Search

Doubly censored data are very common in epidemiology studies. Ignoring censorship in the analysis may lead to biased parameter estimation. In this paper, we highlight that the publicly available COVID19 data may involve high percentage of double-censoring and point out the importance of dealing with such missing information in order to achieve better forecasting results. Existing statistical methods for doubly censored data may suffer from the convergence problems of the EM algorithms or may not be good enough for small sample sizes. This paper develops a new empirical likelihood method to analyze the recovery rate of COVID19 based on a doubly censored dataset. The efficient influence function of the parameter of interest is used to define the empirical likelihood (EL) ratio. We prove that − 2 log (EL-ratio) asymptotically follows a standard χ 2 distribution. This new method does not require any scale parameter adjustment for the log-likelihood ratio and thus does not suffer from the convergence problems involved in traditional EM-type algorithms. Finite sample simulation results show that this method provides much less biased estimate than existing methods, when censoring percentage is large. The application to COVID19 data will help researchers in other field to achieve better estimates and forecasting results. • A new empirical likelihood method to analyze doubly censored data. • To use efficient influence function of the parameter of interest to make the efficient empirical likelihood inference. • To apply the efficient empirical likelihood method to analyze the recovery rate of COVID19 based on doubly censored dataset. [ABSTRACT FROM AUTHOR]

• While emerging diseases might be surprising and unpredictable; some could be foreseen. • Emerging diseases were the reason behind papers published by the WHO, and others, addressing likely pathogens causing future outbreaks. • Such announcements, as for coronaviruses, may be viewed as "warnings" and appeared since the SARS outbreak in the winter of 2002–2003. • Taking these warnings into consideration will doubtlessly aid in analyzing reports regarding possible future outbreaks as well as building various strategies for prevention and coping with such epidemics. Infectious diseases are known to act in both predictable and unpredictable ways, which leads to the notions of emerging and reemerging infectious diseases. Emerging diseases with their disastrous consequences might be surprising and unpredictable, but they could be foreseen. For instance, some emerging diseases and recently the coronavirus disease 2019 (COVID-19) were the reason for papers published by the World Health Organization (WHO) and other researchers addressing the likely pathogens causing future outbreaks, according to the reports of the WHO in 2016 and 2018. Although it might seem like a wisdom in retrospect, several studies had already indicated possible future outbreaks caused by coronaviruses. Announcements, which may be viewed as "warnings," appeared since the emergence of the first coronavirus-related outbreak caused by severe acute respiratory syndrome coronavirus (SARS-CoV) in the winter of 2002–2003 and a later outbreak caused by the Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012–2013. Therefore, we were curious to review the medical literature prior to the COVID-19 pandemic with an aim to enumerate and evaluate studies addressing and warning against future outbreaks, and surprisingly pandemics, of members of coronaviruses. Interestingly, we found numerous studies that correctly predicted the current pandemic of COVID-19. While this part is highly interesting, how authorities reacted and prepared for warnings, if any, and how will they get prepared for the next warnings are our main messages. Taking these points into serious consideration will certainly aid in analyzing reports regarding possible future outbreaks as well as in developing various strategies for prevention and coping with such epidemics. [ABSTRACT FROM AUTHOR]

While many countries have experienced more than one wave of the pandemic throughout 2020, Australia has been able to contain the virus in a way that makes it a stand out (with New Zealand) in the way that it has been contained, with an exception in Victoria linked to failed quarantine procedures for travellers returning from overseas. Through descriptive analysis, this paper builds on earlier papers by the authors on the Australian response, with a focus on the changing dynamics of travel activity, concern with public transport, and attitudes surrounding activity given the perception of risk of COVID-19 and the level of public support for regulatory intervention and restrictions on movement. We find that Australia continues to suppress travel, particularly that for commuting, that comfort in completing day-to-day activities continues to rise (with the exception of Victoria where confidence feel significantly), and while support for intervention measures remains high, there has been an erosion in sentiment. As with previous work, we discuss what this might mean for future transport policy, and attempt to draw lessons from the Australian experience. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper (Part 2 in the paper series), building on earlier studies examining the Australian response, extends on findings related to travel activity, commuting, and attitudes towards COVID-19 measures (Part 1 in the paper series). In this paper we focus in detail on the impact of, and experiences with, working from home (WFH), perhaps the largest of the positive unintended consequence of the pandemic, with respect to transport, and a key lens through which the changing patterns in travel activity and attitudes discussed in Part 1 need to be understood. We conclude that through the widespread adoption of WFH as a result of nationwide public health orders, there is evidence emerging that WFH is now seen as an appealing instrument of change by employees and employers, there is growing support to continue to support WFH into the future. This represents a significant potential contribution to the future management of the transport network, especially in larger metropolitan areas. We also discuss policy implications of this result and what the international community may take from the Australian experience. [ABSTRACT FROM AUTHOR]

• A novel mentoring scheme was adopted in undergraduate forensic and policing curricula. • Mentors were senior (second to fourth year) undergraduate students. • Actively mentoring others significantly developed self-confidence and self-efficacy. • A framework is proposed to link mentor and employer skills with graduate attributes. • Multiple initiatives are needed to maximise mentors' employability potential. This paper considers whether the adoption of a subject-specific, classroom-based, voluntary extra-curricular student mentoring scheme could provide an effective mechanism and andragogic approach to enhance higher education students' employability potential pre-graduation. Over the three-year pilot, 26 more advanced (second to fourth year) undergraduate students actively mentored nearly 400 first year undergraduate students during workshops delivered annually within forensic and policing focused courses. In total, 17 mentors anonymously completed online, post-scheme surveys. Survey data was quantitatively analysed to evaluate the scheme, establish which skills and attributes mentors had developed and investigate whether mentors could appropriately identify example skills within professional terminology used during employer recruitment. In addition, this paper reflects on the implementation of remote student mentoring during the COVID-19 pandemic and its adoption within a blended learning framework. The results from this research strongly support mentoring as an effective mechanism to develop undergraduate employability skills, significantly developing mentors' self-confidence and self-efficacy in their interpersonal and communication skills. Although mentors were aware of university graduate attributes and thought they could evidence these with appropriate examples, in practice this was not necessarily the case. As a result, a framework is proposed to enable mentors to identify their skills and how they may align with competencies sought by relevant forensic and policing employers. However, other andragogic practices may need to be implemented to maximise the potential for successful graduate employment. [ABSTRACT FROM AUTHOR]

This study analyzed the Coronavirus (COVID-19) crisis from the angle of cyber-crime, highlighting the wide spectrum of cyberattacks that occurred around the world. The modus operandi of cyberattack campaigns was revealed by analyzing and considering cyberattacks in the context of major world events. Following what appeared to be substantial gaps between the initial breakout of the virus and the first COVID-19-related cyber-attack, the investigation indicates how attacks became significantly more frequent over time, to the point where three or four different cyber-attacks were reported on certain days. This study contributes in the direction of fifteen types of cyber-attacks which were identified as the most common pattern and its ensuing devastating events during the global COVID-19 crisis. The paper is unique because it covered the main types of cyber-attacks that most organizations are currently facing and how to address them. An intense look into the recent advances that cybercriminals leverage, the dynamism, calculated measures to tackle it, and never-explored perspectives are some of the integral parts which make this review different from other present reviewed papers on the COVID-19 pandemic. A qualitative methodology was used to provide a robust response to the objective used for the study. Using a multi-criteria decision-making problem-solving technique, many facets of cybersecurity that have been affected during the pandemic were then quantitatively ranked in ascending order of severity. The data was generated between March 2020 and December 2021, from a global survey through online contact and responses, especially from different organizations and business executives. The result show differences in cyber-attack techniques; as hacking attacks was the most frequent with a record of 330 out of 895 attacks, accounting for 37%. Next was Spam emails attack with 13%; emails with 13%; followed by malicious domains with 9%. Mobile apps followed with 8%, Phishing was 7%, Malware 7%, Browsing apps with 6%, DDoS has 6%, Website apps with 6%, and MSMM with 6%. BEC frequency was 4%, Ransomware with 2%, Botnet scored 2% and APT recorded 1%. The study recommends that it will continue to be necessary for governments and organizations to be resilient and innovative in cybersecurity decisions to overcome the current and future effects of the pandemic or similar crisis, which could be long-lasting. Hence, this study's findings will guide the creation, development, and implementation of more secure systems to safeguard people from cyber-attacks. [ABSTRACT FROM AUTHOR]

