COVID-19 SEIAR Model with Sensitivity Analysis

Beside the unexpected toll of mortality and morbidity caused by COVID-19 worldwide, low- and middle-income countries are more suffering from the devastating issues on economic and social life. This disease has fostered mathematical modelling. In this paper, a SEIAR mathematical model is presented to illustrate how policymakers may apply efficient strategies to end or at least to control the devastating wide spread of COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

COVID-19 and the Indoor Environment - observations from a joint National Academies of Engineering seminar series

The SARS-CoV-2 virus and its variants and COVID-19 disease have affected every aspect of society. The US National Academy of Sciences has been providing scientific insights and advice to aid policymakers and researchers in their quest to respond to the pandemic. Since 2020, it has produced numerous reports and workshop proceedings intended to integrate science into national preparedness and response decision-making, to explore lessons learned and best practices from previous preparedness and response efforts, and to consider strategies for addressing misinformation (NASEM, 2021). Among these was a 2021 symposium series that analyzed engineering's role in catalyzing COVID-19 response, recovery, and resilience, examining topics including the mitigation of exposure in public transit systems, engineering solutions to managing pathogens indoors, and the factors influence the transmission of infectious diseases in cities. Speaker presentations addressing these indoor environment topics are summarized here. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

COVID-19 and the Motorcycle Taxi Sector in Sub-Saharan African Cities: A Key Stakeholders' Perspective

This article assesses the impact of the COVID-19 outbreak on the urban motorcycle taxi (MCT) sector in Sub-Saharan Africa (SSA). MCToperators in SSA provide essential transport services and have shown ingenuity and an ability to adapt and innovate when responding to different challenges, including health challenges. However, policymakers and regulators often remain somewhat hostile toward the sector. The article discusses the measures and restrictions put in place to reduce the spread of COVID-19 and key stakeholders' perspectives on these and on the sector's level of compliance. Primary data were collected in six SSA countries during the last quarter of 2020. Between 10 and 15 qualitative interviews with key stakeholders relevant to the urban MCT sector were conducted in each country. These interviews were conducted with stakeholders based in the capital city and a secondary city, to ensure a geographically broader understanding of the measures, restrictions, and perspectives. The impact of COVID-19 measures on the MCT and motor-tricycle taxi sector was significant and overwhelmingly negative. Lockdowns, restrictions on the maximum number of passengers allowed to be carried at once, and more generally, a COVID-19-induced reduction in demand, resulted in a drop in income for operators, according to the key stakeholders. However, some key stakeholders indicated an increase in MCT activity and income because of the motorcycles' ability to bypass police and army controls. In most study countries measures were formulated in a non-consultative manner. This, we argue, is symptomatic of governments' unwillingness to seriously engage with the sector. © National Academy of Sciences: Transportation Research Board 2021.

Data-driven Analysis of Remote Work in China during the COVID-19 Pandemic

This paper leverages online content to investigate the biggest impact of COVID-19 - remote work, by using China as a primary case study. Telecommuting has become popular since February 2020 primarily due to the pandemic, and people have been slowly returning to their office from May 2020. This study focuses on two time windows in the year 2020 to calculate the growth of different job sectors. Our results indicate the negative impact of teleworking in manufacturing industry, but shows that information technology-related industries are less affected by working from home. This paper also investigates the impact of COVID-19 on the stock market and discussed what plan of action the policy makers should take to provide a good economic environment for the country. In addition to the overall economic situation, we observed how the psychological situation of employees could affect their job performance, indirectly affecting the development of certain industry sectors. Therefore, misinformation in certain Chinese social media channels was also studied in this paper specifically examining the rumors and their latent topics. We believe that our work will initiate a dialogue between scientists, policy makers and government officials to consider the observations highlighted in this paper. © 2022 IEEE Computer Society. All rights reserved.

