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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has become pandemic, reaching almost one million death worldwide. At present standard treatment for coronavirus disease 2019 (COVID-19) is not well defined because the evidence, either from randomized or observational studies, with conflicting results, has led to rapid changes in treatment guidelines. Our aim was to narratively summarize the available literature on the management of COVID-19 in order to combine current evidence and interpretation of the data by experts who are treating patients in the frontline setting. The panel conducted a detailed review of the literature and eventual press releases from randomized clinical trials for each possible available treatment. Inductive PubMed search waws performed for publications relevant to the topic, including all clinical trials conducted. The result was a flowchart with treatment indications for patients with COVID-19. After 6 months of a pandemic situation and before a possible second coronavirus wave descends on Europe, it is important to evaluate which drugs proved to be effective while also considering that results from many randomized clinical trials are still awaited. Indeed, among treatments for COVID-19, only glucocorticoids have resulted in an association with a significant decrease in mortality in published randomized controlled trials. New therapeutic strategies are urgently needed. [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]

Background: The current COVID-19 pandemic has greatly changed the way surgery is delivered. In particular, current guidelines and policies have highlighted the need to use high level Personal Protective Equipment to reduce the risk of viral infection during open and laparoscopic surgical procedures. In particular, it was felt that the laparoscopic approach was at higher risk of viral transmission due to the chimney effect of the smoke escape from the trocars during and after the procedure. However, with this being a new and largely unknown viral agent, guidelines have been based on speculation and extrapolation from previous studies conducted in completely different situations, and led to anxiety amongst surgeons and theatre staff. We decided to conduct a systematic review of the Literature to try to clarify whether inhalation of surgical smoke can increase the risk of COVID-19 infection.Methods: A thorough search of the relevant Literature was performed following the PRISMA guidelines and the most relevant papers on this topic were selected for qualitative analysis. Duplicates, review, personal opinions and guidelines have been excluded. Quantitative analysis has not been performed due to the lack of homogeneous high-quality studies.Results: Literature search identified 740 papers but only 34 of them were suitable for qualitative analysis. The quality of those studies is generally quite low. We were not able to find any evidence directly linking surgical smoke with viral transmission, other than in patients with active HPV infection.Discussion: Inhalation of surgical smoke can be generally hazardous, and therefore the use of PPE during surgical operations must be recommended in any case. However, the present systematic review of the existent Literature did not identify any significant evidence of the risk of viral transmission with the surgical smoke, therefore the current guidelines restricting the use of laparoscopy and/or diathermy during the current Covid-19 pandemic may be considered excessive and non-evidence based. [ABSTRACT FROM AUTHOR]

• Russia demonstrates low vaccination intentions compared to most European countries and some emerging economies. • The paper uses a nationally representative dataset for Russia (RLMS HSE-2020) to analyze the scope of COVID-19 vaccination hesitancy and resistance prior to the start of the vaccination campaign. • 43% of the Russian adult population were resistant towards vaccination, while 13% were hesitant. • Vaccination attitudes depend on age, family composition, education, type of settlement, employment, self-rated health, previous COVID-19 experience, and self-perceived risk of being infected. • Personality traits, risk attitudes, and trust, reflect the deep-rooted causes of vaccination attitudes and strongly predict vaccination intentions. Vaccination is the main tool available to handle the COVID-19 pandemic globally. Though no vaccine is proven to be 100% effective, vaccination secures against getting seriously ill and dying from the disease. Russia announced the development of its first domestic vaccine back in August 2020 and launched the nationwide immunization campaign at the beginning of 2021. Despite these achievements, as of mid-October 2021, only 36% of the population got at least one shot of the vaccine. Massive vaccination hesitancy and refusal pose a great threat to public health and postpone social and economic recovery. Using nationally representative data from the general adult population of Russia, this study explores the scope of vaccination hesitancy and refusal as well as the determinants of vaccination attitudes. The results suggest that only 45% of the Russian population demonstrated positive attitudes towards the COVID-19 vaccination prior to the launch of a nationwide vaccination program. We analyze a wide array of demographic, socio-economic, and health-related factors in relation to vaccination intentions and explore the deep-rooted causes of vaccination reluctance by looking at personality traits, religiosity, and trust. The obtained results are vital for designing policy measures to promote vaccination. [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]