The impact of COVID-19 on the Global scale is tremendously drastic. There are several types of research going on across the world simultaneously to understand and overcome this dire pandemic outbreak. This paper is purely a statistical study on a distinct set of datasets regarding COVID-19 in India. The motivation of this study is to provide an insight into the rapid growth of confirmed COVID-19 cases in India. The rapid growth of COVID-19 cases in India started in March 2020. The main objective of this paper is to provide a solid statistical model for the policymaker to handle this kind of pandemic situation in the near future with nonlinear data. In this paper, the data was got from 1st April to 29th November 2020. To come up with a solid statistical model, various nonlinear data such as confirmed COVID-19 cases, maximum temperature, minimum temperature, the total population (state-wise), the total area in km2 (state-wise), and the total rural and urban population count (state-wise) have been analyzed. In this paper, six different Generalized Additive Models (GAM) was identified after a thorough analysis of other researchers' (Xie and Zhu, 2020; Prata et al., 2020) findings. In all perspectives, the results were identified and analyzed. The GAM model regarding total COVID-19 confirmed cases, total population, and the total rural population provides the best average fit of R2 value of 0.934. As the population value is quite high, the author has concise it using logarithm to provide the best p -value of 0.000542 and 0.001407 for a relation between the total number of COVID-19 cases regarding the total population and total rural population respectively. [ABSTRACT FROM AUTHOR]

This paper has sorted out the general logic of the impact of COVID-19 on energy consumption. In the short term, the epidemic has forced governments to adopt different levels of lockdown measures. The total electricity consumption decreased and the share of renewable energy has increased. Based on the International Energy Agency's (IEA) data on electricity production and consumption, this paper analyzes the long-term effects: The COVID-19 pandemic does affect overall electricity demand and market structure in the short term, but in the long term, the impact of the pandemic is detrimental to the proportion of electricity generated from renewable sources. Increased, especially in hard-hit countries. The impact on countries with different levels of development is different, and countries with higher levels of economic development are better able to resist such exogenous shocks. The pandemic is still ongoing, how to steadily promote sustainable development? In this paper we suggest that to cope with climate change, to accomplish energy transformation and upgrading or even sustainable development, we should not only rely on reducing human activities, but give more support to countries with low development levels in realizing sustainable development. [ABSTRACT FROM AUTHOR]

This paper proposes a partially periodic oscillation model, which is motivated by time series modelling for COVID-19 daily confirmed cases, in particular, to represent more accurately the dynamic features of the 7-day periodicity. In order to express the phenomenon of the partial 7-day cycle in the COVID-19 data, some partial periodic part is added to a heterogeneous autoregression model. Estimation algorithm based on the least squares errors and regression analysis is provided and parameter estimation consistency is given along with its proof. A Monte-Carlo simulation study is carried out to investigate the finite-sample performance. The proposed model is applied to the COVID-19 daily confirmed cases of the most affected eight countries which posses the partially periodic oscillation. Model criteria such as RMSE, MAE, HMAE, AIC and BIC are compared with other existing models. Efficiency of the model, relative to the benchmark, is evaluated to reveal its better accuracy performance. Out-of-sample forecasting analysis is conducted as well. The novelty is that this work is a challenging trial to identify the partially periodic oscillation of COVID-19 data, without smoothing, as well as the proposed model outperforms the existing time series models in the empirical analysis of the worldwide COVID-19. • A partially periodic oscillation model combined with heterogeneous autoregression is proposed. • The model is applied to the COVID-19 daily confirmed cases in the top eight countries with partially periodic oscillation. • A simple and robust estimation method is developed. • Model criteria measures such as RMSE, MAE, HMAE, AIC and BIC are evaluated and compared with existing models. • Efficiency of the proposed model, relative to benchmark models, is reported as 126%-163% improvement in some countries. [ABSTRACT FROM AUTHOR]

• Significant alteration in the human microbiota has been observed among COVID-19 patients in various studies. • These alterations are mostly characterized by increase in opportunistic and a decrease in beneficial microbes. • Human microbiome is a potential biomarker in forensic pathological investigations. • Alteration in microbiome among COVID-19 patients could pose several challenges in forensic pathological investigations. The human microbiome is vital for maintaining human health and has garnered substantial attention in recent years, particularly in the context of the coronavirus disease 2019 (COVID-19) outbreak. Studies have underscored significant alterations in the microbiome of COVID-19 patients across various body niches, including the gut, respiratory tract, oral cavity, skin, and vagina. These changes manifest as shifts in microbiota composition, characterized by an increase in opportunistic pathogens and a decrease in beneficial commensal bacteria. Such microbiome transformations may play a pivotal role in influencing the course and severity of COVID-19, potentially contributing to the inflammatory response. This ongoing relationship between COVID-19 and the human microbiome serves as a compelling subject of research, underscoring the necessity for further investigations into the underlying mechanisms and their implications for patient health. Additionally, these alterations in the microbiome may have significant ramifications for forensic investigations, given the microbiome's potential in establishing individual characteristics. Consequently, changes in the microbiome could introduce a level of complexity into forensic determinations. As research progresses, a more profound understanding of the human microbiome within the context of COVID-19 may offer valuable insights into disease prevention, treatment strategies, and its potential applications in forensic science. Consequently, this paper aims to provide an overarching review of microbiome alterations due to COVID-19 and the associated impact on forensic applications, bridging the gap between the altered microbiome of COVID-19 patients and the challenges forensic investigations may encounter when analyzing this microbiome as a forensic biomarker. [ABSTRACT FROM AUTHOR]

Heart transplantation (HTx) is the only definitive therapy for patients with end stage heart disease. With the increasing global prevalence of heart failure, the demand for HTx has continued to grow and outpace supply. In this paper, we will review advances in the field of HTx along the clinical journey of a HTx recipient. Starting with the sensitized patient, we discuss current methods to define sensitization, and assays to help identify clinically relevant anti-HLA antibodies. Desensitization strategies targeting all levels of the adaptive immune system are discussed with emphasis on novel techniques such as anti-CD 38 blockade and use of the Immunoglobulin G-Degrading Enzyme of Streptococcus Pyogenes. We next discuss donor procurement and the resurgence of donation after circulatory death as a viable strategy to significantly and safely increase the donor pool. Post-transplant, we evaluate non-invasive surveillance techniques including gene expression profiling and donor-derived cell-free DNA. Last, we discuss the ground-breaking developments in the field of xenotransplantation. [ABSTRACT FROM AUTHOR]

The SARS-CoV-2 virus pandemic has left a massive global death toll in its wake. Associated restrictions, precautions and lockdowns have disrupted daily routines, which has been associated with social isolation and major health implications for the world's youth. This paper shares young adults' visions for life beyond the pandemic as it relates to the prevention and management of noncommunicable diseases (NCDs). NCD Child hosted a global Twitter campaign for young adults, some of whom are living with NCDs, to express their reflections on life beyond the pandemic. Contributions were subjected to qualitative thematic analysis. 52 responses from the campaign described six main themes: Health system strengthening; Access to care; Issues of sustainability, including the environment and the economy; Human rights, equity, and social issues; Mental health, and NCD prevention. Young adults expressed optimism about postpandemic life and emphasized the importance of comprehensive intersectoral approaches to create resilient health systems. [ABSTRACT FROM AUTHOR]

