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The Coronavirus Disease (COVID-19) caused a lot of mortality. The high mortality rate occurred because of the physicians' wrong or late identification of COVID-19 severity. So, developing Computer-Aided Design (CAD) systems using Artificial Intelligence (AI) techniques is critical to help physicians correctly identify the severity of COVID-19 in the early stages of the pandemic and then decrease the COVID-19 mortality percentage. In this paper, we develop a new green CAD system using a new hybrid handcrafted feature extraction algorithm and two-stage neural network architecture to grade the COVID-19 patient based on Computed Tomography (CT) scan images as having a moderate, severe, or critical infection. Because the proposed system uses handcrafted feature extraction algorithms, it consumes minimum resources and time than recent works. The proposed system consists of three phases: lesion segmentation, feature extraction, and diagnosis. Firstly, lesions from the CT scan image are manually segmented, and then three schemes are applied to extract salient features from the segmented lesions. These schemes are the Histogram of Oriented Gradients (HOG), Speeded Up Robust Features (SURF), and a new hybrid method that consists of cascading Discrete Wavelet Transform (DWT) and Gray-Level Co-Occurrence Matrix (GLCM). Then, the Cumulative Distribution Function (CDF) is computed for each scheme to extract the statistical markers. In the grading phase, a two-stage neural network approach is used. First, the extracted features are individually trained and tested for each scheme in the first neural network stage, and then the results of the first stage are combined to train and test each patient in the second neural network stage. The performance of the proposed system was assessed on a CT image dataset of 300 COVID-19-positive patients collected from the Cancer Imaging Archive (TCIA) website. The experimental results showed that our proposed system achieved 100% accuracy and kappa when the dataset was partitioned into 80% for training and 20% for testing. Also, it achieved 95.67% ± 0.47, 99.33% ± 0.77, and 100% ± 0 accuracies and 93.48% ± 0.74, 98.997% ± 1.16, and 100% ± 0 kappa values when the data was organized using 2, 4, and 10 folds, respectively. A green complexity algorithm analysis shows that this proposed system takes O(n) time complexity and 1 h and 20 min to train and test all cases. The performed green complexity analysis shows that the proposed system consumes 117.80 g Carbon Dioxide Equivalent (CO2e), 130.80 Wh, and 0.13 tree months for the carbon footprint, the energy needed, and the carbon sequestration, respectively. These results show that the proposed work consumes fewer resources and provides a green CAD system. • This paper proposes a CAD system for COVID-19 severity prediction. • The proposed system uses a two-stage neural network architecture. • The proposed system consists of two phases: feature extraction and severity identification. • A green complexity analysis is performed to prove the greenness of the proposed system. [ABSTRACT FROM AUTHOR]

Since the beginning of the COVID-19 outbreak, understanding its impacts on cities has received much attention in science and policy circles. This paper systematically reviews the literature on the interface of the pandemic and urban sustainability. The objective is to portray the impacts brought by the COVID-19 outbreak in urban environments within the sustainability framework and to detect trends and challenges for future research. The paper follows a methodology that integrates both bibliometric and systematic review approaches. The first approach relies on bibliometric analysis to provide an overview of the landscape of main trends on this nexus. The second approach presents a content analysis that deepens the work by outlining the impacts of the pandemic and the challenges that emerged on five different key topics for urban sustainability. The role of resilient urban planning is discussed as an integrative concept to face diverse challenges in the construction of sustainable cities in a post-pandemic scenario. Likewise, the study deliberates on future research topics related to resilient urban planning, social equity, healthy urban environments, sustainable mobility, and circular economy. This review serves as a guide for researchers and urban planners to understand emerging challenges and future research trends in urban sustainability. [Display omitted] • A literature review integrating bibliometric and contents analysis synthesizes the effects of the COVID-19 on the urban sustainability. • The impacts are sorted into thematic areas: resilient urban planning, healthy urban environments, social equity, sustainable mobility, and circular economy. • Challenges for urban sustainability and resilience are proposed as topics for future research work. [ABSTRACT FROM AUTHOR]

• Atypical cultural goods offer novelty and influence how cultural markets evolve. • Measures taken to contain crises such as COVID-19 lockdowns induce negative affects. • Both cognition and affects determine how individuals evaluate categorical atypicality. • Negative affects negatively moderate the impact of atypicality on audiences' evaluations. This paper investigates the impact of atypicality on cultural goods reception. While prior research has assumed controlled and highly cognitive mechanisms in audience evaluations, this paper probes the influence of affective states. We suggest that crises, such as the COVID-19 pandemic, trigger affective states and sway evaluations of atypical cultural goods. In a longitudinal study on movie evaluations ("Study 1"), we analyze how the external shock of the COVID-19 lockdown announcement — proxying heightened negative affects including anxiety — interacted with movies' atypicality and their subsequent audience evaluations. Furthermore, two preregistered controlled experiments establish causal links at the individual level. Study 2 corroborates the causal relationship between the COVID-19 lockdown announcement and increased negative affects. Study 3 shows that higher negative affects moderate how perceived novelty mediates atypicality's effect on audience evaluations. Overall, this paper has important implications for research on categorization, social evaluations, and the consequences of COVID-19 policy consequences. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 variants (COVID-19 variants) pandemic is still wreaking havoc across the world; and has a key impact on our lives, but the dynamic evolution of COVID-19 variants is still obscure. Hausdorff fractal calculus provides a powerful method for fractal dynamics modeling in computational biology. This paper studies the applicability of Hausdorff fractal differential equations (HDE) in dynamics modeling and the alleviation of coronavirus for the first time. Scholars have generated amounts of instructive research about the COVID-19 variants, and lack of time dependence. This paper will develop a time-dependent Hausdorff derivative model which focuses on the modeling, applications, and data analysis for the COVID-19 variants pandemic spreading in the world. The model results show that the transmission, infection, and recovery dynamics follow the integral-order Susceptible, Exposed, Infectious, and Recovered (SEIR) model, and the evolution of the death toll follows the time HDE. In this paper, a modified SEIR model is proposed, denoted by Hausdorff fractal derivative SEIR (HSEIR) model. Then we apply the HSEIR model to predict the evolution of COVID-19 variants in Brazil, China, Germany, India, Italy, Japan, and South Africa. The model successfully captured the process of developing COVID-19 variants, which provides a theoretical basis for understanding the trend of the epidemic. • A Hausdorff fractal derivative SEIR model characterizes the spreading of COVID-19 variants pandemic in the world. • The new model can well predict the evolution of COVID-19 variants. [ABSTRACT FROM AUTHOR]

This paper explores the everyday mobilities of low-income populations during the Covid-19 pandemic through a mixed-methods study in İstanbul. It shows how additional economic burdens brought by the Covid-19 pandemic interplay with low-income residents' attitudes towards using different transport modes, mobility levels, and concerns over public transport use based on socioeconomic and demographic differences. It suggests that household structures, social networks and perceived mobility restrictions tend to be associated with mobility patterns and practices. The paper argues that changes and continuities in mobility trends and practices due to the pandemic should be considered with broader social and economic dynamics at the household and community level. It draws key lessons learned concerning the pandemic mobilities to be transferred to future transport policy and mobility governance so that intragenerational inequalities could be mainstreamed in these policy domains. [ABSTRACT FROM AUTHOR]

The lockdown of March and April 2020 declared by Spanish authorities in the Valencian Region to bending the Covid-19 curve, caused a drastic reduction of the economic activity and a severe limitation of mobility. People were asked to stay at home as much as possible. Education and administrative centers, as well as restaurants, theaters, sport arenas, etc., were closed. Work at conventional workplaces was prohibited for people who could tele-work, and students were compelled to attend classes on-line. Such limitation of mobility and spending so many time at home, could affect the wellbeing of people. The objective of the present paper is to present a study on the differences on wellbeing according to the mobility of respondents during the lockdown. Information from 1,827 individuals regarding the satisfaction of the basic psychological needs (Autonomy, Competence and Relatedness) and Positive and Negative affect were collected through and web-survey during the first lockdown of the Covid-19 pandemic, together with mobility data and sociodemographic characteristics. Mann-Whitney U tests, Confirmatory Factor Analyses and Structural Equation models are used to find out differences in the wellbeing of people according to their mobility characteristics during the first lockdown, and how these mobility characteristics are associated to the psychological variables studied. Mobility of people during the first lockdown reduced drastically, especially the youngest ones, and the main travel mode was walking. In general, the youngest participants in this research and females present lower values of the psychological wellbeing variables during the lockdown. A very low or very high degree of mobility is also associated to discomfort, although the more time spent traveling the better people feel. Those who walked more are related to lower values of wellbeing. Some lessons are learned to improve transport and mobility planning during a pandemic. [ABSTRACT FROM AUTHOR]