Moderating effect of government policy on the relationship between low-cost housing demand-supply gap and COVID-19 impact

Purpose: The COVID-19 impact across major sectors did not exempt the low-cost housing (LCH) sub-sector. This may have increased the existing LCH demand-supply gap, especially in developing countries such as Malaysia. Studies showed that government policy (GP) aids in mitigating COVID-19 impact on goods and services, including housing-related issues. However, there is an academic literature scarcity regarding GP on LCH demand-supply gap during the COVID-19 crisis in Malaysia. Hence, this study aims to investigate the moderating effect of GP on the relationship between LCH demand-supply gap and COVID-19 impact in Malaysia. Design/methodology/approach: The research utilised a quantitative method in collating the data from four major cities in Malaysia. SmartPLS was utilised to analyse the usable 305 questionnaires retrieved from respondents. Structuralist Theory supported the developed framework. Findings: Findings show that GP moderates the relationships between the LCH demand-supply gap and COVID-19 impact on Malaysia's low-income groups' (LIGs) homeownership delivery. It implies that the study's findings provide more understanding of issues influencing LCH demand-supply gap in the COVID-19 era via applying GP to mitigate the gap and improve homeownership for the disadvantaged. Practical implications: The study intends to stir policymakers toward formulating policies and programmes that will mitigate LCH demand-supply gap during the present and future pandemics. Originality/value: Besides the theoretical value of the developed model, policymakers can use the study's recommendations to mitigate future LCH demand-supply gaps during pandemics in developing countries using Malaysia as a case study. © 2023, Emerald Publishing Limited.

India's public transportation system: the repercussions of COVID-19

The COVID-19 pandemic has left scars on the Indian public transportation system. In order to regain its original momentum, policymakers will need to assess the barriers hindering the effectiveness of the public transportation sector. In this regard, this article analyzes the various factors affecting the public transportation sector in India and determines their interrelationships. The research is presented in three steps. First, we review the literature to identify the factors that affect the public transportation system in India. Next, we propose an integrated model of grey-DEMATEL and ANP, grey-DANP, to calculate the priority ranking and weight of the factors. The grey-DEMATEL method is used to find the interrelationships among the factors, while ANP determines the local and global weights of the factors to form a priority order. Then, we present the interrelationships in the form of influential relation maps. Furthermore, we provide a sensitivity analysis to enhance the credibility of our study. The paper reveals that governmental regulations are the most influential factors in India's public transportation system. The transportation authorities and policymakers must also focus on improving the financial stability and enhancing the customer's trust in the public transportation system. The framework provided in this paper can be applied to other countries where similar hindrances in the public transportation system have been caused by COVID-19. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Applying an Epidemiological Model to Evaluate the Propagation of Toxicity related to COVID-19 on Twitter

The prevalence of social media has increased the propagation of toxic behavior among users. Toxicity can have detrimental effects on users' emotion and insight and disrupt beneficial discourse. Evaluating the propagation of toxic content on social networks such as Twitter can provide the opportunity to understand the characteristics of this harmful phenomena. Identifying a mathematical model that can describe the propagation of toxic content on social networks is a valuable approach to this evaluation. In this paper, we utilized the SEIZ (Susceptible, Exposed, Infected, Skeptic) epidemiological model to find a mathematical model for the propagation of toxic content related to COVID-19 topics on Twitter. We collected Twitter data based on specific hashtags related to different COVID-19 topics such as covid, mask, vaccine, and lockdown. The findings demonstrate that the SEIZ model can properly model the propagation of toxicity on a social network with relatively low error. Determining an efficient mathematical model can increase the understanding of the dynamics of the propagation of toxicity on a social network such as Twitter. This understanding can help researchers and policymakers to develop methods to limit the propagation of toxic content on social networks. © 2022 IEEE Computer Society. All rights reserved.

Analysis of the relationship between bitcoin, oil prices, and the Dow Jones Industrial Average using a wavelet-based approach

This paper analyzes the correlation between bitcoin, oil price fluctuations and the DOW Jones Industrial Index in the time-frequency framework. Coherent wavelet method applied to recent daily data in the United States (1863 in total). Our research has several implications and supports for policy makers and asset managers. We find that oil prices lead the U.S. market at both low and high frequencies throughout the observation period. This result suggests that sanctions against Russia by a number of countries, including the U.S., are influencing oil prices, while oil remains a major source of systemic risk to the U.S. economy and economic uncertainty between the international level is exacerbated by tensions between Russia and Ukraine. © COPYRIGHT SPIE.

