considered fully vaccinated, according to the dataset provided by CDC. However, a significant number of American people still hesitate to receive a shot of the COVID-19 vaccine. This paper aims to demystify COVID-19 vaccine hesitancy by analyzing various socioeconomic characteristics among individuals and communities, including unemployment rate, age groups, median household income, and education level. A multiple regression modeling and data visualization analysis show patterns with an increasing trend of vaccine hesitancy associated with a lower median household income, a younger age group, and a lower education level, which would help policymakers to make policies accordingly to target vaccine support information and remove this hurdle to end the COVID-19 pandemic effectively. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Comparison of the Impacts of the Financial Crisis and the Covid-19 Pandemic Crisis on Selected Financial Indicator of Construction Companies

In the past, there have been several major and minor economic crises in global society. The financial crisis in 2008 was one of the biggest economic crises since the Great Depression in 1928. The crisis was a direct result of the decline in liquidity in global financial markets that arose in the United States as a result of the collapse of the US housing market. The Covid-19 pandemic crisis stunned all aspects of society and saw dramatic effects on society's socio-economic spectrum. The paper analyzes the effects of selected crises on the profitability of sales. The research analyzed data from companies that belong to the TOP 100 construction companies operating in Slovakia and their activities began before 2008. The data used in the survey were obtained from the annual reports of selected companies and publicly available economic portals. The aim of the paper is to compare the profitability of sales and results of selected construction companies in three periods, namely during the financial crisis in 2008, in 2014, which can be specified as a transitional period, or the market stabilized after the financial crisis and the crisis caused by the Covid-19 pandemic. The survey will result in conclusions and future recommendations that will help eliminate the adverse effects of future crises on the activities of construction companies. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Open science progress: A literature assessment of open access articles

The open science movement has been widely adopted in multiple scientific fields across nations. Its benefit has been proven in many cases, most notably when the practice accelerated the search for solutions to the Covid-19 pandemic both in medical and socio-economic contexts. Still, the movement has faced multiple challenges, including an imbalance in the adoption of its numerous aspects. For example, the open access aspect which indicates the starting point of the movement has been widely practiced. Unfortunately, while open access is essential, an open access practice alone is not enough to pursue open science. In this work, we would like to assess the imbalance of the adoption, especially to measure how open access practice contributes to other practices, namely open data and open source as a sub-aspect of the open reproducibility research. Our assessment is based on descriptive statistic analysis of 300 open access articles from three domains, that is engineering, social and life science. Our findings indicated that the free and open source computer codes were dominantly adopted by the three scientific fields. However, social science has the lowest involvement in public data. © 2022 ACM.

A Novel Classification Technique for Safety Measures on Covid-19 Using Featured-Based Sentimental Analysis

Covid-19 is a term that has frightened the globe because it has broken beyond socioeconomic barriers in which people literally forgot the word social help because of this deadliest virus.The main goal of this study is to create a model that forecasts Covid-19 reviews based on coronavirus ratings from Kaggle repository. The World Health Organization(WHO) declared a pandemic of the coronavirus infection when it first appeared in 2019. People are worrying and concerned about their health as the number of instances rises throughout the world. People's physical and emotional health is inversely proportional to the pandemic scenario. As a result, in this case, a categorization model based on numerous metrics is required to rescue nations by analyzing facts and information about the outbreak. In this article to organise the reviews or opinions provided by people worldwide, we performed emotional or opinion classification using a Novel classifier. Then, the accuracy of the proposed model is compared with existing base classifiers like NB(Naive-Bayes) and Support Vector Machine(SVM), where Novel classifier gave the best accuracy compared to the other two classifiers, i.e., 95 © 2022 IEEE.

Sentiment Analysis of COVID-19 Tweets Using BiLSTM and CNN-BiLSTM

In a society where people express almost every thought they have on social media, analysing social media for sentiment has become very significant in order to understand what the masses are thinking. Especially microblogging website like twitter, where highly opinionated individuals come together to discuss ongoing socioeconomic and political events happening in their respective countries or happening around the world. For analysing such vast amounts of data generated every day, a model with high efficiency, i.e., less running time and high accuracy, is needed. Sentiment analysis has become extremely useful in this regard. A model trained on a dataset of tweets can help determine the general sentiment of people towards a particular topic. This paper proposes a bidirectional long short-term memory (BiLSTM) and a convolutional bidirectional long short-term memory (CNN-BiLSTM) to classify tweet sentiment;the tweets were divided into three categories—positive, neutral and negative. Specialized word embeddings such as Word2Vec or term frequency—inverse document frequency (tf-idf) were avoided. The aim of this paper is to analyse the performance of deep neural network (DNN) models where traditional classifiers like logistic regression and decision trees fail. The results show that the BiLSTM model can predict with an accuracy of 0.84, and the CNN-BiLSTM model can predict with an accuracy of 0.80. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Evaluating the Covid-19 Screening Regime for Cross-Border Workers

Global travel and trade have been hit hard by the COVID-19 pandemic. Border closures have impacted both leisure and business travel. The socioeconomic costs of border closure are particularly severe for individuals living and working across state lines, for which previously unhindered passage has been curtailed, and daily commute across borders is now virtually impossible. Here, we examine how the periodic screening of daily cross-border commuters across territories with relatively low COVID-19 incidence will impact the transmission of SARS-CoV-2 across borders using agent-based simulation. We find that periodic testing at practical frequencies of once every 7, 14 or 21 days would reduce the number of infected individuals crossing the border. The unique transmission characteristics of SARS-CoV-2 suggest that periodic testing of populations with low incidence is of limited use in reducing cross-border transmission and is not as cost-effective as other mitigation measures for preventing transmission. © 2022 IEEE.