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]

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]

Background: This review provides an estimate of the global prevalence of depression and anxiety symptoms among doctors, based on analysis of evidence from the first year of the COVID-19 pandemic.Methods: A systematic review was conducted to identify suitable studies. Final searches were conducted on 3rd March 2021. Papers were initially screened by title and abstract, based on pre-agreed inclusion criteria, followed by full-text review of eligible studies. Risk of bias was assessed using the Joanna Briggs Checklist for Prevalence Studies. Data from studies rated as low or medium risk of bias were pooled using a random-effects meta-analysis. Sensitivity and subgroup analyses were conducted to explore heterogeneity.Results: Fifty-five studies were included after full-text review. Of these, thirty studies were assessed as low or medium risk of bias and were included in primary analyses. These comprised twenty-six studies of depression (31,447 participants) and thirty studies of anxiety (33,281 participants). Pooled prevalence of depression and anxiety was 20.5% (95% CI 16.0%-25.3%) and 25.8% (95% CI 20.4%-31.5%) respectively.Interpretation: Evidence from the first year of the pandemic suggests that a significant proportion of doctors are experiencing high levels of symptoms of depression and anxiety, although not conclusively more so than pre-pandemic levels. Differences in study methodology and variation in job demands may account for some of the observed heterogeneity.Limitations: Findings must be interpreted with caution due to the high heterogeneity and moderate risk of bias evident in the majority of included studies. [ABSTRACT FROM AUTHOR]

There has been widespread interest in the potential for the significant behavioural and policy adaptations rendered necessary by Coronavirus to act as a catalyst for radical longer term policy change in transport. However, this body of work to date has been limited in its consideration of how such policy change might be brought about. Translating the lessons from the Coronavirus response to other ongoing strategic challenges such as decarbonisation requires analysis of what the pandemic has revealed about processes of policy formulation and how institutions responsible for policy implementation actually work. This paper explores the extent to which rapid policy change has actually been possible in the transport sector in England and Scotland during the pandemic, and key examples of how such change has been both achieved and blocked. Two rounds of interviews with senior stakeholders from across the transport sector were undertaken in June and November 2020 to inform the analysis. We find that the pandemic has accelerated some policy commitments that were already planned, but at a time of huge stress on the whole of government and its partner delivery organisations, the potential to deliver radical policy adaptation was limited. However, Coronavirus is recognised as being a potentially path-changing disruption to existing trajectories in terms of the adaptations to business practices, industry structures, ways of working and the public finances. Paradoxically, whilst recognising these uncertainties, decision-makers are yet to deviate from pre-pandemic planning assumptions and policy plans and this risks missing the opportunities to steer how those changes unfold. • Addresses research gap in the study of governance responses during disruptions. • Identifies transport as a distant policy sub-sector in a crisis that affected all parts of government. • Finds that the response to the pandemic has accelerated already planned policies but generated few new policies. • Long-term impacts on the future structure of cities, use of public transport and uptake of active travel could still emerge. • The leading strategic thinkers on transport policy are still unsure what kinds of future demand to plan for. [ABSTRACT FROM AUTHOR]