This article documents recent developments in emerging markets in the context of the COVID-19 pandemic, assesses their prospects and challenges, and discusses appropriate policy settings for the medium term. It argues that EM policymakers' ability to grapple with an incomplete and uneven recovery will be constrained by high public debt and uncertain inflation prospects as well as external risks surrounding capital flows and exchange rate developments. The paper also discusses potential impact of a tightening in global financial conditions and appreciation of the US dollar that could be triggered by a general increase in risk aversion or a reassessment of the likely path of US monetary policy. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus is likely to remain endemic globally despite widespread vaccination. There is increasing concern for myocardial involvement and ensuing cardiac complications due to COVID-19, however, the available evidence suggests these risks are low. Pandemic publishing has resulted in rapid manuscript availability though pre-print servers. Subsequent article retractions, a lack of standardised definitions, over-reliance on isolated troponin elevation and the heterogeneity of studied patient groups (i.e. severe vs. symptomatic vs all infections) resulted in early concern for high rates of myocarditis in patients with and recovering from COVID-19. The estimated incidence of myocarditis in COVID-19 infection is 11 cases per 100,000 infections compared with an estimated 2.7 cases per 100,000 persons following mRNA vaccination. For substantiated cases, the clinical course of myocarditis related to COVID-19 or mRNA vaccination appears mild and self-limiting, with reports of severe/fulminant myocarditis being rare. There is limited data available on the management of myocarditis in these settings. Clinical guidance for appropriate use of cardiac investigations and monitoring in COVID-19 is needed for effective risk stratification and efficient use of cardiac resources in Australia. An amalgamation of national and international position statements and guidelines is helpful for guiding clinical practice. This paper reviews the current available evidence and guidelines and provides a summary of the risks and potential use of cardiac investigations and monitoring for patients with COVID-19. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is an ongoing pandemic and is placing additional burden on healthcare systems around the world. Timely and effectively detecting the virus can help to reduce the spread of the disease. Although, RT-PCR is still a gold standard for COVID-19 testing, deep learning models to identify the virus from medical images can also be helpful in certain circumstances. In particular, in situations when patients undergo routine X -rays and/or CT-scans tests but within a few days of such tests they develop respiratory complications. Deep learning models can also be used for pre-screening prior to RT-PCR testing. However, the transparency/interpretability of the reasoning process of predictions made by such deep learning models is essential. In this paper, we propose an interpretable deep learning model that uses positive reasoning process to make predictions. We trained and tested our model over the dataset of chest CT-scan images of COVID-19 patients, normal people and pneumonia patients. Our model gives the accuracy, precision, recall and F-score equal to 99.48%, 0.99, 0.99 and 0.99, respectively. • Quasi-ProtoPNet uses the positive reasoning process. • The positive reasoning process is usually used to solve a multiple-choice question. • The performance is on par with the state-of-the-art non-interpretable models. • Theorem 3.1. provides the theoretical evidence of better performance of our model. • Quasi-ProtoPNet uses prototypes with both types of spatial dimensions. • Our model keeps constant connections between similarity scores and logits. • The suspension of convex optimization of the last layer reduces the training time. [ABSTRACT FROM AUTHOR]

• Impact of COVID-19 on ECE programs differed greatly by program type, funding source. • Family child care homes fared worse in most measures of economic well-being with direct implications for individual providers during COVID-19. • Lower attendance, staffing concerns were pandemic challenges for center-based care. • Voucher-receiving centers more likely to face negative impacts during COVID-19. • Head Start/state-contract centers more able to support staff well-being in pandemic. The COVID-19 crisis has overwhelmed and weakened the United States early care and education (ECE) sector, jeopardizing a system that was already precariously situated atop a weak foundation. While multiple national- and state-level studies have highlighted the overwhelming impacts of the pandemic on the ECE sector, little has been reported about how much variation in impacts exists, and in what forms, within the ECE sector. Based on a statewide survey of 953 licensed care providers in California conducted in June 2020, this paper examines the impact of COVID-19 experienced by ECE providers, focusing on the variations between centers and family child care homes (FCCs) and among center-based programs. Results indicate that the challenges programs face differ greatly depending on program type and funding source. Compared to center-based programs, FCCs fared worse in most measures of economic hardship that directly impact individual providers with medium to large effect sizes. Centers were more likely than FCCs to struggle with reduced attendance and changes in program operations by medium to large effect sizes and report staffing challenges by small to medium effect sizes. Among the center-based programs, subsidized programs holding contracts with Head Start or the California Department of Education (such as state preschool programs) were more stable and better able to financially support their staff during the pandemic, with effect sizes ranging from medium to large. Centers receiving government subsidies in the form of vouchers were more likely to be negatively impacted by the pandemic compared to unsubsidized centers and Head Start and state-contracted centers. Implications for future research and policy are discussed in the context of addressing the complex delivery system of ECE services and supporting outcomes that are effective and equitable for children, families, and the ECE workforce. [ABSTRACT FROM AUTHOR]

• COVID-19 is a threat to recent progress in expanding access to quality early childhood education ECE in low- and middle- income countries. • Governments in the study did not prioritize ECE in their responses to the pandemic. • Some conditions that previously helped ECE advance on national agendas worsened. • Lessons from this analysis have implications for prioritizing ECE postpandemic. Despite strong evidence of its importance to the welfare of children and societies, early childhood education has been comparatively neglected as a policy priority both before and during the COVID-19 pandemic. This paper seeks to understand what factors have contributed to the relative lack of priority for early childhood education in distance learning and school reopening plans by applying a political prioritization framework to the pandemic context in 4 low- and middle-income countries: Ethiopia, Jamaica, Liberia, and Pakistan (Punjab Province). Some aspects of the pre-COVID-19 status quo which disfavored early childhood education have continued, including a lack of cohesive support from civil society and a greater focus by international partners on norm promotion and technical assistance than financing. In other respects, the pandemic put early childhood education at an even greater disadvantage. These include perceptions that early childhood education is less suited to distance delivery than other levels of education, concerns about young children's ability to comply with health protocols, and competition with high-stakes examinations for education ministries' attention. Previous country experience with pandemics (in Liberia) and a strong coordinating entity (in Jamaica) were mitigating factors. These results point to an urgent need to elevate priority for early childhood education in more normal times and to capture the lessons of COVID-19 to improve the resilience of early childhood education in future crises. [ABSTRACT FROM AUTHOR]

The significance of social media has increased manifold in the past few decades as it helps people from even the most remote corners of the world to stay connected. With the advent of technology, digital media has become more relevant and widely used than ever before and along with this, there has been a resurgence in the circulation of fake news and tweets that demand immediate attention. In this paper, we describe a novel Fake News Detection system that automatically identifies whether a news item is "real" or "fake", as an extension of our work in the CONSTRAINT COVID-19 Fake News Detection in English challenge. We have used an ensemble model consisting of pre-trained models followed by a statistical feature fusion network, along with a novel heuristic algorithm by incorporating various attributes present in news items or tweets like source, username handles, URL domains and authors as statistical feature. Our proposed framework have also quantified reliable predictive uncertainty along with proper class output confidence level for the classification task. We have evaluated our results on the COVID-19 Fake News dataset and FakeNewsNet dataset to show the effectiveness of the proposed algorithm on detecting fake news in short news content as well as in news articles. We obtained a best F1-score of 0.9892 on the COVID-19 dataset, and an F1-score of 0.9156 on the FakeNewsNet dataset. [ABSTRACT FROM AUTHOR]