This research paper investigates the correlation between the extent of digitalization and business recovery in microenterprises in Colombia one year after the start of the 2019 Coronavirus (COVID-19). The study involved the analysis of 1.600 surveys conducted across various sectors of microenterprises. The findings revealed that, on average, businesses that had embraced a higher degree of digitization demonstrated a more significant rebound in their sales following the constraints imposed by the pandemic. [ABSTRACT FROM AUTHOR]

• An improved social force model considering the influence of COVID-19 is proposed. • Appropriate desired speed can lead to a win‒win situation between safety and efficiency. • The increase in social distance under high desired speed can slightly promotes evacuations. • The presence of epidemic-sensitive pedestrians effectively inhibits "faster-is-slower effect". Collective behavior has been frequently studied in previous works. However, during pandemics, the method in which system dynamics control the collective behavior of pedestrians has rarely been studied. To further understand this issue, an improved social force model is proposed in this paper to explore the potential constraints caused by pandemics during pedestrian evacuations. During COVID-19, Regulatory authorities usually require pedestrians to keep a certain social distance from each other to avoid virus infection. Our model can characterize the feature that pedestrians are sensitive to surrounding pedestrians within this prescriptive social distance. Based on the simulated results in an ordinary room, the effects of prescriptive social distance, behavioral response times and other potential factors on evacuation efficiency are compared to reflect the changes caused by COVID-19. The main findings are as follows. (1) Trying to maintain a social distance of 1.5 m will not delay evacuations but instead promote overall process of evacuations. Under the appropriate desired speed, a win‒win situation between epidemic safety and evacuation efficiency can be achieved. (2) The increase in prescriptive social distance under a high desired speed will slightly promote evacuations. When the desired speed is too low, however, it may substantially reduce evacuation efficiency. (3) The response time reflects the epidemic sensitivity of pedestrians. For pedestrians in congestion, a high response time value indicates a longer social distance adjustment, which will have a certain inhibitory effect on evacuation efficiency. (4) The presence of epidemic-sensitive pedestrians can effectively inhibit the "faster-is-slower effect". This work will help managers formulate relevant evacuation plans by taking advantage of the balance between personal purpose of fast evacuation and the public regulations, thereby inhibiting the spread of COVID-19 without lowing evacuation efficiency. [ABSTRACT FROM AUTHOR]

This paper takes advantage of the COVID-19 outbreak to explore the determinants of firms' R&D choices around an exogenous shock. We make use of unique panel data on 7,800 Italian companies between January –right before the pandemic– and March 2020 –amid lockdown policies. We exploit the short-run revision in firms' research plans to test the impact of deteriorating expectations and uncertainty on firms' R&D. In addition to the significant role played by firms' expectations, we find that preexisting innovative activities significantly shaped the reaction to the general uncertainty shock. • We explore the determinants of firms R&D choices around an exogenous shock. • We test the differential effect of deteriorating expectations and uncertainty on firms' R&D. • Expectations on future demand have a dramatic effect, especially for innovative companies. • Characteristics of the innovative process shape firms' reaction to the general uncertainty. • Newly innovative and transitioning companies are particularly affected. [ABSTRACT FROM AUTHOR]

This paper aims to examine the impact of regulatory policies on cryptocurrency market volatility in the context of the COVID-19 pandemic. We find that price volatility, liquidity volatility, and return volatility in the cryptocurrency market increased significantly following regulatory announcements by Chinese policymakers targeting the cryptocurrency industry, and this effect was more pronounced during the COVID-19 pandemic. It indicates that investors are paying a great deal of attention to such events. Yet further findings suggest that as the risk of pandemic transmission increases and stock market returns decline, investor response to regulation decreases. Our findings also suggest that while regulation typically brings market volatility in cryptocurrencies, the release of regulatory policies can instead be beneficial to the stability of the cryptocurrency market when investors are in an highly greedy mood. • Regulatory policies cause market volatility in cryptocurrencies. • COVID-19 transmission risk affects investors' negative reaction to regulation. • Cryptocurrencies are potential safe havens during COVID-19. • Regulatory policies reduce cryptocurrency investor greed. [ABSTRACT FROM AUTHOR]

While Canada has had relatively high vaccination rates against COVID-19, specifically during earlier waves of the pandemic, vaccine hesitancy has continued to serve as a significant barrier to adequate protection against the virus and, more recently, booster vaccine uptake. This paper explores the processes underlying Canadians' perceptions of COVID-19 vaccines and their decisions to take or refuse them, as well as how public policy and health messaging about vaccination has influenced vaccination attitudes and behaviors. Our focus group interviews with 18 vaccinated and unvaccinated adult Canadians conducted during October 2021 reveal that, in some respects, COVID-19 vaccine hesitancy conforms to prior knowledge about some of the factors that affect vaccine attitudes (e.g., the influence of known medical providers) but deviates from current theoretical frameworks regarding general vaccine hesitancy. Specifically, these frameworks emphasize a lack of scientific knowledge and literacy ("knowledge deficit" accounts) or individuals' inability to incorporate rational risk perceptions into initial emotional responses to vaccines ("emotionality/irrationality" accounts). In contrast to the knowledge deficit account, we find that expressions of COVID-19 vaccine hesitancy were most frequently associated with an information surplus or inability to prioritize information from multiple and often contradictory sources. Furthermore, top-down pro-vaccination messaging often triggered significant pushback against what participants perceived as moral shaming of the unvaccinated. Our findings demonstrate the necessity for a new framework to understand and address vaccine hesitancy. A better theoretical account of vaccine hesitancy has important implications for future vaccination efforts, specifically within the context of new variants and low booster vaccination rates in Canada. [ABSTRACT FROM AUTHOR]

Covid-19 has triggered a massive global challenge with 1,576,069 overall confirmed cases, 1,545,429 recovered cases, and 30,640 deaths across Pakistan. Epidemiological models have always played an important role in understanding the dynamics of such infectious diseases. The severity of Covid-19 infection depends on several factors such as immune system and virus variants. Although detailed epidemiological models which consider these factors are generally more accurate, their complexity limits their application due to the unavailability of comprehensive historical data. Therefore, in this paper, we proposed a Susceptible-Non-severely Infected-Severely Infected-Recovered-Deceased model, for the prediction of Covid-19 infections. The proposed model broadly classifies Covid-19 infections into non-severe and severe infections. This classification is based on the overall effect of virus variants and individual immune systems. The important epidemiological proprieties of the model such as equilibrium points, stabilities and bifurcation analysis are discussed. The model is fitted to the Covid-19 data of Pakistan to predict the non-severe and severe infections over the next four months. The comparison of predicted and actual infections shows the forecasting efficiency of the model. Moreover, the prediction of a severely-infected population in the upcoming months can be of significant help to policy-makers in the efficient management of hospitalization services. [ABSTRACT FROM AUTHOR]

In Europe, flu vaccination coverage has decreased, and there are complex barriers to overcome to vaccinate against flu. Many studies have been conducted to estimate vaccination coverage. The COVID-19 pandemic threatens to disrupt immunization programs in many countries, including Italy, where vaccination against the flu is recommended but not mandatory. This paper aims to understand changes in flu vaccine uptake in Italian regions. Using functional data analysis and fuzzy functional k-means clustering, we investigated changes in flu vaccine coverage in Italian regions before (2010–2019) and after (2020–2022) the COVID-19 vaccination period. The period of COVID-19 pandemic brought an increase in vaccine coverage globally. Elbow's method determined that the optimal number of clusters in vaccination uptake is 2. Apulia, Basilicata, Emilia Romagna, Liguria, Molise, Tuscany, and Umbria in 2019 belong less to the group with low flu vaccination uptake (G1) but increase their tendency to belong to this group over time: they decrease their propensity to be vaccinated for flu. For others, it seems that COVID-19 served as a push to increase flu vaccination coverage rates. Sicily appears to be the region that has responded best to the pandemic, changing its membership value from 2019 to 2022. The present study highlights that the COVID-19 era has resulted in a higher flu vaccination coverage rate. Moreover, the regional level's improvement or worsening in flu vaccination coverage rate is not affected by the historical gap and socio-cultural and economic differences prevailing among Italian regions. [ABSTRACT FROM AUTHOR]