Research on Measurement and Evaluation of High-Quality Entrepreneurship: A Review and Synthesis Prospectus

How to measure and evaluate the quality of entrepreneurial activities is not only an important academic issue in the field of entrepreneurship research but also an important practical problem faced by economic policymakers, especially in the context of the global Covid-19 epidemic and the shift of China's economy from the entrepreneurship high-rate growth stage to the high-quality grow stage. In this paper, we explore the development process of defining and measuring the high-quality entrepreneurial activities, discuss and synthesize the various measurement index for identifying the high-quality entrepreneurship in a complex and uncertain context, concluding that measurement and evaluation of high-quality measurement index experiencing the process of single index to composite index with the consideration of impact of general entrepreneurship policy and specific environment, and also the measurement and evaluation more and more focused on antecedent of entrepreneurial activities which can effectively predict the high quality of entrepreneurial activities from the onset of new firms instead of consequence of entrepreneurial activities. At the end of the article, we propose three viewpoints: First, entrepreneurial quality can be measured using quantitative methods;second, there are limitations for the evaluation of high-quality entrepreneurial quality in practice;third, entrepreneurship indicators should be continuously updated with the accumulation of practice. © 2023 The authors and IOS Press.

The perspective of energy poverty and 1st energy crisis of green transition

The role of energy transition amidst the energy crisis and how policymakers can drive down emissions while focusing on energy security are critical. Given the geo-political situation, energy crisis volatility, energy shortage and climate change all affect the green transition and the short-term priorities for energy companies and policymakers. Energy security is not an isolated issue but has widespread implications as various sectors depend on energy supply to function properly. Governments around the world are faced with this trilemma, how to balance energy security with energy sustainability while also considering energy affordability. Sustainability has been in focus for about a decade. However, energy security is suddenly becoming one of the most important priorities that policymakers need to consider. Unfortunately, the renewable energy infrastructure is not yet ready to replace the growing volume of energy demand from hydrocarbon, which the world has been dependent on. This means, for now, a surge in energy generation through hydrocarbon to meet the existing energy demand deficit. However, it is important not to lose focus on the challenge of energy sustainability and climate change adaption and mitigation. Where trends like carbon capture and storage;solar, wind, hydro, green hydrogen, etc.;renewable energy infrastructure and integrations, with supply chain and engineering services consideration [in aspect for the growing market in this space] need better attention with regards to investment and full-scale implementation. This paper aims to analyze this 1st energy crisis of green transition with a priori on energy poverty with consideration of major influences and associated impacts. Furthermore, it proposes a specific framework for inclusive investigations, which considers the entire energy ecosystem with consideration of major influences, to enable the policymakers to better drive the green transition. This involves formulating energy policies that are not entirely conservative towards renewable energy sources but instead promote investments in both green and relatively more environmentally benign energy sources compared to high emission hydrocarbons. In this regard, this paper renders exhaustive prospects and recommendations. © 2023 Elsevier Ltd

Forecasting Emerging Pandemics with Transfer Learning and Location-aware News Analysis

Monitoring and forecasting epidemic diseases are of prime importance to public health organizations and policymakers in taking proper measures and adjusting prevention tactics. Early prediction is especially important to restrict the spread of emerging pandemics such as COVID-19. However, despite increasing research and development for various epidemics, several challenges remain unresolved. On the one hand, early-stage epidemic prediction for emerging new diseases is difficult because of data paucity and lack of experience. On the other hand, many existing studies ignore or fail to leverage the contribution of social factors such as news, geolocations, and climate. Even though some researchers have recognized the profound impact of social features, capturing the dynamic correlation between these features and pandemics requires an extensive understanding of heterogeneous formats of data and mechanisms. In this paper, we design TLSS, a neural transfer learning architecture for learning and transferring general characteristics of existing epidemic diseases to predict a new pandemic. We propose a new feature module to learn the impact of news sentiment and semantic information on epidemic transmission. We then combine this information with historical time-series features to forecast future infection cases in a dynamic propagation process. We compare the proposed model with several state-of-the-art statistics approaches and deep learning methods in epidemic prediction with different lead times of ground truth. We conducted extensive experiments on three stages of COVID-19 development in the United States. Our experiment demonstrates that our approach has strong predictive performance for COVID infection cases, especially with longer lead times. © 2022 IEEE.