This paper reconsiders transport inequities through the lens of environmental racism. Based on participant observations of 1972 rural-to-urban migrants at 76 worksites and 25 residential communities in five cities in the Yangtze Delta Region, China, we identified two main challenges facing migrants experiencing ethnic discrimination during Covid-19. First, they were more likely to experience housing eviction and, consequently, bear heavier transport burdens when moving. Second, they were more likely to face difficulties when returning to the cities, such as repeated quarantine and displacement, long-time drifting on the highway and transport-related job uncertainty. Although the long-term effects of these policies on migrants' everyday activity-travel behaviour may be limited, their experiences during the early phase of Covid-19 had a significant impact on their Spring Festival homecoming the following year. Regionally targeted transport policies to prevent Covid-19 have fuelled ethnic discrimination by officially classifying people from some provinces as "dangerous". Moreover, transport policies favoured some ethnic groups over others, contributing to environmental racism and exacerbating transport inequity. • Participant observation in 5 cities in the Yangtze Delta Region. • Two main challenges migrants faced during Covid-19: housing instability and transport burdens it engendered, and barriers to returning to the cities. • Experiences during the early phase of the Covid-19 outbreak influence rural-urban migrant workers' Spring Festival homecoming in the long term. • Transport policies implemented in the early phase of the pandemic fuelled environmental racism and exacerbated transport inequity. [ABSTRACT FROM AUTHOR]

In this paper, we shed the light on Beirut's blast that took place in the coronavirus disease 2019 (COVID-19) era. An explosion that ripped the heart of Beirut, it produced a destructive shock wave that left thousands of casualties and people homeless. This explosion, which had a mushroom-like cloud appearance similar to that of Hiroshima and Nagasaki, was described as the third-biggest explosion in human history. It was a blast that not only destroyed lives but also fell as a heavy burden on the shoulders of a country that was suffering from unprecedented economic crisis on top of the COVID-19 pandemic. Facing all this, health care providers were the first line of defense in what looked like an impossible mission. We seek to share with the medical community our experience and the challenges we faced, as a neurosurgery team, during this event, particularly that we were short of basic medical equipment as well as intensive care unit beds since we were in the middle of an economic crisis and the peak of the COVID-19 pandemic. This prohibited us from delivering proper care, whether in the triage of patients or in the operating room, as well as postoperative care. Now, 1 year after this sad event, we revisit the whole situation and examine all the pitfalls that could have been avoided. Thus, we discuss the importance of initiating a disaster response, in particular the neurosurgical emergency response, to be better prepared to face future potential events. The rate-limiting step in such disasters is definitely a well-prepared trained team with a prompt and fast response. And, since time is brain, then what saves the brain is proper timing. [ABSTRACT FROM AUTHOR]

• There are significant satisfaction differences in bus traveling before and after COVID-19. • COVID-19 has severely affected satisfaction with bus traveling of low-income individuals. • Low-income individuals' satisfaction with bus traveling is determined by socioeconomic attributes rather than traveling attributes after COVID-19. • Risk concern has a significant positive impact on risk perception, but risk concern and risk perception have only a minor impact on satisfaction before and after COVID-19. • There is a significant relationship between satisfaction levels and modes of traveling alone and traveling with companions by bus. Although all cities in China have promulgated public transportation control measures to choke off the spread of COVID-19, it also has brought severe changes to low-income individuals' bus traveling. However, the study focusing on satisfaction differences in bus traveling before and after COVID-19 is far under-researched, this paper therefore explored satisfaction differences among low-income individuals under socioeconomic attributes, traveling attributes, and psychological attributes by using the data consist of interviews addressed to 930 individuals in Taiyuan, China. Furthermore, the relationship between satisfaction levels and modes of traveling alone and traveling with companions by bus has also been deeply analyzed to reduce single-person driving problem. As a result, many exciting phenomena were discovered: the significant factors affecting low-income individuals' satisfaction occur "shift" on a large scale after COVID-19; risk concern has a significant positive impact on risk perception, but risk concern and risk perception have only a minor impact on satisfaction before and after COVID-19; it was found that there is a significant relationship between satisfaction levels and modes of traveling alone and traveling with companions by bus. Understanding them can be a reference for improving the travel environment between low-income individuals. [ABSTRACT FROM AUTHOR]

As an essential sub-network of the global liner shipping network, China's international liner shipping network was the earliest to be affected by the COVID-19 and also had a significant impact on the global shipping network. This paper uses Automatic Identification System (AIS) data to analyze the impact of COVID-19 on the typical route networks and major ports of China's international liner shipping. On this basis, the changes in network efficiency and connectivity under the failure of important nodes is simulated. The research finds that, during the epidemic period, the scale of China's international liner shipping network increased, with more routes gathering at fewer hub ports. Still, the overall connectivity and connection strength declined. Meanwhile, the epidemic caused fluctuations in container volume and the mismatch of ship cargo capacity supply, in which China-U.S. routes was the most prominent. From the view of node, the competitiveness of China's mainland ports was significantly promoted during the epidemic. In addition, ports such as Busan, Singapore, and Hong Kong substantially impacted China's international liner shipping network. The current study might be helpful for the industry management departments and related companies to prepare contingency plans, thus enhancing the resilience of the logistics chain and ensuring the stability of the global supply chain. [ABSTRACT FROM AUTHOR]

• The pandemic-crisis effects on Brazilian manufacturing industries are investigated. • An input-output model is employed to address its sectorial composition and linkages. • The pandemic-crisis has further harmful effects on knowledge-intensive industries. • EIHC promotes high-tech industries, while it showed higher degree of import dependence. • COVID-19 may be crucial to rethink industrial policy space within societal purposes. The COVID-19 crisis represents a turning point to rethink industrial policy spaces and countries' productive autonomy, especially for developing economies, such as Brazil, that call for new ways of thinking about manufacturing within societal purposes. Using an input-output model, the paper explores the pandemic-crisis-effects in the case of the Brazilian manufacturing sector. We find that the pandemic-crisis has harmful effects on the Brazilian productive structure, revealing the dependence on imports of the Brazilian Health System. Reductions in manufacturing gross output and value-added are mainly seen in knowledge-intensive subsectors, followed by job losses and tax revenue reduction. We suggest that the pandemic points to some roads to reindustrialization and resilience, given the reorganization of international production networks and the growing dependence on imports in key manufacturing sectors. Thus, the potential role of the manufacturing sector to achieve inclusive and sustainable growth reveals the importance of building productive capacity beyond the pandemic. [ABSTRACT FROM AUTHOR]

Uncertainty surrounding the risk of developing and dying from Thrombosis and Thrombocytopenia Syndrome (TTS) associated with the AstraZeneca (AZ) COVID-19 vaccine may contribute to vaccine hesitancy. A model is urgently needed to combine and effectively communicate evidence on the risks versus benefits of the AZ vaccine. We developed a Bayesian network to consolidate evidence on risks and benefits of the AZ vaccine, and parameterised the model using data from a range of empirical studies, government reports, and expert advisory groups. Expert judgement was used to interpret the available evidence and determine the model structure, relevant variables, data for inclusion, and how these data were used to inform the model. The model can be used as a decision-support tool to generate scenarios based on age, sex, virus variant and community transmission rates, making it useful for individuals, clinicians, and researchers to assess the chances of different health outcomes. Model outputs include the risk of dying from TTS following the AZ COVID-19 vaccine, the risk of dying from COVID-19 or COVID-19-associated atypical severe blood clots under different scenarios. Although the model is focused on Australia, it can be adapted to international settings by re-parameterising it with local data. This paper provides detailed description of the model-building methodology, which can be used to expand the scope of the model to include other COVID-19 vaccines, booster doses, comorbidities and other health outcomes (e.g., long COVID) to ensure the model remains relevant in the face of constantly changing discussion on risks versus benefits of COVID-19 vaccination. [ABSTRACT FROM AUTHOR]