• The paper fills the gap in the literature by reviewing and analyzing the current studies that apply deep learning for Covid-19 forecasting. • The initial search identified 152 studies of which 53 passed the quality control. • The existing literature is categorized using a model-based taxonomy. • The description of the models along with their performance evaluation is presented. • Recommendations for future improvements are provided. The Covid-19 pandemic has galvanized scientists to apply machine learning methods to help combat the crisis. Despite the significant amount of research there exists no comprehensive survey devoted specifically to examining deep learning methods for Covid-19 forecasting. In this paper, we fill the gap in the literature by reviewing and analyzing the current studies that use deep learning for Covid-19 forecasting. In our review, all published papers and preprints, discoverable through Google Scholar, for the period from Apr 1, 2020 to Feb 20, 2022 which describe deep learning approaches to forecasting Covid-19 were considered. Our search identified 152 studies, of which 53 passed the initial quality screening and were included in our survey. We propose a model-based taxonomy to categorize the literature. We describe each model and highlight its performance. Finally, the deficiencies of the existing approaches are identified and the necessary improvements for future research are elucidated. The study provides a gateway for researchers who are interested in forecasting Covid-19 using deep learning. [ABSTRACT FROM AUTHOR]

During the year 2020, the COVID-19 pandemic affected mobility around the world, significantly reducing the number of trips by public transport. In this paper, we study its impact in five South American capitals (i.e., Bogotá, Buenos Aires, Lima, Quito and Santiago). A decline in public transport patronage could be very bad news for these cities in the long term, particularly if users change to less sustainable modes, such as cars or motorbikes. Notwithstanding, it could be even beneficial if users selected more sustainable modes, such as active transport (e.g., bicycles and walking). To better understand this phenomenon in the short term, we conducted surveys in these five cities looking for the main explanation for changes from public transport to active and private modes in terms of user perceptions, activity patterns and sociodemographic information. To forecast people's mode shifts in each city, we integrated both objective and subjective information collected in this study using a SEM-MIMIC model. We found five latent variables (i.e., COVID-19 impact, Entities response , Health risk , Life related activities comfort and Subjective well-being), two COVID-19 related attributes (i.e. , new cases and deaths), two trip attributes (i.e., cost savings and time), and six socio-demographic attributes (i.e. , age, civil status, household characteristics, income level , occupation and gender) influencing the shift from public transport to other modes. Furthermore, both the number of cases and the number of deaths caused by COVID-19 increased the probability of moving from public transport to other modes but, in general, we found a smaller probability of moving to active modes than to private modes. The paper proposes a novel way for understanding geographical and contextual similarities in the pandemic scenario for these metropolises from a transportation perspective. [ABSTRACT FROM AUTHOR]

In recent years, Artificial Intelligence has had an evident impact on the way research addresses challenges in different domains. It has proven to be a huge asset, especially in the medical field, allowing for time-efficient and reliable solutions. This research aims to spotlight the impact of deep learning and machine learning models in the detection of COVID-19 from medical images. This is achieved by conducting a review of the state-of-the-art approaches proposed by the recent works in this field. The main focus of this study is the recent developments of classification and segmentation approaches to image-based COVID-19 detection. The study reviews 140 research papers published in different academic research databases. These papers have been screened and filtered based on specified criteria, to acquire insights prudent to image-based COVID-19 detection. The methods discussed in this review include different types of imaging modality, predominantly X-rays and CT scans. These modalities are used for classification and segmentation tasks as well. This review seeks to categorize and discuss the different deep learning and machine learning architectures employed for these tasks, based on the imaging modality utilized. It also hints at other possible deep learning and machine learning architectures that can be proposed for better results towards COVID-19 detection. Along with that, a detailed overview of the emerging trends and breakthroughs in Artificial Intelligence-based COVID-19 detection has been discussed as well. This work concludes by stipulating the technical and non-technical challenges faced by researchers and illustrates the advantages of image-based COVID-19 detection with Artificial Intelligence techniques. [ABSTRACT FROM AUTHOR]

With the rapid evolution and technology advancement, the healthcare sector is evolving day by day. It is taking advantage of different technologies such as Internet of things and Blockchain. Several applications related to daily healthcare activities are adopting the use of these technologies. In this paper, we present a review in which we group different healthcare applications that integrate the Internet of things and Blockchain in their systems. A review study about the integration of IoT and Blockchain in healthcare systems was conducted. We searched the databases ScienceDirect, IEEE Xplore, Google Scholar and ACM Digital Library. This review focuses on categorizing the use cases of IoT and Blockchain in the healthcare sector. The study listed 6 applications in medical services, namely, remote patient monitoring, electronic medical records management, disease prediction, patient tracking, drug traceability and fighting infectious disease especially COVID-19. The paper also investigates the challenges associated with the adoption of the Blockchain technology in healthcare IoT-based systems and some of the existing solutions. It also introduces some future research directions. The survey of the use cases of IoT and Blockchain in the healthcare sector will serve as a state of the art for future researches. In addition, the paper gives some directions to new possible researches that could help to revolutionize the healthcare sector by using other technologies such as artificial intelligence, big data, fog and cloud computing. [ABSTRACT FROM AUTHOR]

This paper uses Vietnam – where overexploitation of wildlife resources is a major threat to biodiversity conservation – as a case study to examine how government officials perceive the impacts of COVID-19 on wildlife farming, as well as the opportunities and challenges presented for sustainable wildlife management. Findings show Vietnamese government officials perceive COVID-19 to have had mixed impacts on wildlife conservation policies and practice. While the pandemic strengthened the legal framework on wildlife conservation, implementation and outcomes have been poor, as existing policies are unclear, contradictory, and poorly enforced. Our paper also shows policymakers in Vietnam are not in favor of banning wildlife trade. As our paper documents the immediate impacts of the pandemic on wildlife farming, more research is necessary to analyse longer-term impacts. • This paper to examine how Vietnamese government officials perceive the impacts of COVID-19 on wildlife farming. • Policy makers' perceptions and reality are not matched. • Data around both wildlife farming and wildlife trade is contradictory and differs across provinces. • Existing wildlife conservation policies are unclear, contradictory, and poorly enforced. • Policymakers in Vietnam are not in favor of banning wildlife trade. [ABSTRACT FROM AUTHOR]

After the widespread impact of the COVID-19 pandemic, all public transport, including urban rail transit, inevitably adopted a vigorous physical-distancing policy to prevent the disease from spreading among passengers. Adoption of this measure resulted in a substantial reduction in train service capability and required control of the risk contact exposure duration. Thus, this paper proposes the Skip-Stop Strategy Patterns (3S–P) decision-support model to incorporate social distancing constraints in train operations. The 3S–P model is a two-stage, multi-objective optimization model for scheduling train skip-stop patterns to satisfy the study's two main objectives of minimizing the average passenger travel time and unserved passengers. In the proposed model, the first optimization identifies the optimal train skip-stop patterns, while the second assigns these patterns to establish an hourly train schedule. The paper's case study uses data from the Bangkok Mass Transit System (BTS) SkyTrain Silom Line in Bangkok, Thailand and considers the 0.5, 1, 1.5, and 2 m social distancing schemes. The results reveal that the optimal train skip-stop patterns are superior to the all-stop alternative with, on average, a 13.4% faster travel time at the same level of unserved passengers. Furthermore, the non-dominated schedules from the second optimization decrease the numbers of unserved passengers given equal average passenger travel times. • Decision-support model proposed for urban train operation policy in COVID-19 pandemic. • Two-stage multi-objective optimization to schedule train skip-stop patterns. • Trade-off among social distancing policy, passenger travel time, and unserved passengers. • Skip-stop schemes have 13.4% faster passenger travel times than all-stop. [ABSTRACT FROM AUTHOR]