[Display omitted] The recent ongoing outbreak of coronavirus disease 2019 (COVID-19), still is an unsolved problem with a growing rate of infected cases and mortality worldwide. The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is targeting the angiotensin-converting enzyme 2 (ACE2) receptor and mostly causes a respiratory illness. Although acquired and resistance immunity is one of the most important aspects of alleviating the trend of the current pandemic; however, there is still a big gap of knowledge regarding the infection process, immunopathogenesis, recovery, and reinfection. To answer the questions regarding "the potential and probability of reinfection in COVID-19 infected cases" or "the efficiency and duration of SARS-CoV-2 infection-induced immunity against reinfection" we critically evaluated the current reports on SARS-CoV-2 immunity and reinfection with special emphasis on comparative studies using animal models that generalize their finding about protection and reinfection. Also, the contribution of humoral immunity in the process of COVID-19 recovery and the role of ACE2 in virus infectivity and pathogenesis has been discussed. Furthermore, innate and cellular immunity and inflammatory responses in the disease and recovery conditions are reviewed and an overall outline of immunologic aspects of COVID-19 progression and recovery in three different stages are presented. Finally, we categorized the infected cases into four different groups based on the acquired immunity and the potential for reinfection. In this review paper, we proposed a new strategy to predict the potential of reinfection in each identified category. This classification may help to distribute resources more meticulously to determine: who needs to be serologically tested for SARS-CoV-2 neutralizing antibodies, what percentage of the population is immune to the virus, and who needs to be vaccinated. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has changed the way we go about our daily lives in ways that are unlikely to return to the pre-COVID-19 levels. A key feature of the COVID-19 era is likely to be a rethink of the way we work and the implications this may have on commuting activity. Working from home (WFH) has been the 'new normal' during the period of lockdown, except for essential services that require commuting. In recognition of the new normal as represented by an increasing amount of WFH, this paper develops a model to identify the incidence of WFH and what impact this could have on the amount of weekly one-way commuting trips by car and public transport. Using Wave 1 of an ongoing data collection effort done at the height of the restrictions in March and April 2020 in Australia, we develop a number of days WFH ordered logit model and link it to a zero-inflated Poisson (ZIP) regression model for the number of weekly one-way commuting trips by car and public transport. Scenario analysis is undertaken to highlight the way in which WFH might change the amount of commuting activity when restrictions are relaxed to enable changing patterns of WFH and commuting. The findings will provide one reference point as we continue to undertake similar analysis at different points through time during the pandemic and after when restrictions are effectively removed. [ABSTRACT FROM AUTHOR]

The sanctioning of different coronavirus vaccines (with some approved by regulators for public delivery, and others in the pipeline) has met with relief by many sections of the public and the government. However, partly due to the damages associated with the pandemic and the ensuing euphoria over vaccines' arrival, some of the challenges are mostly being ignored or are not recognized. This paper identifies some pitfalls and drawbacks in vaccine delivery. We argue that the somewhat unique tension between the speed of vaccine delivery and its scale can create opportunities for corrupt behavior that are often at odds with effective means to check abuse. While data on instances of abuse will emerge over time, it is useful to point out different avenues of abuse so that some preventive government actions can be undertaken. Specifically, we argue that the potential for out of turn delivery of vaccines and the stockpiling by unauthorized agents creates incentives for corruption, with the public or bureaucrats initiating corrupt transactions. An understanding of the potential avenues for corruption should guide the formulation of appropriate corruption-control policies and similar challenges that will be faced by policy makers in addressing future pandemics. [ABSTRACT FROM AUTHOR]

The current COVID pandemic is happening while the long-term effects of coronavirus infection remain poorly understood. The present article meta-analyzed mental health outcomes (anxiety, depression, etc.) from a previous coronavirus outbreak in China (2002). CNKI, Wanfang, PubMed/Medline, Scopus, Web of Science, Baidu Scholar, and Google Scholar were searched up to early June 2020 for articles in English or Chinese reporting mental illness symptoms of SARS patients. Main outcome measures include SCL-90, SAS, SDS, and IES-R scales. 29 papers met the inclusion criteria. The longest follow-up time included in the analysis was 46 months. The systematic meta-analysis indicated that mental health problems were most serious before or at hospital discharge and declined significantly during the first 12 months after hospital discharge. Nevertheless, average symptom levels remained above healthy norms even at 12 months and continued to improve, albeit slowly, thereafter. The adverse mental health impact of being hospitalized with coronavirus infection long outlasts the physical illness. Mental health issues were the most serious for coronavirus infected patients before (including) hospital discharge and improved continuously during the first 12 months after hospital discharge. If COVID-19 infected patients follow a similar course of mental health development, most patients should recover to normal after 12 months of hospital discharge. [ABSTRACT FROM AUTHOR]