With the Coronavirus disease 2019 (COVID-19) spread, causing a world pandemic, and recently, the virus new variants continue to appear, making the situation more challenging and threatening, the visual assessment and quantification by expert radiologists have become costly and error-prone. Hence, there is a need to propose a model to predict the COVID-19 cases at the earliest possible to control the disease spread. In order to assist the medical professionals and reduce workload and the time the COVID-19 diagnosis cycle takes, this paper proposes a novel neural network architecture termed as O-Net to automatically segment chest Computerised Tomography Ct-scans infected by COVID-19 with optimised computing power and memory occupation. The O-Net consists of two convolutional autoencoders with an upsampling channel and a downsampling channel. Experimental tests show our proposal's effectiveness and potential, with a dice score of 0.86, pixel accuracy, precision, specificity of 0.99, 0.99, 0.98, respectively. Performance on the external dataset illustrates generalisation and scalability capabilities of the O-Net model to Ct-scan obtained from different scanners with different sizes. The second objective of this work is to introduce our virtual reality platform, COVIR, that visualises and manipulates 3D reconstructed lungs and segmented infected lesions caused by COVID-19. COVIR platform acts as a reading and visualisation support for medical practitioners to diagnose COVID-19 lung infection. The COVIR platform could be used for medical education professional practice and training. It was tested by Thirteen participants (medical staff, researchers, and collaborators), they conclude that the 3D VR visualisation of segmented Ct-Scan provides an aid diagnosis tool for better interpretation. [Display omitted] • Firstly, we develop a novel neural network architecture O-Net to automatically segment lung lesions of chest Ct-scans infected by COVID-19 with optimised computing power and memory occupation. • Secondly, the proposed O-Net architecture is based on U-Net, inspired by the Ki-U-Net double convolutional channels. It newly consists of two convolutional autoencoders with an upsampling channel and a downsampling channel. The O-Net enhanced the U-Net performance based on experimental results. • Finally, a Virtual Reality platform is designed and deployed, allowing the 3D lungs and infected regions visualisation and 3D interaction. Thirteen participants (medical staff, researchers, and collaborators) volunteered to investigate our COVIR platform and provide their opinions through user experience. [ABSTRACT FROM AUTHOR]

• COVID-19 has led to drastic changes in people's mobility and lifestyle. • The transformation due to COVID-19 may account for the potentially disruptive impacts on urban and transportation planning. • Recognizing the adverse effects of COVID-19 is crucial in mitigating the negative behavioral changes. • The major sources of impacts on mobility decisions are reviewed and discussed. The outbreak of SARS-COV-2 has led to the COVID-19 pandemic in March 2020 and caused over 4.5 million deaths worldwide by September 2021. Besides the public health crisis, COVID-19 affected the global economy and development significantly. It also led to changes in people's mobility and lifestyle during the COVID-19 pandemic. In addition to short-term changes, the drastic transformation of the world may account for the potentially disruptive long-term impacts. Recognizing the adverse effects of the COVID-19 pandemic is crucial in mitigating the negative behavioral changes that directly relate to people's psychological and social well-being. It is important to stress that citizens and governments face an uncertain situation since nobody knows exactly how the viruses and cures will develop. Better understanding of uncertainties and evaluating behavioral changes contribute to addressing the future of urban development, public transportation, and behavioral strategies to tackle COVID-19 negative consequences. The major sources of impacts on short-term (route, departure time, mode, teleshopping, and teleworking) and medium and long-term (car ownership, work location, choice of job, and residential location) mobility decisions are mostly reviewed and discussed in this paper. [ABSTRACT FROM AUTHOR]

Using daily data on the coronavirus disease 2019 (COVID-19) cases from China and the rest of the world, this paper investigates the corresponding effects on the global economic activity. The empirical results based on a structural vector autoregression model using crude oil prices (COP) and the Baltic Exchange Dry Index (BDI) are consistent with increases in COVID-19 cases acting as negative demand shocks in the global economic activity (reflected as reductions in COP) and negative supply shocks in the global transportation of commodities (reflected as increases in BDI). The historical decomposition results further suggest that the effects of COVID-19 cases on COP and BDI have been mostly observed in the early COVID-19 period. [ABSTRACT FROM AUTHOR]

• A cross-country analysis identifying risk and protective factors on the COVID-19 deaths. • Ranking of 100 countries based on the proposed pandemic containment effectiveness (PCE) score. • Positive relationship between the PCE score and economic growth in major economies. • A counterfactual analysis discussing the tradeoff between the PCE scores and GDP growth. This paper studies whether containing COVID-19 pandemic by stringent strategies deteriorates or saves economic growth. Since there are country-specific factors that could affect both economic growth and deaths due to COVID-19, we first start with a cross-country analysis on identifying risk and protective factors on the COVID-19 deaths using large across-country variation. Using data on 100 countries from 3 January to 27 November 2020 and taking into account the possibility of underreporting, we find that for deaths per million population, GDP per capita, population density, and income inequality are the three most important risk factors; government effectiveness, temperature, and hospital beds are the three most important protective factors. Second, inspired by the stochastic frontier literature, we construct a measure of pandemic containment effectiveness (PCE) after controlling for country-specific factors and rank countries by their PCE scores for deaths. Finally, by linking the PCE score with GDP growth data in Quarters 2 and 3 of 2020, we find that PCE is positively associated with economic growth in major economies. Countries with average PCE scores, such as Malaysia, would gain more GDP growth by 3.47 percentage points if they could improve their PCE scores for deaths to South Korea's level in Q2 of 2020. Therefore, there is not a trade-off between lives and livelihood facing by governments. Instead, to save economy, it is important to contain the pandemic first. Our conclusion is also mainly valid for infections due to COVID-19. [ABSTRACT FROM AUTHOR]

• Analysis of 275-day long time series of county level COVID-19 data in California state. • Multiple change point estimation in the framework of mixture modeling and model-based clustering. • Change point given by gap between logit transformed segments in negative binomial nonhomogeneous Levy process. • Three geographically meaningful clusters, each with several change points indicating the spread and decline of infection. In this paper, a model-based clustering algorithm relying on a finite mixture of negative binomial Lévy processes is proposed. The algorithm models heterogeneous stochastic count process data and automatically estimates multiple change points upon fitting the mixture model. Such change point estimation identifies time points when deviation from the standard process has occurred and serves as an important diagnostic tool for analyzing temporal data. The proposed model is applied to the COVID-positive ICU cases in the state of California with very interesting results. [ABSTRACT FROM AUTHOR]

The COVID-19 has become a global pandemic that dramatically impacted human lives and economic activities. Due to the high risk of getting affected in high-density population areas and the implementation of national emergency measures under the COVID-19 pandemic, both travel and transportation among cities become difficult for engineers and equipment. Consequently, the costly physical commissioning of a new manufacturing system is greatly hindered. As an emerging technology, digital twins can achieve semi-physical simulation to avoid the vast cost of physical commissioning of the manufacturing system. Therefore, this paper proposes a digital twins-based remote semi-physical commissioning (DT-RSPC) approach for open architecture flow-type smart manufacturing systems. A digital twin system is developed to enable the remote semi-physical commissioning. The proposed approach is validated through a case study of digital twins-based remote semi-physical commissioning of a smartphone assembly line. The results showed that combining the open architecture design paradigm with the proposed digital twins-based approach makes the commissioning of a new flow-type smart manufacturing system more sustainable. [ABSTRACT FROM AUTHOR]

This paper shows how existing methods of time series analysis and modeling can be exploited in novel ways to monitor and forecast the COVID-19 epidemic. In the past, epidemics have been monitored by various statistical and model metrics, such as evaluation of the effective reproduction number, R (t). However, R (t) can be difficult and time consuming to compute. This paper suggests two relatively simple data-based metrics that could be used in conjunction with R (t) estimation and provide rapid indicators of how the epidemic's dynamic behavior is progressing. The new metrics are the epidemic rate of change (RC) and a related state-dependent response rate parameter (RP), recursive estimates of which are obtained from dynamic harmonic and dynamic linear regression (DHR and DLR) algorithms. Their effectiveness is illustrated by the analysis of COVID-19 data in the UK and Italy. The paper also shows how similar methodology, combined with the refined instrumental variable method for estimating hybrid Box–Jenkins models of linear dynamic systems (RIVC), can be used to relate the daily death numbers in the Italian and UK epidemics and then provide 15-day-ahead forecasts of the UK daily death numbers. The same approach can be used to model and forecast the UK epidemic based on the daily number of COVID-19 patients in UK hospitals. Finally, the paper speculates on how the state-dependent parameter (SDP) modeling procedures may provide data-based insight into a nonlinear differential equation model for epidemics such as COVID-19. [ABSTRACT FROM AUTHOR]