• Post COVID-19, a third of Scottish residents expect to use public transport less. • A random parameter bivariate probit model was estimated to analyse survey data. • Sociodemographic, perceptual and behaviour factors affected future bus & train use. • Perceived risk of COVID-19 infection significantly influenced future travel choices. • Frequent bus users in urban areas were less likely to abandon public transport. This paper examines the determinants of changes in future public transport use in Scotland after the COVID-19 pandemic. An online questionnaire was distributed to 994 Scottish residents in order to identify travel habits, attitudes and preferences during the different phases of the COVID-19 outbreak and travel intentions after the pandemic. Quota constraints were enforced for age, gender and household income to ensure the sample was representative of the Scottish population. The respondents indicated that they anticipated they would make less use of buses and trains at the end of the pandemic. Over a third expect to use buses (36%) and trains (34%) less, whilst a quarter expect to drive their cars more. As part of the analysis, a random parameter bivariate probit model with heterogeneity in the means of random parameters was estimated to provide insights into the socio-demographic, behavioural and perceptual factors which might affect future public transport usage. The inclusion of random parameters allows for the potential effects of unobserved heterogeneity within the independent variables to be captured, whilst making allowances for heterogeneity in the means of the random parameters. The model estimation showed that several factors, including pre-lockdown travel choices, perceived risk of COVID-19 infection, household size and region significantly affected intended future use of public transport. In addition, several variables related to age, region, pre-lockdown travel choices and employment status resulted in random parameters. The current paper contributes to our understanding of the potential loss of demand for public transport and the consequences for future equitable and sustainable mobility. Our findings are highly relevant for transport policy when developing measures to strengthen the resilience of the public transport system during and after the pandemic. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has presented a new wave of health, infrastructure and built environment challenges and opportunities. The COVID-19 pandemic induced environment presents a divide between the "new and old normal" with policy and planning implications for health, transport and general socio-economic growth and development. Multiple and complex nuanced transport matters cascade all geographic scales and pervade all sectors of the economy. The extent to which existing transport systems capacities are resilient, adaptive, and optimized for complete disaster planning, management and sustainability is questioned. This paper critically reviews how the COVID-19 pandemic has stretched the resilience and adaptive transport systems capacities in South Africa. A critical question interrogated is whether on-going policy and planning interventions constitute imperfect or perfect attempts at closing COVID -19 policy and planning emergent gaps. The paper makes use of South Africa as a case study, referencing the Disaster Management Act (No. 57 of 2002) and logical Disaster Management Act: Regulations relating to COVID-19 (Government Notice 318 of 2020), 1 1 As amended from time to time i.e., Disaster Management Act, 2002 (Act No. 57 of 2002), having declared a national state of disaster published by Government Notice No 313 of 15 March 2020, and extended by Government Notices Nos 646 of 5 June 2020. 765 of 13 July 2020, 889 of 15 August 2020, 995 of 14 September 2020, 1090 of 14 October 2020, 1225 of 14 November 2020 and 1341 of 11 December 2020, No. R. 15 of 13 January 2021 and No. R69 of 1 February 2021. with specific reference to the transport sector lockdown regulations in unravelling policy and planning implications. Drawing from the complex systems adaptive theory (CSAT), sustainability theory (ST), innovation theory (IT), transitions theory (TT), thematic COVID -19 transport planning and policy adaptation, mitigation measures in the South African transportation sector are discussed. Emergent lessons with respect to developing and advancing a new generation of resilient, adaptive, and optimized transport proof infrastructure and services including revising transport and related policies that navigates through various waves and cycles of induced pandemic and shocks is suggested. • The transport divide between the "new and old normal" planning and policy regimes. • COVID-19 transport complexities, challenges, and tensions in policy (re)formulation. • Questions whether policy and planning interventions constitute (im)perfect attempts at closing COVID -19 emergent gaps. • Emergent lessons for resilient, adaptive, and optimized transport proof COVID -19 infrastructure and services are suggested. [ABSTRACT FROM AUTHOR]

This paper develops the concept of 'thick time' through podcast experiments that foreground feminist voice, speech and aurality. Drawing upon William Kentridge's notion of 'thick time', this paper draws upon gendered experiences of struggles that are accumulated over generations, reinforced through technologies of timekeeping and time-management, leading to a state of permanent crisis. In particular, it focusses on gendered time through thick descriptions marked by fragmentation and simultaneity and heavy with the struggles of the past, present and future. Using experimentation as a method of examining thick time, I document how the temporalities of an intimate domesticity under crisis unfolds in parallel with the public crisis of a pandemic. Experimentation emerges in podcast training workshops with young women living in resettlement colonies in Delhi's urban peripheries, leading to a series of four podcasts co-produced remotely during the COVID19 lockdown. These podcasts are marked by fragments of speech, poetry, and music by those who are unable to speak to the state or to their families directly. They suggest how entangling the body (as voice, accent and language) with thick descriptions of time is possible through the unwinding, slowing down, rewinding and enriching the experience and significance of time in their lives. The podcasts are marked by repetition, futility, in-betweenness and remarkability, and the paper suggests that podcasts as experiments in value-laden thick descriptions of time can reveal pathways to feminist urban futures. [ABSTRACT FROM AUTHOR]

This paper explains the evolving role of the local supply chain across different disruption scenarios. A systematic literature review of 91 papers from 33 journals supports definition of the local supply chain and explication of benefits before, during and after COVID-19 disruption. Resilience emerges as the prevalent benefit at each stage, but to varying scales. In the pre-COVID-19 era, where the local supply chain serves as a back-up approach to the more dominant global supply chain, localisation's capacity to absorb change promotes resilience to mitigate low-magnitude disruption. At initial outbreak of COVID-19 disruption the local supply chain was necessitated as an emergency response to the untenability of global operations, demonstrating transformational resilience to survive. As disruption continued, the local supply chain upscaled and adapted to recover. In the post-COVID-19 era, resilience is expected to remain a strategic priority, promoting continued investment in local operations to thrive with embedded resilience. As a result, the COVID-19 pandemic welcomed a point of transition for the local supply chain as capacities and benefits are revaluated and localisation recognised as critical in developing resilient supply networks for the future. This study consolidates this evolution to offer a propositional framework showcasing the local supply chain across different disruption scenarios. This offers long overdue definition and explanation of the local supply chain and its relationship with resilience, addressing an existing lack of academic attention and encouraging alignment of local-versus-global decisions with changing strategic priorities. [ABSTRACT FROM AUTHOR]