Economic cascades, tipping points, and the costs of a business-as-usual approach to COVID-19

Decisions to shutdown economic activities to control the spread of COVID-19 early in the pandemic remain controversial, with negative impacts including high rates of unemployment. Here we present a counterfactual scenario for the state of California in which the economy remained open and active during the pandemic's first year. The exercise provides a baseline against which to compare actual levels of job losses. We developed an economic-epidemiological mathematical model to simulate outbreaks of COVID-19 in ten large Californian socio-economic areas. Results show that job losses are an unavoidable consequence of the pandemic, because even in an open economy, debilitating illness and death among workers drive economic downturns. Although job losses in the counterfactual scenario were predicted to be less than those actually experienced, the cost would have been the additional death or disablement of tens of thousands of workers. Furthermore, whereas an open economy would have favoured populous, services-oriented coastal areas in terms of employment, the opposite would have been true of smaller inland areas and those with relatively larger agricultural sectors. Thus, in addition to the greater cost in lives, the benefits of maintaining economic activity would have been unequally distributed, exacerbating other realized social inequities of the disease's impact. Copyright © 2023 Roopnarine, Abarca, Goodwin and Russack.

An agent-based model of COVID-19 pandemic and its variants using fuzzy subsets and real data applied in an island environment

In this paper, we present a model of the spread of the COVID-19 pandemic simulated by a multi-agent system (MAS) based on demographic data and medical knowledge. Demographic data are linked to the distribution of the population according to age and to an index of socioeconomic fragility with regard to the elderly. Medical knowledge are related to two risk factors: age and obesity. The contributions of this approach are as follows. Firstly, the two aggravating risk factors are introduced into the MAS using fuzzy sets. Secondly, the worsening of disease caused by these risk factors is modeled by fuzzy aggregation operators. The appearance of virus variants is also introduced into the simulation through a simplified modeling of their contagiousness. Using real data from inhabitants of an island in the Antilles (Guadeloupe, FWI), we model the rate of the population at risk which could be critical cases, if neither social distancing nor barrier gestures are respected by the entire population. The results show that hospital capacities are exceeded. The results show that hospital capacities are exceeded. The socioeconomic fragility index is used to assess mortality and also shows that the number of deaths can be significant. © The Author(s), 2023. Published by Cambridge University Press.

COVID-19 Pandemic Impacted Food Security and Caused Psychosocial Stress in Selected States of Nigeria.

The COVID-19 disease has infected many countries, causing generalized impacts on different income categories. We carried out a survey among households (n = 412) representing different income groups in Nigeria. We used validated food insecurity experience and socio-psychologic tools. Data obtained were analyzed using descriptive and inferential statistics. The earning capacities of the respondents ranged from 145 USD/month for low-income earners to 1945 USD/month for high-income earners. A total of 173 households (42%) ran out of food during the COVID-19 pandemic. All categories of households experienced increasing dependency on the general public and a perception of increasing insecurity, with the high-income earners experiencing the greatest shift. In addition, increasing levels of anger and irritation were experienced among all categories. Of the socio-demographic variables, only gender, educational level of the household head, work hours per day, and family income based on society class were associated (p < 0.05) with food security and hunger due to the COVID-19 pandemic. Although psychological stress was observed to be greater in the low-income earning group, household heads with medium and high family income were more likely to have satisfactory experiences regarding food security and hunger. It is recommended that socio-economic groups should be mapped and support systems should target each group to provide the needed support in terms of health, social, economic, and mental wellness.

Socio-Economic and Health Management of Pandemics Based on Forgotten Effects Theory

Intense and frequent changes increase uncertainty and complexity in decision-making. The COVID-19 pandemic exacerbates this situation. Therefore, the decision-maker seeks to reduce risks and meet these challenges. The manuscript aims to identify cause-effect relationships between variables affecting countries and changes caused by the COVID-19 pandemic and propose an algorithm to facilitate decision-making by identifying forgotten effects. The authors use thematic analysis to synthesize the semi-systematic literature review findings. The applied research uses a quantitative approach through modeling and simulation. The results highlight that the pandemic effects are associated with causes such as health care, political and economic stability, social justice, and the level of corruption. Decision-makers must prioritize the management of these variables guided by science. The main contribution is to show an algorithm that identifies forgotten effects in pandemics' socio-economic and health management, preventing future crises. In addition, the study advances the frontier of knowledge by addressing identified gaps and contributes to academia and policy makers. The most critical limitation is the number of variables included in this research. Future investigations could include analyses on the impact of climate change and sustainable development of nations and country-specific studies on the forgotten effects of the COVID-19 pandemic. © 2022 Taylor & Francis Group, LLC.