The COVID-19 disease continues to cause unparalleled disruption to life and the economy world over. This paper is the second in what will be an ongoing series of analyses of a longitudinal travel and activity survey. In this paper we examine data collected over a period of late May to early June in Australia, following four-to-six weeks of relatively flat new cases in COVID-19 after the initial nationwide outbreak, as many state jurisdictions have begun to slowly ease restrictions designed to limit the spread of the SARS-CoV-2 virus. We find that during this period, travel activity has started to slowly return, in particular by private car, and in particular for the purposes of shopping and social or recreational activities. Respondents indicate comfort with the idea of meeting friends or returning to shops, so authorities need to be aware of potential erosion of social distancing and appropriate COVID-safe behaviour in this regard. There is still a concern about using public transport, though it has diminished noticeably since the first wave of data collection. We see that working from home continues to be an important strategy in reducing travel and pressure on constrained transport networks, and a policy measure that if carried over to a post-pandemic world, will be an important step towards a more sustainable transport future. We find that work from home has been a generally positive experience with a significant number of respondents liking to work from home moving forward, with varying degrees of employer support, at a level above those seen before COVID-19. Thus, any investment to capitalise on current levels of work from home should be viewed as an investment in transport. • Aggregate travel has increased by 50% but is still less than two-thirds of that which occurred prior to COVID-19. • Motor vehicle travel rebounding more than other modes, those planning a return to train and bus intended to do so strongly. • Large increases in activity planned for shopping and social and recreation purposes. • People feel most comfortable with meeting with friends, going to the shops and visiting restaurants. • Working from home continues, and has been largely positive for those who have been able to do so.. • Concern about the risk of COVID-19 has decreased significantly since the initial outbreak. [ABSTRACT FROM AUTHOR]

Railway stations are centers of mass accumulation of people. Additional regulations change the intensity of the flow of visitors and the time of entering the station. Delays become an essential factor affecting the perceived satisfaction with all services at stations. This paper analyzes the impact of the intensity of passenger flow in the key areas of the station (entrance groups) on the satisfaction of visitors with station functioning (by the example of Moscow railway stations). The authors of the paper used methods of observation to measure the current passenger flows at the entrances to the station and also collected secondary data on transport flows at city railway stations. To predict passenger flow, the authors used statistical methods to assess the relationship between variables and regression. The paper used a survey of passengers to analyze satisfaction with the functioning of railway station facilities and assess the acceptable delay time at the entrance/exit to the station. The article substantiates that in the context of current requirements for transport security, an important factor for perceived satisfaction with station services is the forecasting and management of passenger flows and control of time of entering the station. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease 2019 (COVID-19) epidemic that emerged in December 2019 has rapidly evolved in recent months to become a worldwide and ongoing pandemic. Shortage of medical masks remains an unresolved problem. This study aims to investigate the filtration efficiency (FE) of home-made masks that could be used as alternatives for community mitigation of COVID-19. Experimental observational analytic study. The FE of home-made masks and medical masks (as the control) were tested under laminar flow within a scaled air duct system using nebulised NaCl aerosols sized 6–220 nm. The size-resolved NaCl aerosol count was measured using a scanning mobility particle-sizer spectrometer. Home-made masks with an external plastic face shield also underwent a splash test. In addition, the fibre structures of medical masks were studied under an electron microscope after treatment with either 75% alcohol or soap and water at 60 °C. The FE of the home-made masks at 6–200 nm were non-inferior to that of medical masks (84.54% vs 86.94%, P = 0.102). Both types of masks achieved an FE of 90% at 6–89 nm. A significantly higher FE was achieved when one piece of tissue paper was added adjacent to the inner surface of the medical mask than medical mask alone (6–200 nm: 91.64% vs 86.94%, P < 0.0001; 6–89 nm: 94.27% vs 90.54%, P < 0.0001; 90–200 nm: 82.69% vs 73.81%, P < 0.0001). The plastic face shield prevented the home-made mask from fluid splash. The fibre structures of the external surface of medical masks were damaged after treatment with either 75% alcohol or soap and water at 60 °C. The home-made masks in this study, which were made of one piece of tissue paper and two pieces of kitchen towels, layered from face to external, had an FE at 6–200 nm non-inferior to that of medical mask materials, which had a certified FE of ≥95% at 3 μm. In the current COVID-19 pandemic with the shortage of medical masks, these home-made masks combined with an external plastic shield could be used as an alternative to medical masks for community mitigation. In addition, one piece of tissue paper could be placed adjacent to the inner surface of a medical mask to prolong effective lifespan of the medical mask. These demand reduction strategies could be used to reserve medical masks for use in healthcare and certain high-risk community settings, such as symptomatic persons, caregivers and attendees to healthcare institutions. • Our home-made masks have filtration efficiency comparable to medical masks. • They may be used as alternatives in low-risk community settings. • Plastic face shields may be used in situations when social distancing and/or face masks are not feasible. • Decontamination of medical masks with 75% alcohol or soap and water damages the fibres and is not recommended. • Community mitigation measures are an important part of the global efforts in combating COVID-19. [ABSTRACT FROM AUTHOR]

• This paper assesses the early socio-medical response to COVID-19 in Nigeria, with a focus on the first 100 days after the index case. • The incidence of COVID-19 grows steadily in Nigeria, moving from an imported case and elitist pattern to community transmission, spreading across the states of the federation. • The paper presents a concise response framework to highlight the various multisectoral responses to the pandemic. • The response is not without some challenges, including inadequate testing laboratories, under-testing, inadequate palliatives and economic stimuli. • The potential of an overwhelming COVID-19 upsurge is still imminent in Nigeria as the country is attempting to hurriedly open the economy. • A most important lesson is the impossibility of the usual medical tourism among Nigerian elites. The coronavirus disease of 2019 (COVID-19) pandemic shocked the world, overwhelming the health systems of even high-income countries. Predictably, the situation has elicited social and medical responses from the public and governments, respectively. Nigeria recorded an imported case from Italy on February 27, 2020. Hence, this paper assesses the early socio-medical response to COVID-19 in Nigeria in the first 100 days after the index case. The paper employs analytical methods and collates data from various media reports and official sources. The incidence of COVID-19 grew steadily in Nigeria, moving from an imported case and elitist pattern to community transmission. The case fatality stood at 2.8%. The country recorded an upsurge (52% of total cases) in the transmission of COVID-19 during the short period the lockdown was relaxed. This paper presents a concise response framework to highlight some specific multisectoral responses to the pandemic. A combination of social and medical responses to a large extent helped Nigeria curtail the spread of the virus. The potential of overwhelming COVID-19 is still imminent in Nigeria as the country is attempting to hurriedly open the economy, which could sacrifice public health gains for temporary economic gains. [ABSTRACT FROM AUTHOR]

To understand the roles of different transport modes in the spread of COVID-19 pandemic across Chinese cities, this paper looks at the factors influencing the number of imported cases from Wuhan and the spread speed and pattern of the pandemic. We find that frequencies of air flights and high-speed train (HST) services out of Wuhan are significantly associated with the number of COVID-19 cases in the destination cities. The presence of an airport or HST station at a city is significantly related to the speed of the pandemic spread, but its link with the total number of confirmed cases is weak. The farther the distance from Wuhan, the lower number of cases in a city and the slower the dissemination of the pandemic. The longitude and latitude coordinates do not have a significant relationship with the number of total cases but can increase the speed of the COVID-19 spread. Specifically, cities in the higher longitudinal region tended to record a COVID-19 case earlier than their counterparties in the west. Cities in the north were more likely to report the first case later than those in the south. The pandemic may emerge in large cities earlier than in small cities as GDP is a factor positively associated with the spread speed. • This paper explores the factors influencing the COVID-19 spread speed and spatial pattern. • Frequencies of flights and HSTs are significantly associated with the number of cases. • The farther the distance from Wuhan, the lower the number of cases and dissemination speed. [ABSTRACT FROM AUTHOR]