The paper contends that the covid-19 pandemic has reconfigured how cities manage urban marginality within Zimbabwe. The pandemic enabled cities to push everyday mundane practices away from the centre of the city to the margins of the cities through securitisation and criminalising informal activities within the centre. The paper's methodological underpinnings are hinged on a six month ethnography within Bulawayo (Zimbabwe's second largest city). The paper maintains the view that managing urban marginality in the context of covid-19 must be framed within the binary of two opposing forces – governing institutions (the government and municipality) and the low-income residents. Using covid-19 as a pretext of "curbing the pandemic" governing institutions have pushed informal economic activities from the centre through employing law and force; on the other hand low-income residents have found eclectic means to counteract the measures imposed to constrain their agency. Finally, the paper suggests that covid-19 has reconfigured how cities manage urban marginality through pushing everyday informal practices to the periphery and how the low-income residents deploy their agency to counteract confinement hence managing urban marginality can be understood as a two-way process involving governing institutions and the low-income residents. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has reinforced an interest in the relationship between air pollution and respiratory viral infections, indicating that their burden can be increased under poor air quality. This paper reviews the pathways through which air pollutants can enhance susceptibility to such infections and aggravate their clinical course and outcome. It also summarizes the research exploring the links between various viral infections and exposure to solid and gaseous pollution in Poland, a region characterized by poor air quality, especially during a heating season. The majority of studies focused on concentrations of particulate matter (PM; 86.7%); the other pollutants, i.e., BaP, benzene, CO, NO x , O 3 , and SO 2, were studied less often and sometimes only in the context of a particular infection type. Most research concerned COVID-19, showing that elevated levels of PM and NO 2 correlated with higher morbidity and mortality, while increased PM 2.5 and benzo[a]pyrene levels were related to worse clinical course and outcome in hospitalized, regardless of age and dominant SARS-CoV-2 variant. PM 10 and PM 2.5 levels were also associated with the incidence of influenza-like illness and, along with NO 2 concentrations, with a higher rate of children's hospitalizations due to lower respiratory tract RSV infections. Higher levels of air pollutants also increased hospitalization due to bronchitis (PM, NO x , and O 3) and emergency department admission due to viral croup (PM 10 , PM 2.5 , NO x , CO, and benzene). Although the conducted studies imply only correlations and have other limitations, as discussed in the present paper, it appears that improving air quality through reducing combustion processes in energy production in Poland should be perceived as a part of multilayered protection measures against respiratory viral infections, decreasing the healthcare costs of COVID-19, lower tract RSV infections, influenza, and other respiratory viral diseases prevalent between autumn and early spring, in addition to other health and climate benefits. [Display omitted] • Polluted air may increase the severity of respiratory viral infections. • Poland is a PM and BaP pollution hot spot in Europe. • Air pollution is associated with a higher burden of respiratory viral infections. • Improving the air quality should be a protection measure against infections. • Further studies are encouraged to explore the relationships with other pathogens. [ABSTRACT FROM AUTHOR]

• Childcare policy responses towards Covid-19 differed in European welfare states. • Policy responses cause informalization of childcare to different degrees. • Informalization of childcare is mainly connected with social risks for women. • Cultural and institutional differences help to understand varying policy responses. The closure of extra-familial childcare facilities by European governments in 2020 was an important part of interventions against the spread of the COVID-19 pandemic. One consequence was that childcare was provided by parents at home, mainly by women. As a result, women mainly experienced financial and employment risks related to this "informalization" of childcare. The childcare policies of European welfare states differ in the extent to which they include measures to reduce the social risks related to informalization. Against this backdrop, this paper asks: How should one understand cross-national differences in childcare policies during the pandemic? We are also particularly interested in the effects of childcare policies on the social risks connected with the informalization of childcare and what these mean for the gendered division of paid work and care. Differences in childcare policies during the pandemic are commonly explained in terms of the path dependence of such policies. Using the theoretical approach of "care arrangement," this article introduces a broader theoretical framework that considers the role of cultural and institutional factors for understanding the cross-national differences in childcare policies during the pandemic. We introduce the findings of a comparative empirical study of childcare policies in three European welfare states—Denmark, Germany and England—that represent different types of care arrangements. This paper uses policy and media documents, quantitative data on childcare and women's employment, cultural ideas and secondary analysis of empirical studies. We find that governments did not per se respond to the pandemic based on institutional path dependence regarding childcare policies, while the integration of culture into the theoretical framework allows for a more comprehensive understanding. [ABSTRACT FROM AUTHOR]

In September 2020 Deutsche Bank published a report entitled "The Age of Disorder." Fronted dramatically by the image of a volcano and volley of lightning across a brooding sky, the report warned that the global economy was on the precipice of a phase shift from neo-liberal quasi-stability to an era characterized by dramatic social, economic, and ecological upheavals. Deutsche Bank's researchers call this new period "the age of disorder". This paper proposes Deutsche Bank's appraisal is part of a broader condition that it calls capitalist catastrophism and an emerging regime of eco-apartheid. Taking as its point of departure Geoff Mann and Joel Wainwright's call for geographers to construct concepts that might help navigate a hotter planet, the paper introduces capitalist catastrophism as a concept that names and theorizes the effects of a dramatic and asymmetrically catastrophic reconfiguration of the capitalist world-system. Capitalist catastrophism has three characteristics. First, a newfound ability for social movements and theorists to imagine post-capitalist futures combined with an inability to realize them. Second, cascading and mutually amplifying social and ecological crises that outrun the capacities of states and capital to contain them. Third, an unevenly distributed cancellation of human and non-human futures. Drawing on Marxist political economy and Nyasha Mboti's theorization of apartheid, the paper concludes that capitalist catastrophism is resolving itself into a global system of eco-apartheid in which a green transition for some is secured by putting the exploited and oppressed majority in harm's way. [ABSTRACT FROM AUTHOR]

• SARS-CoV-2 significantly impacts the UPS, a key cellular regulatory mechanism. • SARS-CoV-2 encoded proteinsinteract with host UPS, influencing immune signaling and apoptosis. • Stability of ORF3a and ORF8 may or may not be influenced by host proteasome. • Unlike previous studies, recent studies do not show ORF3a as an ion channel. • SARS-CoV-2 PLpro alters host immune responses via interfering withUPS. • Proteomic studies reveal changes in ubiquitination in SARS-CoV-2 infected cells. • Promising treatment include targeting PLpro with zinc-ejector drugs, targeting viral nsp12 via heat treatment. The Ubiquitin proteasome system (UPS), an essential eukaryotic/host/cellular post-translational modification (PTM), plays a critical role in the regulation of diverse cellular functions including regulation of protein stability, immune signaling, antiviral activity, as well as virus replication. Although UPS regulation of viral proteins may be utilized by the host as a defense mechanism to invade viruses, viruses may have adapted to take advantage of the host UPS. This system can be manipulated by viruses such as the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) to stimulate various steps of the viral replication cycle and facilitate pathogenesis, thereby causing the respiratory disease COVID-19. Many SARS-CoV-2 encoded proteins including open reading frame 3a (ORF3a), ORF6, ORF7a, ORF9b, and ORF10 interact with the host's UPS machinery, influencing host immune signaling and apoptosis. Moreover, SARS-CoV-2 encoded papain-like protease (PLpro) interferes with the host UPS to facilitate viral replication and to evade the host's immune system. These alterations in SARS-CoV-2 infected cells have been revealed by various proteomic studies, suggesting potential targets for clinical treatment. To provide insight into the underlying causes of COVID-19 and suggest possible directions for therapeutic interventions, this paper reviews the intricate relationship between SARS-CoV-2 and UPS. Promising treatment strategies are also investigated in this paper including targeting PLpro with zinc-ejector drugs, as well as targeting viral non-structural protein (nsp12) via heat treatment associated ubiquitin-mediated proteasomal degradation to reduce viral pathogenesis. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic received considerable attention from public relations scholars and research publications after the pandemic exploded. This study provides an overview of the status of COVID-19 public relations research in terms of topics, regional focuses, organization-public relationships, public relations approaches and themes, stages of the pandemic, and theoretical and methodological frameworks. This study also investigated how public relations research has described the characteristics of the COVID-19 pandemic. We reviewed 126 articles published in peer-reviewed journals using quantitative and qualitative content analyses. We expect that this review provides new insights based on the current literature to advance public relations research in today's fast-changing society. • We analyzed 126 COVID-19 public relations research papers that have been published in peer-reviewed journals from the height of the pandemic in 2020 to February 2023. • Public Relations Review was the largest publication venue in COVID-19 public relations research (n = 17, 13.5%). • The most common topics in COVID-19 public relations research were relationship management (n = 43, 34.1%), crisis/risk management (n = 37, 29.4%), and issue management (n = 24, 19.0%). • Public Relations research papers described the COVID-19 pandemic situation commonly with six characteristics: transboundary impacts, uncertainty, global-scale event, health crisis, calling for sophisticated communication strategies, and an external crisis event. [ABSTRACT FROM AUTHOR]