STORM-GAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human Mobility Responses to COVID-19

Human mobility estimation is crucial during the COVID-19 pandemic due to its significant guidance for policymakers to make non-pharmaceutical interventions. While deep learning approaches outperform conventional estimation techniques on tasks with abundant training data, the continuously evolving pandemic poses a significant challenge to solving this problem due to data non-stationarity, limited observations, and complex social contexts. Prior works on mobility estimation either focus on a single city or lack the ability to model the spatio-temporal dependencies across cities and time periods. To address these issues, we make the first attempt to tackle the cross-city human mobility estimation problem through a deep meta-generative framework. We propose a Spatio-Temporal Meta-Generative Adversarial Network (STORM-GAN) model that estimates dynamic human mobility responses under a set of social and policy conditions related to COVID-19. Facilitated by a novel spatio-temporal task-based graph (STTG) embedding, STORM-GAN is capable of learning shared knowledge from a spatio-temporal distribution of estimation tasks and quickly adapting to new cities and time periods with limited training samples. The STTG embedding component is designed to capture the similarities among cities to mitigate cross-task heterogeneity. Experimental results on real-world data show that the proposed approach can greatly improve estimation performance and outperform baselines. © 2022 IEEE.

Detection of anomalies in cycling behavior with convolutional neural network and deep learning

Background: Cycling has always been considered a sustainable and healthy mode of transport. With the increasing concerns of greenhouse gases and pollution, policy makers are intended to support cycling as commuter mode of transport. Moreover, during Covid-19 period, cycling was further appreciated by citizens as an individual opportunity of mobility. Unfortunately, bicyclist safety has become a challenge with growing number of bicyclists in the 21st century. When compared to the traditional road safety network screening, availability of suitable data for bicycle based crashes is more difficult. In such framework, new technologies based smart cities may require new opportunities of data collection and analysis. Methods: This research presents bicycle data requirements and treatment to get suitable information by using GPS device. Mainly, this paper proposed a deep learning-based approach "BeST-DAD” to detect anomalies and spot dangerous points on map for bicyclist to avoid a critical safety event (CSE). BeST-DAD follows Convolutional Neural Network and Autoencoder (AE) for anomaly detection. Proposed model optimization is carried out by testing different data features and BeST-DAD parameter settings, while another comparison performance is carried out between BeST-DAD and Principal Component Analysis (PCA). Result: BeST-DAD over perform than traditional PCA statistical approaches for anomaly detection by achieving 77% of the F-score. When the trained model is tested with data from different users, 100% recall is recorded for individual user's trained models. Conclusion: The research results support the notion that proper GPS trajectory data and deep learning classification can be applied to identify anomalies in cycling behavior. © 2023, The Author(s).

The Impact of Public Activity Restriction during COVID-19 to Air Quality in Urban Area of Bandung Measured Using Mobile Monitoring

The COVID-19 outbreak impacted the people's lives in the world. Lockdown is one way of controlling the spread of the virus. In Indonesia, the government would rather implement public activity restriction than lockdown. The detailed comprehension of the effect of lockdown or similar policies on air pollution is valuable for making future policies about the control of pandemics as well as its effect on air quality. To understand the effect of public activity restriction (PAR) and its correlation with air pollution, mobile monitoring (MM) of particulate matter (PM2.5) was performed in the urban area of Bandung, Indonesia, in July 2021. Based on MM using a bicycle, we found that a PAR had an impact on air pollution. Our result showed that there was a decrease between 20% and 30% in 3 of 6 sub-districts. The advantage of MM was highlighted by the prominent visualization of the concentration of PM2.5 MM data at the level of the road. Localization of polluted roads could be seen clearly through the MM method. The uncovering effect of PAR on air pollution using the MM method will provide important insights for government and policymakers to develop future policy that controls air pollution for better citizen health. © 2023, AAGR Aerosol and Air Quality Research. All rights reserved.