In this paper, a possibilistic bi-objective bi-level programming model is proposed to optimize the management of scarce drugs supply and rationing under difficult situations such as COVID-19 outbreak. Multiple followers and single leader exist in the proposed Stackelberg model in which the followers are divided into two groups, manufacturers and suppliers. The first group has priority over the latter. The competition between these two groups is addressed using a lexicographical procedure. Moreover, the interaction between the individuals in each group is incorporated through a normalized Nash equilibrium. To avoid the discriminative allocations, the disability-adjusted life years (DALY) index is employed to improve the rationing process. Due to the significance of uncertainty in such a problem, a novel risk-based possibilistic expected value approach is devised to control the mean and risk values of the objective functions. Finally, to validate the proposed solution method, the model is solved by applying Karush–Kuhn–Tucker (KKT) conditions and using the data extracted from a real case study. The results reveal the applicability and usefulness of the proposed possibilistic model compared to the conventional possibilistic approach [ABSTRACT FROM AUTHOR]

Maybe 2021 wasn't so bad after all! It was like 2020, but with COVID vaccines and better access to plastic pipette tips, and clearly, 2022 is getting off to a bad start. Even better, 2021 had some exciting papers in clinical microbiology, which is what is highlighted in this 2021 year in review. None of them are about COVID-19. Of course, there were some interesting discoveries about SARS-CoV-2 and COVID-19, but this review is completely COVID-19 free (almost). [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic is the defining global health crisis of our time. Chest X-Rays (CXR) have been an important imaging modality for assisting in the diagnosis and management of hospitalised Covid-19 patients. However, their interpretation is time intensive for radiologists. Accurate computer aided systems can facilitate early diagnosis of Covid-19 and effective triaging. In this paper, we propose a fuzzy logic based deep learning (DL) approach to differentiate between CXR images of patients with Covid-19 pneumonia and with interstitial pneumonias not related to Covid-19. The developed model here, referred to as CovNNet , is used to extract some relevant features from CXR images, combined with fuzzy images generated by a fuzzy edge detection algorithm. Experimental results show that using a combination of CXR and fuzzy features, within a deep learning approach by developing a deep network inputed to a Multilayer Perceptron (MLP), results in a higher classification performance (accuracy rate up to 81%), compared to benchmark deep learning approaches. The approach has been validated through additional datasets which are continously generated due to the spread of the virus and would help triage patients in acute settings. A permutation analysis is carried out, and a simple occlusion methodology for explaining decisions is also proposed. The proposed pipeline can be easily embedded into present clinical decision support systems. [ABSTRACT FROM AUTHOR]

Objective: To describe the experience of the ASRM COVID-19 Task Force over the past 2 years and to discuss lessons learned during the pandemic that can be applied to future public health crises.Design: Descriptive narrative.Subjects: None.Intervention: Creation of the ASRM COVID-19 Task Force in March 2020.Main Outcome Measures: None.Results: Effective pandemic management requires a joint effort on the part of physicians, scientists, government agencies, subject area experts and funders.Conclusion: Reproduction is a fundamental human right that should be protected at all times. Advanced preparation for future pandemics should include appointment of a standing group of experts so that a response is both informed and immediate when a public health crisis arises. This approach will help ensure that the ultimate objective - preserving the safety and well-being of patients and health care workers - is fulfilled. The recommendations put forth in this paper from the ASRM's Center for Policy and Leadership can be used as a template to prepare for future public health threats. [ABSTRACT FROM AUTHOR]

Vaccine hesitancy can hinder the successful roll-out of vaccines. This paper examines COVID-19 vaccine hesitancy in the European Union, drawing from a large-scale cross-national survey covering all 27 EU Member States, carried out between February and March 2021 (n = 29,755). We study the determinants of vaccine hesitancy, focusing on the role of social media use. In multivariate regression models, we find statistically significant (p < 0.05) impacts on vaccine hesitancy of heavy use of social media and using social media as a main source of news. However, the effect of social media and the drivers of vaccine hesitancy vary depending on the reason for hesitancy. Most notably, hesitancy due to health concerns is mainly driven by physical health status and less by social media use, while views that COVID-19 risks are exaggerated (or that COVID-19 does not exist) are more common among men, people in good health, and those using social media as their main source of news. [ABSTRACT FROM AUTHOR]

The pandemic caused by coronavirus disease 2019(COVID-19) continues to disrupt the global supply chain system, bringing new risks and challenges. The uncertainty created by COVID-19 makes it is difficult for various industries to deal with the pandemic. Since the pandemic, the supply chain's resilience has been discussed and examined in some studies. However, most existing works start from a single industry perspective or pay more attention to the disturbance caused by changes in the production side. Supply chain networks of different industries, mainly transport networks, are relatively limited under the epidemic's impact. In this paper, from the perspective of highway freight transport, a comprehensive competitiveness evaluation framework was proposed to reveal and the disruption and resilience of the supply chain under the outbreak based on nine indexes with five dimensions, including efficiency, capacity, activity, connectivity, and negotiability. Based on the availability of the data(Large-scale truck trajectory), we sorted out seven categories of Chinese industries(related to highway transport) and divided them into four categories respectively: (a) Slight disruption and worse resilience; (b) Slight disruption and remarkable resilience; (c) Serious disruption and worse resilience; (d) Serious disruption and remarkable resilience. The measurement results of supply chain network performance show that the industries (cold-chain, general products, and other industries) dominated by "Efficiency - Negotiability - Connectivity" are slightly disrupted (about 33%), forming a spatial diffusion with Wuhan(the city where the pandemic first broke out) as the disrupted center, spreading outward in a circle structure. Simultaneously, five urban agglomerations surrounding it have been impacted. By contrast, due to the strict isolation measures, the industries (building materials, construction, engineering, and high-value products industry) more vulnerable to be disrupted seriously (about 82%) tend to be the pattern of "Capacity - Activity". However, a large-scale centralized disruption was observed in the Triangle of Central China urban agglomeration was presented, resulting in almost stagnation of industry development. Meanwhile, as the future of the pandemic remains uncertain, the supply chain represented by the engineering industry, construction industry, etc are deserved to be paid more attention in line with they are prone to large-scale centralized damage due to the disruption of a single city node. • Disruption and resilience of multi-industry supply chain under COVID-19 epidemic. • Multi-dimensional comprehensive competitiveness evaluation framework for supply chain performance. • Supply chain destruction and repair performance of seven industries in China under COVID-19 epidemic. • Characteristic differences of supply chain between different industries from the perspective of highway freight transport. [ABSTRACT FROM AUTHOR]

Various unexpected, low-probability events can have short or long-term effects on organizations and the global economy. Hence there is a need for appropriate risk management practices within organizations to increase their readiness and resiliency, especially if an event may lead to a series of irreversible consequences. One of the main aspects of risk management is to analyze the levels of change and risk in critical variables which the organization's survival depends on. In these cases, an awareness of risks provides a practical plan for organizational managers to reduce/avoid them. Various risk analysis methods aim at analyzing the interactions of multiple risk factors within a specific problem. This paper develops a new method of variability and risk analysis, termed R.Graph, to examine the effects of a chain of possible risk factors on multiple variables. Additionally, different configurations of risk analysis are modeled, including acceptable risk, analysis of maximum and minimum risks, factor importance, and sensitivity analysis. This new method's effectiveness is evaluated via a practical analysis of the economic consequences of new Coronavirus in the electricity industry. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a global pandemic in the last year. Along with major respiratory distress, a myriad of neurological manifestations was also reported to be associated with COVID-19 patients. These cases indicate that SARS-CoV-2 can be considered as an opportunistic pathogen of the brain. SARS-CoV-2 enters the brain through the olfactory bulb, retrograde axonal transport from peripheral nerve endings, or via hematogenous or lymphatic routes. Notably, COVID-19 infection can cause or even present with different neurological features including encephalopathy, impaired consciousness, confusion, agitation, seizure, ataxia, headache, anosmia, ageusia, neuropathies, and neurodegenerative diseases. In this paper, we provide a brief review of observed neurological manifestations associated with COVID-19. [ABSTRACT FROM AUTHOR]