The new coronavirus disease (COVID-19) is a systemic disease. Mounting evidence depict signs and symptoms involving multiple organs, most of which supported by pathological data. A plausible link to these manifestations is vascular and endothelial dysfunction/damage. However, much of the current knowledge relies on opinion and incipient evidence. We aim to objectively appraise current evidence on the association between COVID-19 and vascular disease, specifically endotheliitis and vasculitis. Two researchers independently entered the search terms COVID-19 OR SARS-CoV-2 AND vasculitis, endotheliitis OR endothelium in the following online platforms: MedRxiv and LitCovid (PubMed). The search period was set from November 1, 2019 to August 28, 2020. Manuscripts with unavailable full texts, not in English, mainly on pre-clinical data, presenting only study designs or not directly related to the topics of this review were excluded. Retrospective and prospective studies, especially longitudinal ones, were given priority to the purpose of this review. Since there was paucity of prospective controlled evidence, case reports/series were also considered. A total of 318 manuscripts were initially found. Sixty-seven (21%) were excluded: 59 (18.5%) met exclusion criteria and 8 (2.5%) were duplicates. One hundred and forty-two manuscripts (44,6%) did not provide original data and were also excluded: 35 (11%) were comments, 108 (33.9%) reviews; 1 (0.3%) position paper. One hundred and seven (33.6%) studies were considered for the present scoping review: 81 (25,5%) case reports/series; 18 (5.7%) prospective; 8 (2.5%) retrospective. Viral inclusions in endothelial cells, mononuclear cell infiltrates in the intima of small vessels and markers of endothelial cell apoptosis were demonstrated. Specificities of COVID-19 may lead to diverse vascular manifestations in different levels of the vascular bed. Evidence indicates that COVID-19 targets vasculature and endothelium. However, high quality data is still lacking and studies with prospective designs and appropriately matched controls are needed. [Display omitted] • The new coronavirus disease (Covid-19) is a systemic disease. • Vascular and endothelial dysfunction/damage at different levels is a plausible link through which the disease can occur. • Endothelial cell damage and apoptosis could lead to basal membrane exposure and a thrombotic macro- and/or microangiopathy. • Endothelial cell dysfunction due to unbalanced angiotensin II could drive vasoconstriction in virtually any affected organ. • Endothelial cells are recognized as part of the innate immune system and could play a role in an exacerbated inflammation. [ABSTRACT FROM AUTHOR]

Infectious disease outbreaks can be distressing for everyone, especially those deemed to be particularly vulnerable such as pregnant women, who have been named a high-risk group in the current COVID-19 pandemic. This paper aimed to summarise existing literature on the psychological impact of infectious disease outbreaks on women who were pregnant at the time of the outbreak. The design of this study is a rapid review. Five databases were searched for relevant literature, and main findings were extracted. Thirteen articles were included in the review. The following themes were identified: negative emotional states; living with uncertainty; concerns about infection; concerns about and uptake of prophylaxis or treatment; disrupted routines; non-pharmaceutical protective behaviours; social support; financial and occupational concerns; disrupted expectations of birth, prenatal care and postnatal care and sources of information. Pregnant women have unique needs during infectious disease outbreaks and could benefit from up-to-date, consistent information and guidance; appropriate support and advice from healthcare professionals, particularly with regards to the risks and benefits of prophylaxis and treatment; virtual support groups and designating locations or staff specifically for pregnant women. • Pregnant women may be particularly susceptible to distress during pandemics. • Infection fears and prophylaxis concerns may exacerbate distress. • Disrupted routines, financial concerns and uncertainty are also stressors. • Disrupted expectations of birth and related healthcare may be distressing. • Pregnant women may benefit from clear information/guidance and support groups. [ABSTRACT FROM AUTHOR]

• Vitamin D serum levels above 35 ng/mL seem to correlate with the mortality rate of COVID-19 patients. • The majority of the Western population is deficient in Vitamin D. • Vitamin D3 supplementation also requires additional vitamin K2 intake. • The immunoregulating function of vitamin D is promising and might decrease the global epidemic mortality. The world is desperately seeking for a sustainable solution to combat the coronavirus strain SARS-CoV-2 (COVID-19). Recent research indicated that optimizing Vitamin D blood levels could offer a solution approach that promises a heavily reduced fatality rate as well as solving the public health problem of counteracting the general vitamin D deficiency. This paper dived into the immunoregulatory effects of supplementing Vitamin D 3 by elaborating a causal loop diagram. Together with D 3 , vitamin K 2 and magnesium should be supplemented to prevent long-term health risks. Follow up clinical randomized trials are required to verify the current circumstantial evidence. [ABSTRACT FROM AUTHOR]