Governance in mental healthcare policies during the COVID-19 pandemic in Mexico.

The COVID-19 pandemic has become the greatest burden of disease worldwide and in Mexico, affecting more vulnerable groups in society, such as people with mental disorders (MD). This research aims to analyze the governance processes in the formulation of healthcare policies for people with MD in the face of the COVID-19 pandemic. An analytical qualitative study, based on semi-structured interviews with key informants in the healthcare system was conducted in 2020. The study followed the theoretical-methodological principles of the Governance Analytical Framework (GAF). The software ATLAS.ti-V.9 was used for inductive thematic analysis, classifying themes and their categories. To ensure the proper interpretation of the data, a process of triangulation among the researchers was carried out. The findings revealed that in Mexico, the federal Secretary of Health issued guidelines for mental healthcare, but there is no defined national policy. Decision-making involved multiple actors, with different strategies and scopes, depending on the type of key-actor and their level of influence. Majority of informants described a problem of implementation in which infection control policies in the psychiatric population were the same as in the general populations which decreased the percentage of access to healthcare during the pandemic, without specific measures to address this vulnerable population. The results suggest that there is a lack of specific policies and measures to address the needs of people with mental disorders during the COVID-19 pandemic in Mexico. It also highlights the importance of considering the role of different actors and their level of influence in the decision-making process.

Integrated quantitative evaluation framework of sustainable development – The complex case of the Yangtze River Delta

Sustainable development is central to the current societal functioning, whose complexity demands consideration on a regional scale. However, there are disparate methods to express sustainable development, many of which use qualitative analysis cumbersome for policy-makers. Previous studies focused on environmental, economic, and social impacts without fully considering the regulation mechanisms of the plethora of administrative bodies. To fill this research gap, this research establishes an integrated assessment framework involving four pillars: environment and ecology, society and culture, economics, and governance and policy. Further, indicator systems and quantitative analysis give comparable and objective results. The current study applied them to one of the most economically significant and developed Chinese regions, the Yangtze River Delta. The result shows a dynamic variation in regional sustainability from 2010 to 2019, indicating an annual increase. Although economic and societal development has been increasing steadily, environmental development has stagnated in the past two years, and the influencing policy has fluctuated dramatically. Our analysis was done in Shanghai, Jiangsu, Zhejiang, and Anhui. Even though all regions showed increasing sustainability, we observed an imbalance in regional sustainable development. Achieving a regional approach and enhanced regional coordination in the Yangtze River Delta is imperative and cannot be ignored by local, regional, and national policy-makers. More importantly, this study created a model capable of predicting the impact of the COVID-19 epidemic on regional sustainable development. The model showed that, compared with predicted values, a 6.65% decrease in the integrated sustainability index ensued, attributed to the pandemic in Zhejiang province. © 2022 Elsevier Ltd

Connectedness among El Niño-Southern Oscillation, carbon emission allowance, crude oil and renewable energy stock markets: Time- and frequency-domain evidence based on TVP-VAR model

Understanding the interactions among climate change, carbon emission allowance trading, crude oil and renewable energy stock markets, especially the role of climate change in this system is of great significance for policy makers, energy producers/consumers and relevant investors. The present paper aims to quantify the time-varying connectedness effects among the four factors by using the TVP-VAR based extensions of both time- and frequency-domain connectedness index measurements proposed by Antonakakis et al. (2020) and Ellington and Barunik (2021) [8,48]. The empirical results suggest that, firstly, the average total connectedness among climate change, carbon emission allowance trading, crude oil and renewable energy stock markets is not so strong for the heterogenous fundamentals underlying them. Nevertheless, the time-varying total connectedness fluctuates fiercely through May 2005 to September 2021, varying from about 8% to 30% and rocket to very high levels during the global subprime mortgage crisis and the COVID-19 pandemic. Furthermore, the total connectedness mainly centers on the short-term frequency, i.e., 1–3 months. Secondly, climate change is generally the leading information contributor among the four factors, although not particularly strong, and its leading role also performs mainly on the short-term frequency (1–3 months). Thirdly, renewable energy stock market and crude oil market show tight interactions between them and they are the two major bridges of information exchanges across various time frequencies (horizons) in this system. Finally, we confirm the evidence that the primary net connectedness contributor and receiver switch frequently across different time frequencies, implying that it is extremely essential for policy makers, energy producers/consumers and investors to make time-horizon-specific regulatory, production/purchasing or investment decisions when facing the uncertain effects of climate change on the interactions among carbon emission allowance, crude oil and renewable energy stock markets. © 2022 Elsevier Ltd