Marketplaces are regarded as essential public spaces, providing not only access to fresh produce but also functioning as important social infrastructures. Marketplaces also strengthen urban-rural linkages, and their functioning is important for the livelihoods of urban and rural dwellers. However, many marketplaces closed down or had to adhere to various restrictions in response to the COVID-19 coronavirus outbreak. This paper provides an assessment of the impacts and perspectives of the COVID-19 pandemic on urban-rural linkages in the Musanze District in Rwanda. In addition, the study presents policy measures in the context of the COVID-19 pandemic that the Government of Rwanda put in place in 2020 that impacted the urban-rural flow of people and goods, with severe implications for market vendors' livelihoods. This paper also answers on how have COVID-19 related policy reactions in Rwanda influenced urban-rural linkages, learning from Musanze District. Combining research conducted at markets in the Musanze District before and during the COVID-19 pandemic movement restrictions, this paper provides recommendations on market infrastructure upgrades necessary for markets to become more resilient and better functioning in preparation for any new public health crisis occurring. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic, which broke out in early 2020, has disrupted China's population mobility on which its cities depend. Especially, during the two years of the pandemic, female migrant laborers have been taking the risk of becoming the surplus population in an unstable and hyper-competitive job market. Such circumstance has led their images and urban engagement to be finitely recorded by the narrative that emerged based on the pandemic -- outbreak narrative in Priscilla Wald's term (2008), serving as an essential archive to examine Chinese women labor and urban history during the COVID-19. Taking an article narrating the vagrancy of a female migrant worker -- Ah Fen in Shanghai as a threshold, this paper explores China's gendered migrancy and female migrant communities during the COVID-19. By tracing China's COVID-19-related outbreak narratives through literary analysis, this paper reveals that the COVID-19 pandemic has flooded more migrant workers, especially female laborers, into the gig economy and this new urban engagement pattern has made migrant women's mobility higher and encouraged them to rebuild migrant communities in the city. However, the high mobility and fragmented urban distribution, in turn, have strengthened their roles as "infected strangers" in the outbreak narrative, particularly in the context of China's strict differentiated quarantine governance. This research, initiated by literary analysis, on the one hand, draws a dynamic map of capital and population flow during the pandemic era, while on the other hand, exposes the injustice among different classes, genders, and ages in urban China enlarged by the virus. • The outbreak narrative is an alternative archive of examining China's women labor migrancy and urban history in COVID-19. • Migrant-labor-themed outbreak narratives are largely gender-blind, ignoring the subjectivity and uniqueness of women individuals. • Female Chinese migrant workers built high-mobility and temporary settlements by occupying urban public spaces in COVID-19. • Pandemic regulation, industrial transformation, and capital outflow have jointly pushed women labor into the gig economy. • Women labor with higher mobility serves as an urban infrastructure to communicate boundraries between different communities. [ABSTRACT FROM AUTHOR]

The impacts of the global financial crisis (GFC) and the COVID-19 pandemic crisis can be far-reaching, shedding light on the dynamics of dependence between commodity markets (e.g., gold, silver, and Brent) and stock markets. This paper employs a novel quantile-on-quantile regression and the causality-in-quantiles approaches to elicit significant asymmetric dependence between commodity and the stock markets across MENA countries. Our results show that the marginal impact of stock prices on commodity markets varies; the effects are generally negative across quantiles, with all stock markets exerting a greater impact on commodity prices at lower quantiles. Exogenous shocks (e.g., GFC) appear to have imparted greater negative impact on both the dynamic nature of interdependence and the direction of causality, demonstrating investors' adaptability to uncertainty. For several MENA countries, the hedging ratio shows strong hedging effectiveness for silver and even greater for Brent. The gold appears to lose its shine over hedging effectiveness. [Display omitted] ● This paper investigates the dynamics of dependence between commodity and MENA stock markets. ● This paper analyzes the impacts of global financial crisis and COVID-19 pandemic. ● All stock markets exert larger effects on commodity prices at lower quantiles. ● The GFC imparted greater negative effects on both dynamic nature of interdependence. ● Strong hedging effectiveness for silver and more for Brent for a number of MENA countries [ABSTRACT FROM AUTHOR]

During COVID-19, global supply chain disruption marred communities around the world. In active humanitarian crises, these logistical issues compounded underlying vulnerabilities in communities most in need, pushing humanitarian supply networks to adapt rapidly in order to continue providing aid and development activities. This paper therefore seeks to answer: " how can social capital be leveraged in disaster response during a crisis?". By conceptualizing resilience through the lens of social capital theory, the paper abductively analyses 17 semi-structured interviews, into other to identify four examples of how social capital was leveraged by local humanitarian supply networks during COVID-19. By adopting a "peri-crisis lens" from trauma-informed research, the paper finds four resiliency behaviours of humanitarian supply networks: 1) localising supplier networks, 2) engaging local production, 3) pivoting to cash provision for local households, and 4) handing over to national staff. This paper argues that individuals, organisations, and communities are more resilient, not merely when they have social capital, but when they can rapidly leverage that social capital in a crisis. Through a close examination of the factors that enabled and hindered the leveraging of social capital during COVID-19, the paper offers practical policy suggestions for government and non-governmental organisations engaged in humanitarian supply networks, to rapidly respond in future pandemics. Additionally, the paper demonstrates the use of peri-crisis as a lens. [ABSTRACT FROM AUTHOR]

• Socio-demographic attributes and daily habits influence the COVID-19 infection. • Madrid's measures make public transport a relatively safe option during the pandemic. • High crowding increases the risk of COVID-19 on certain public transport services. The COVID-19 pandemic has changed people's mobility patterns, increasing the preference for private modes and reducing the public transportation demand. Most scientific contributions have studied the role of mobility levels in the spread of the virus and the influence of public transport on COVID-19 infections, but ignoring the importance of individual-level variables potentially affecting COVID-19 infection, such as daily habits. This paper analyses the relationship between the probability of being infected by COVID-19 and using public transport through a survey data analysis, taking Madrid (Spain) as the case study. This research uses a survey campaign with more than 15,000 responses, capturing socio-demographic aspects, COVID-19 infections, daily habits, and mobility patterns with high risk of COVID-19 infection. Through a multilevel probit model, this paper explores the extent to which a higher use of public transport is related to a greater likelihood of COVID-19 infection. The results suggest a relationship, although not very strong, between the probability of infection and the conjunction of higher frequency of use of metro services and level of crowding during the trip, whereas the use of bus services and travel time within the vehicle do not appear to affect. [ABSTRACT FROM AUTHOR]

Coronavirus 2019 (COVID-19) and its variants are still spreading rapidly with deadly consequences and profound impacts on the global health and world economy. Without a suitable vaccine, mobility restriction has been the most effective method so far to prevent its spreading and avoid overwhelming the heath system of the affected country. The compartmental model SIR (or Susceptible, Infected, and Recovered) is the most popular mathematical model used to predict the course of the COVID-19 pandemic in order to plan the control actions and mobility restrictions against its spreading. A major limitation of this model in relation to modeling the spreading of COVID-19, and the mobility limitation strategy, is that the SIR model does not include mobility or take into account changes in mobility within its structure. This paper develops and tests a new hybrid SIR model; SIR-M which is integrated with an urban activity travel model to explore how it might improve the prediction of pandemic course and the testing of mobility limitation strategies in managing virus spread. The paper describes the enhanced methodology and tests a range of mobility limitation strategies on virus spread outcomes. Implications for policy and research futures are suggested. [ABSTRACT FROM AUTHOR]

The current paper deals with the transmission of MERS-CoV model between the humans populace and the camels, which are suspected to be the primary source for the infection. The effect of time MERS-CoV disease transmission is explored using a non-linear fractional order in the sense of Caputo operator in this paper. The considered model is analyzed for the qualitative theory, uniqueness of the solution are discussed by using the Banach contraction principle. Stability analysis is investigated by the aid of Ulam-Hyres (UH) and its generalized version. Finally, we show the numerical results with the help of generalized Adams-Bashforth-Moulton Method (GABMM) are used for the proposed model, for supporting our analytical work. [ABSTRACT FROM AUTHOR]