• Different vaccination plans across EU and non-EU Countries, with their own peculiarities. • Italian vaccine campaign: priority groups and national guide lines for vaccine rollout. • Fiera Milano Pavilion is the first experimental mass vaccination site in Lombardy. In occurrence of the coronavirus disease 19 (COVID-19) pandemic, carrying out an efficient large-scale vaccination campaign is vital in order to control the virus. Especially in high prevalence areas of COVID – 19, it is crucial to implement an effective vaccination strategy. In Italy, programming an efficient COVID-19 mass vaccination campaign has been the main target of the Ministry of Health. This paper gives a comprehensive overview of how the mass vaccination campaign is performed in Milan, one of the cities that has been mostly affected by the COVID-19 pandemic in Italy. We analyze the vaccination strategy implemented by Fondazione Ca' Granda – Ospedale Maggiore Policlinico located in Milan. Furthermore, we compare the organization of this campaign in regards of those carried out across EU and UK. The data derive from an analysis of the different vaccination plans implemented across EU and UK from the 27/12/2020 to the 15/06/2020. In addition, we discuss the data collected from the internal data server of IRCCS Fondazione Ca' Granda – Ospedale Maggiore Policlinico from the 15/02/2021 to the 15/06/2021.The collected data are examined by means of descriptive statistics. From the analysis of the internal data server, we observe that the modular organization of Fiera Milano City guarantees up to 5000 vaccinations/day. Moreover, the precise flow organization of users and a series of strategies adopted to avoid identification errors or vaccine type administration errors are crucial to reach the aforementioned target. The institution of mass vaccination centers thanks to the optimization of all the involved processes and the meticulous organization of these structures, allows to avoid crowds and guarantees the administration of elevated amounts of vaccines. All these elements assure a rapid vaccination coverage of the population in Lombardy, with a meaningful increase in daily administration doses. [ABSTRACT FROM AUTHOR]

• Developing a time-fractional compartmental model that incorporates the effect of contact tracing for Covid-19. • Describe the effective reproduction number in terms of observable quantities. • Discuss effects of tracking, reporting and monitoring in contact tracing. • Monitoring proportion of reported cases which must be tracked to ensure that the reproduction number is below one. • Numerical simulations are done to discuss the impact of contact tracing on the pandemic. Contact Tracing (CT) is one of the measures taken by government and health officials to reduce the spread of the novel coronavirus. In this paper, we investigate its efficacy by developing a compartmental model for assessing its impact on mitigating the spread of the virus. We describe the impact on the reproduction number R 0 of COVID-19. In particular, we discuss the importance and relevance of parameters of the model such as the number of reported cases, effectiveness of tracking and monitoring policy, and the transmission rates to contact tracing. We describe the terms "perfect tracking", "perfect monitoring" and "perfect reporting" to indicate that traced contacts will be tracked while incubating, tracked contacts are efficiently monitored so that they do not cause secondary infections, and all infected persons are reported, respectively. We consider three special scenarios: (1) perfect monitoring and perfect tracking of contacts of a reported case, (2) perfect reporting of cases and perfect monitoring of tracked reported cases and (3) perfect reporting and perfect tracking of contacts of reported cases. Furthermore, we gave a lower bound on the proportion of contacts to be traced to ensure that the effective reproduction, R c , is below one and describe R c in terms of observable quantities such as the proportion of reported and traced cases. Model simulations using the COVID-19 data obtained from John Hopkins University for some selected states in the US suggest that even late intervention of CT may reasonably reduce the transmission of COVID-19 and reduce peak hospitalizations and deaths. In particular, our findings suggest that effective monitoring policy of tracked cases and tracking of traced contacts while incubating are more crucial than tracing more contacts. The use of CT coupled with other measures such as social distancing, use of face mask, self-isolation or quarantine and lockdowns will greatly reduce the spread of the epidemic as well as peak hospitalizations and total deaths. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has caused huge and disruptive technological changes in the healthcare sector, transforming the way businesses and societies function. To respond to the global health crisis, there have been numerous innovation projects in the healthcare sector, including the fast design and manufacturing of personal protective equipment (PPE) and medical devices, and testing, treatment, and vaccine technologies. Many of these innovative activities happen beyond organizational boundaries with collaboration and open innovation. In this paper, we review the current literature on open innovation strategy during the pandemic and adopt the co-evolution view of business ecosystems to address the context of change. Based on a detailed exploration of the COVID-19-related technologies in the UK and global healthcare sectors, we identify the key emerging themes of open innovation in crisis. Further discussions are conducted in relation to each theme. Our results and analysis can help provide policy recommendations for the healthcare sector, businesses, and society to recover from the crisis. [ABSTRACT FROM AUTHOR]

Nucleocapsid protein (N protein) is the primary antigen of the virus for development of sensitive diagnostic assays of COVID-19. In this paper, we demonstrate the significant impact of dimerization of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) N-protein on sensitivity of enzyme-linked immunosorbent assay (ELISA) based diagnostics. The expressed purified protein from E. coli is composed of dimeric and monomeric forms, which have been further characterized using biophysical and immunological techniques. Indirect ELISA indicated elevated susceptibility of the dimeric form of the nucleocapsid protein for identification of protein-specific monoclonal antibody as compared to the monomeric form. This finding also confirmed with the modelled structure of monomeric and dimeric nucleocapsid protein via HHPred software and its solvent accessible surface area, which indicates higher stability and antigenicity of the dimeric type as compared to the monomeric form. The sensitivity and specificity of the ELISA at 95% CI are 99.0% (94.5–99.9) and 95.0% (83.0–99.4), respectively, for the highest purified dimeric form of the N protein. As a result, using the highest purified dimeric form will improve the sensitivity of the current nucleocapsid-dependent ELISA for COVID-19 diagnosis, and manufacturers should monitor and maintain the monomer-dimer composition for accurate and robust diagnostics. [Display omitted] [ABSTRACT FROM AUTHOR]

• Aging affects the ability of human immune system to fight infections and neoplasms. • All components of the innate and adaptive immune system are affected. • Major mechanisms of immune system aging are immunosenescence and inflammaging. • Immunosenescence and inflammaging lead to aging-related chronic inflammatory diseases. • Immunosenescence and inflammaging decide about severity of COVID-19. The interest in the process of aging, and specifically in how aging affects the working of our immune system, has recently enormously grown among both specialists (immunologists and gerontologists) and representatives of other disciplines of health sciences. An obvious reason for this interest is the current pandemics of COVID-19, known to affect the elderly more than younger people. In this paper current knowledge about mechanisms and complex facets of human immune system aging is presented, stemming from the knowledge about the working of various parts of the immune system, and leading to understanding of immunological mechanisms of chronic, inflammatory, aging-related diseases and of COVID-19. [ABSTRACT FROM AUTHOR]

Various unexpected, low-probability events can have short or long-term effects on organizations and the global economy. Hence there is a need for appropriate risk management practices within organizations to increase their readiness and resiliency, especially if an event may lead to a series of irreversible consequences. One of the main aspects of risk management is to analyze the levels of change and risk in critical variables which the organization's survival depends on. In these cases, an awareness of risks provides a practical plan for organizational managers to reduce/avoid them. Various risk analysis methods aim at analyzing the interactions of multiple risk factors within a specific problem. This paper develops a new method of variability and risk analysis, termed R.Graph, to examine the effects of a chain of possible risk factors on multiple variables. Additionally, different configurations of risk analysis are modeled, including acceptable risk, analysis of maximum and minimum risks, factor importance, and sensitivity analysis. This new method's effectiveness is evaluated via a practical analysis of the economic consequences of new Coronavirus in the electricity industry. [ABSTRACT FROM AUTHOR]