Energy assistance programs in Ibero-America and their adaptation to the COVID-19 pandemic

Ibero-America, a region with high levels of pre-existing poverty, has been considerably affected by the pandemic. Several regulatory measures have been implemented to provide additional financial assistance to the population. Due to the significant fiscal expenditure involved in universal subsidies, several countries have decided to target resources to the most vulnerable sectors. However, the literature focused on these targeted subsidies and beneficiary selection mechanisms is scarce. This article presents a descriptive review of the targeted subsidies implemented in eight Ibero-American countries during the COVID-19 pandemic, the targeting mechanisms, and the modifications made to pre-existing subsidies to adapt them to the health crisis. The research was conducted with the support of regulators from the countries studied and demonstrates that the Ibero-American regulatory response is in line with measures implemented internationally. By showing a catalog of regulatory measures implemented during the COVID-19 pandemic, this article is relevant for policymakers to face future health crises and any scenario that forces the population to be confined in their homes, including extreme weather events. © 2023 The Author(s)

Cycling in an ‘ordinary city': A practice theory approach to supporting a modal shift

There has long been evidence of the benefit of a modal shift toward cycling can bring to meeting several pressing urban challenges including ill-health, climate change, and poor air quality. In the wake of COVID-19, policy-makers have identified a modal shift toward cycling as part of the solution to mobility challenges introduced by social distancing measures. However, beyond exemplar areas, cycling has been largely characterized by a stubbornly-low modal share. In this paper, we use the ‘ordinary city'–in cycling terms–of Liverpool as a case study to understand this. We apply practice theory in doing so, finding the provision of materials for cycling is the key factor in supporting a modal shift. Not only do they provide the means to support the practice of cycling in the city, but they also have a key role in shaping individuals perceptions of, and the skills required to cycle. We then reflect upon the utility of practice theory in understanding the patterns of everyday life, finding it was particularly well suited in understanding the interactions between different factors which influence modal choice. We go on to identify practical challenges in its application within our analysis raising questions around an inconsistent analysis of influential factors including ‘driver behavior' and ‘political commitment'. We suggest how this might be overcome, through the isolation of such factors within a category of ‘action of others', this we argue means the findings in this paper have broad relevance to researchers and policy-makers alike. © 2021 The Author(s). Published with license by Taylor and Francis Group, LLC.

Impact Analysis of Covid-19 Pandemic on Construction Employment in the United States

Construction industry is one of the major contributing sectors of the U.S. economy. Due to COVID-19 pandemic construction industry has witnessed halt and cancellation of ongoing and planned projects. As projects got halted and cancelled many construction companies furloughed or terminated employment contracts of their workers. This sudden termination has been reflected in the monthly employment numbers. This paper presents the employment change in three constituting subsectors of construction industry: building, heavy and civil, and specialty trade due to COVID-19 pandemic. The paper has utilized historical data from the U.S. Bureau of Labor Statistics to forecast the expected employment numbers in absence of the pandemic. It has been found that due to pandemic the construction employment went down by 5.5 million between March 2020 and December 2020. Additionally, it has been found that the variation of the extent of impact of the COVID-19 pandemic in terms of employment on the three subsectors is insignificant. This means that the three subsectors suffered the consequences equally. The outcomes of the paper can be utilized by the policy makers in exploring the broader implications of the construction employment change. It can also be used in subsector specific policy planning purpose. © 2023, Canadian Society for Civil Engineering.