This paper considers the nature of social surveillance through the physical activity tracking app MapMyRun and examines how this was experienced during the COVID-19 pandemic during the UK and USA summer 2020 lockdowns. In contributing to debates in digital geographies around the entanglements of the fleshy and digital body, the paper responds to calls for research to recognise the increasing sociality of self-tracking (Couture, 2021), specifically considering how, during the COVID-19 pandemic, these apps offered a form of connection during a time of isolation. Using data from email and video interviews, I argue that whilst a Foucauldian account of surveillance can be used as a point of departure, it is limited in accounting for the social aspects of self-tracking. I therefore propose that applying Robinson's (2000) concept of 'noisy surveillance' to self-tracking is useful for understanding the messiness of surveillance in terms of the complications and noisiness involved in interactions in digital spaces, as well as the opportunities for performance management online particularly during lockdown. [ABSTRACT FROM AUTHOR]

This paper presents the results of primary research involving a survey of Polish passengers during the COVID-19 pandemic. The survey was conducted in January 2022 using the CAWI method on a representative sample of N = 1,129 Polish adults aged 18 to 60. First, means of transport were classified by distance covered into urban, regional and interregional transport. Survey participants were asked a series of questions regarding the use of modern technologies in public transportation. Before that, however, the frequency of urban (agglomeration), regional, interregional (transregional) and private transport use during the COVID-19 pandemic was examined. More than half of respondents said they used modern technology across all modes of public transportation, and about a quarter said that while they had not yet used these technologies, they intended to do so. Only about 15% of respondents replied they did not use and did not intend to use modern technologies, regardless of the mode of transport. The aim of the paper is to examine the use of modern technologies by passengers in the era of the COVID-19 pandemic. Research results may help to influence passengers' purchasing decisions and to improve services offered by carriers in accordance with the objectives of long-term transport policy of both the European Union and Poland related to sustainable transport development. [ABSTRACT FROM AUTHOR]

A co-author of this paper had previously presented the principle of "Stay with Your Community" as a method of countermeasures against COVID-19 infection spread and have been working on its social implementation. This case study paper presents an example of activities to spread the Stay with Your Community principle to citizens and visitors in Shimoda City, Shizuoka Prefecture, in order to control the spread of COVID-19 infection. As a result, the infection cluster was successfully controlled. The authors discuss the effect of the regional workshop as a key to open the way to Organizational Citizenship Behavior of participants. [ABSTRACT FROM AUTHOR]

This paper presents the results of primary research involving a survey of travelers in Poland during the COVID-19 pandemic. The survey was conducted in January 2022 using the CAWI method on a representative sample of n =1,129 Polish adults aged 18 to 60. As many as 74% of respondents believe that the COVID-19 pandemic has changed traveler preferences with regard to urban, regional and trans-regional transport. Two-thirds of respondents (67%) cited a car as the most common mode of transportation, 40% said they walk, and 25% use buses. Just over 10% chose a bicycle (14%) or tram (11%). The least preferred mode of transportation was the train (7%). The three most frequently cited features of transportation services that respondents considered most important included easy ticket purchasing, ticket prices, and cost of travel. The aim of this paper is to discuss the decisions of Polish passengers using public transport during the COVID-19 pandemic. The results of the research may help to influence the passengers' purchasing decisions in accordance with the objectives of long-term transport policy of both the European Union and Poland related to sustainable transport development, as well as the decisions of transport operators as this is a stakeholder group that may be interested in applying them in their daily work and carriers themselves, competing for passengers. [ABSTRACT FROM AUTHOR]

One of the options for corporations to gain a competitive advantage in the marketplace is by establishing business cooperation between large companies and start-ups. Start-ups see corporations as recipients of their solutions (products, services). Meanwhile corporations are interested to a large extent in getting access to breakthrough technologies, innovations, new business models present on the market. This paper deals with the assessment of opportunities for business cooperation in the start-up-corporate model at the background of global market turbulence caused by the Covid-19 pandemic. The research focused on the perception of this cooperation from the perspective of start-ups', which: are at different phases of development (concept phase, development phase, scaling up phase) and are characterised by different lengths of time they operate in the marketplace. This paper aims at presenting the impact of market turbulence on the relationship between start-ups and corporations, and thus to verify the research question regarding the impact of market perturbations caused by the COVID-19 pandemic on mounting corporate interest in cooperation with start-ups. The results were analysed based on a sample of 101 start-ups participating in acceleration programmes with the involvement of large companies, organised by start-up accelerators. The findings allow to draw several conclusions: representatives of start-ups more often observe a decrease in interest and priority of cooperation between corporations and start-ups in connection with the emergence of a global pandemic. Moreover, they less often feel that corporations are looking for start-ups outside their core areas of interest, while it is start-ups that have to adapt to the changing needs of technology users. [ABSTRACT FROM AUTHOR]

The COVID-19 global pandemic has changed every facet of our lives overnight and has resulted in many challenges and opportunities. Utilizing the Lens of Vulnerability we investigate how disparities in technology adoption affect activities of daily living. In this paper, we analyze the existing literature and case studies regarding how the lifestyles of socially vulnerable populations have changed during the pandemic in terms of technology adoption. Socially vulnerable populations, such as racial and ethnic minorities, people with disabilities, older adults, children, and the socially isolated, are specifically addressed because they are groups of people who have been significantly and disproportionately affected by the pandemic. This paper emphasizes that despite seeing changes in and research on technology adoption across healthcare, employment, and education, the impact of COVID-19 in government and social services and activities of daily living is underdeveloped. The study concludes by offering practical and academic recommendations and future research directions. Lessons learned from the current pandemic and an understanding of the differential technology adoption for activities of daily living amid a disaster will help emergency managers, academics, and government officals prepare for and respond to future crises. [ABSTRACT FROM AUTHOR]

The influence of pandemics is still a black box, and the mechanism is attracting the attention of policymakers and scholars to guide the policy design in the aftermath of Covid-19 pandemics. This paper takes an in-depth look at the performance impact of pandemics from the perspective of operation, which is essential in a comprehensive evaluation of the economic effects of pandemics. With the help of novel quarterly data of Chinese listed firms from 2019 Q1 to 2021 Q2, we find that the Covid-19 decreases the sale-related profitability. For the mechanism, this paper finds that the pandemics make the operation longer, increase the cost, and reduce the potential cash flows. In addition, the environmental tax can significantly weaken the adverse shocks. The policy implication is that the sale boosting or consumption stimulus is vital in economic recovery, and the governments should efficiently use the positive effect of environmental tax. [ABSTRACT FROM AUTHOR]

Global efforts are focused on discussing effective measures for minimizing the impact of COVID-19 on global community. It is clear that the ongoing pandemic of this virus caused an immense threat to public health and economic development. Mathematical models with real data simulations are powerful tools that can identify key factors of pandemic and improve control or mitigation strategies. Compared with integer-order and left-hand side fractional models, two-side fractional models can better capture the state of pandemic spreading. In this paper, two-side fractional models are first proposed to qualitative and quantitative analysis of the COVID-19 pandemic. A basic framework are given for the prediction and analysis of infectious diseases by these types of models. By means of asymptotic stability analysis of disease-free and endemic equilibrium points, basic reproduction number R 0 can be obtained, which is helpful for estimating the severity of an outbreak qualitatively. Sensitivity analysis of R 0 is performed to identify and rank key epidemiological parameters. Based on the real data of the United States, numerical tests reveal that the model with both left-hand side fractional derivative and right-hand side fractional integral terms has a better forecast ability for the epidemic trend in the next ten days. Our extensive computational results also quantitatively reveal that non-pharmaceutical interventions, such as isolation, stay at home, strict control of social distancing, and rapid testing can play an important role in preventing the pandemic of the disease. Thus, the two-side fractional models are proposed in this paper can successfully capture the change rule of COVID-19, which provide a strong tool for understanding and analyzing the trend of the outbreak. [ABSTRACT FROM AUTHOR]