Purpose: The establishment of community-academic partnerships to digest data and create actionable policy and advocacy steps is of continuing importance. In this paper, we document COVID-19 racial and geographic disparities uncovered via a collaboration between a local health department and university research center.Methods: We leverage individual level data for all COVID-19 cases aggregated to the census block group level, where group-based trajectory modeling was employed to identify latent patterns of change and continuity in COVID-19 diagnoses.Results: Linking with socioeconomic data from the census, we identified the types of communities most heavily affected by each of Michigan's two waves (in spring and fall of 2020). This includes a geographic and racial gap in COVID-19 cases during the first wave, which is largely eliminated during the second wave.Conclusions: Our work has been extremely valuable for community partners, informing community-level response toward testing, treatment, and vaccination. In particular, identifying and conducting advocacy on the sizeable racial disparity in COVID-19 cases during the first wave in spring 2020 helped our community nearly eliminate disparities throughout the second wave in fall 2020. [ABSTRACT FROM AUTHOR]

Vaccine discovery and vaccination against preventable diseases are one of most important achievements of the human race. While medical, scientific & technological advancements have kept in pace and found their way into treatment options for a vast majority of diseases, vaccines as a prevention tool in the public health realm are found languishing in the gap between such innovations and their easy availability/accessibility to vulnerable populations. This paradox has been best highlighted during the unprecedented crisis of the COVID-19 pandemic. As part of a two series publication on the vaccine industry's view on how to accelerate the availability of vaccines worldwide, this paper offers a deep dive into detailed proposals to enable this objective. These first-of-its-kind technical proposals gleaned from challenges and learnings from the COVID-19 pandemic are applicable to vaccines that are already on the market for routine pathogens as well as for production of new(er) vaccines for emerging pathogens with a public health threat potential. The technical proposals offer feasible and sustainable solutions in pivotal areas such as process validation, comparability, stability, post-approval changes, release testing, packaging, genetically modified organisms and variants, which are linked to manufacturing and quality control of vaccines. Ultimately these proposals aim to ease high regulatory complexity and heterogeneity surrounding the manufacturing & distribution of vaccines, by advocating the use of (1) Science and Risk based approaches, (2) global regulatory harmonization, (3) use of reliance, work-sharing, and recognition processes and (4) digitalization. Capitalizing & collaborating on such new-world advancements into the science of vaccines will eventually benefit the world by turning vaccines into vaccination, ensuring the health of everyone. [ABSTRACT FROM AUTHOR]

Background: This systematic review and meta-analysis aim to synthesize the extant literature reporting the effects of COVID-19 pandemic based on the pooled prevalence of depression among affected populations in Asia Pacific, as well as its risk factors.Method: A systematic review and meta-analysis approach was adopted as per the PRISMA guidelines, targeting articles published in PubMed, Google Scholar and Scopus from January 2021 to March 30, 2021. The screening resulted in 82 papers.Results: The overall pooled depression prevalence among 201,953 respondents was 34% (95%CI, 29-38, 99.7%), with no significant differences observed between the cohorts, timelines, and regions (p > 0.05). Dominant risk factors found were fear of COVID-19 infection (13%), gender (i.e., females; 12%) and deterioration of underlying medical conditions (8.3%), regardless of the sub-groups. Specifically, fear of COVID-19 infection was the most reported risk factor among general population (k = 14) and healthcare workers (k = 8). Gender (k = 7) and increased workload (k = 7) were reported among healthcare workers whereas education disruption among students (k = 7).Limitation: The review is limited to articles published in three electronic databases. Conclusion The pandemic has caused depression among the populations across Asia Pacific, specifically among the general population, healthcare workers and students. Immediate attention and interventions from the concerned authorities are needed in addressing this issue. [ABSTRACT FROM AUTHOR]

As countries across the world modify their travel in the face of the Covid-19 pandemic, the first casualty becomes the public transport sector. Transport authorities across the world have reported about 95% reduction in users during peak COVID-19, decrease of fare box revenues and additional costs for disinfecting and implementing physical distancing measures. The public transport companies in India face a larger crisis as nearly 85% people travelling intercity use public road transport in normal times. In this paper we drawpassenger clusters based on their travel dynamics and develop two frameworks, namely, passenger driven transportation strategy framework and epidemic prevention strategy framework to deal with the COVID-19 induced travel changes. The frameworks use three tenets of mobility, namely, agility, integrated movement, and public based partnership. The strategies aim to enable the transport enterprises to open new windows of travel and efficiencies for the passengers rather than restricting access and choices. However, security remains fundamental to making these new and innovative service changes possible. [ABSTRACT FROM AUTHOR]

The global outbreak of COVID-19 has further exposed deficiencies in city logistics based on human and ground roads, such as poor emergency response capacity and high risk of infection during transportation. Metro-based underground logistics system (M-ULS) may be an innovative approach to deal with this city-level disaster due to its efficient operation, contactless and driverless characteristics. However, the market evolution process and the quantitative calculation framework of comprehensive benefits after the application of M-ULS are still unclear, which has become a problem of mutual restriction with the extensive application of M-ULS. This paper attempts to use the system dynamics method, based on the real-world simulation, to analyze the quantitative relationship between the M-ULS implementation and the city logistics performance under epidemic outbreaks. Wuhan city in China was selected as the empirical background, and five simulation scenarios were set under different implementation strategies of M-ULS in response to the epidemic. Six variables were selected to measure city logistics performance and M-ULS operation status, including demand fill-rate, unit delivery time, total deprivation cost, total transportation cost, total number of susceptible people, and utilization rate of M-ULS. The results show that M-ULS is effective in improving the performance of city logistics and responding to the epidemic. The delivery time and transportation cost have a strong impact on the market share of M-ULS. Finally, a set of incentive policies was designed to promote the adoption of M-ULS. The findings not only provide a method for evaluating the overall performance of M-ULS, but also provide a unique perspective for promoting the implementation of M-ULS and responding to the transportation challenges brought by the epidemic. • M-ULS is introduced to break through the plight of city logistics. • A set of effective macro-micro incentive policies are established to promote the implementation of M-ULS. • The delivery time, transportation cost and government subsidies play different roles in the market penetration of M-ULS. • The benefits of M-ULS have been analyzed. [ABSTRACT FROM AUTHOR]

• COVID-19 has significantly impacted on the foot and ankle trauma activity in the UK. • The overall COVID-19 infection rate across 6644 patients was 0.53% (35 patients). • The cumulative percentage of COVID-19 infection across the audit was 1.37%. • Both the COVID-19 infection rate and timing of the peaks of COVID-19 infection differed significantly across the country. • National surgical activity significantly reduced for all cases during lockdown, however in the post-lockdown period there was normalisation of activity in trauma and diabetic surgery. This paper details the impact of COVID-19 on foot and ankle activity in the UK. It describes regional variations and COVID-19 infection rate in patients undergoing foot and ankle surgery before, during and after the first national lock-down. This was a multicentre, retrospective, UK-based, national audit on foot and ankle patients who underwent surgery between 13th January and 31st July 2020. Data was examined pre- UK national lockdown, during lockdown (23rd March to 11th May 2020) and post-lockdown. All adult patients undergoing foot and ankle surgery in an operating theatre during the study period included from 43 participating centres in England, Scotland, Wales and Northern Ireland. Regional, demographic and COVID-19 related data were captured. 6644 patients were included. In total 0.53% of operated patients contracted COVID-19 (n = 35). The rate of COVID-19 infection was highest during lockdown (2.11%, n = 16) and lowest after lockdown (0.16%, n = 3). Overall mean activity during lockdown was 24.44% of pre-lockdown activity with decreases in trauma, diabetic and elective foot and ankle surgery; the change in elective surgery was most marked with only 1.73% activity during lock down and 10.72% activity post lockdown as compared to pre-lockdown. There was marked regional variation in numbers of cases performed, but the proportion of decrease in cases during and after lockdown was comparable between all regions. There was also a significant difference between rates of COVID-19 and timing of peak, cumulative COVID-19 infections between regions with the highest rate noted in South East England (3.21%). The overall national peak infection rate was 1.37%, occurring during the final week of lockdown. General anaesthetic remained the most common method of anaesthesia for foot and ankle surgery, although a significant increase in regional anaesthesia was witnessed in the lock-down and post-lockdown periods. National surgical activity reduced significantly for all cases across the country during lockdown with only a slow subsequent increase in elective activity. The COVID-19 infection rate and peaks differed significantly across the country. [ABSTRACT FROM AUTHOR]