When 2020 began, we had no idea what was to unfold globally as we learnt about the Novel-Coronavirus in Wuhan, in the Hubei province of China. As this virus spread rapidly, it became a matter of time before many countries began to implement measures to try and contain the spread of the disease. COVID-19 as it is referred to, resulted in two main approaches to fighting the viral pandemic, either through a progressive set of measures to slow down the number of identified cases designed to 'flatten the curve' over time (anticipated to be at least six months), or to attack it by the severest of measures including a total lock-down and/or herding exposure to fast track 'immunisation' while we await a vaccine. The paper reports the findings from the first phase of an ongoing survey designed to identify the changing patterns in travel activity of Australian residents as a result of the stage 2 restrictions imposed by the Australian government. The main restrictions, in addition to social distancing of at least 1.5 m, are closure of entry to Australia (except residents returning), and closure of non-essential venues such as night clubs, restaurants, mass attendee sporting events, churches, weddings, and all social gatherings in any circumstance. With some employers encouraging working from home and others requiring it, in addition to job losses, and many children attending school online from home, the implications on travel activity is extreme. We identify the initial impacts associated with the first month of stricter social distancing measures introduced in Australia. • General support for the actions of government and business. • Widespread suppression of travel demand for all trip purposes across all modes. • Sizeable shift to working from home by those who can. • Public transport will face the largest hurdles in regaining confidence. • Retail and food logistics will need to examine distribution processes • The aviation sector will likely need to restart with a heavy focus on domestic travel. • Public transport will need to take overt measures to restore confidence. • The dominance of the car is further enforced in the context of biosecurity concerns. • Active transport is a viable option for short inner-city trips. • Infrastructure investment should be carefully considered. • Flexible working arrangements are perhaps the biggest policy lever available to governments. [ABSTRACT FROM AUTHOR]

• Oxygen supplementation may disrupt virus replication. • Oxygen therapy can improve the antiviral immune response. • An oxygen-rich environment may down-regulate ACE2 expression. • Nocturnal oxygen therapy may delay the progression of COVID-19. There is currently no effective antiviral therapy or immune-based treatment for coronavirus disease (COVID-19). The urgent challenge is to prevent the transition of COVID-19 from mild to severe infection. This paper discussed nocturnal oxygen therapy as a new option for people with COVID-19 under home quarantine. It suggested that nocturnal oxygen therapy in the early stages may be helpful in preventing disease progression by inhibiting the rapid replication of the virus and improving the body's antiviral ability. [ABSTRACT FROM AUTHOR]

• SARS-CoV-2 may impair host antiviral response, causing subsequent hyperinflammation. • SARS-CoV-2 likely deranges the renin angiotensin aldosterone system (RAAS). • Hyperinflammation and RAAS imbalance may drive acute lung injury and coagulopathy. • RAAS imbalance impairs fibrinolysis, which can result in relative hypofibrinolysis. • This can lead widespread immunothrombosis, contributing to multi-organ damage. Early clinical evidence suggests that severe cases of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), are frequently characterized by hyperinflammation, imbalance of renin-angiotensin-aldosterone system, and a particular form of vasculopathy, thrombotic microangiopathy, and intravascular coagulopathy. In this paper, we present an immunothrombosis model of COVID-19. We discuss the underlying pathogenesis and the interaction between multiple systems, resulting in propagation of immunothrombosis, which through investigation in the coming weeks, may lead to both an improved understanding of COVID-19 pathophysiology and identification of innovative and efficient therapeutic targets to reverse the otherwise unfavorable clinical outcome of many of these patients. [ABSTRACT FROM AUTHOR]

COVID-19 vaccines are new brands of consumer health technology being introduced to the market. Considering consumer behaviour approaches in this time of crisis, the risk of vaccine hesitancy, the call for more transparency and effective messaging to gain trust, and equitable distribution of this vaccine, this is unexplored theoretical terrain. This commentary takes a multidisciplinary approach to understand and theoretically explore the marketing, distribution, and acceptance of the COVID-19 vaccine. The paper integrates marketing principles, including advertisement and branding of consumer health technology with supply chain management, public affairs, and public health. A theoretical framework was presented to illustrate this relationship and key areas of concerns. The practical implications relevant to equity, ethics, education, employment, and the economic impact was presented. [ABSTRACT FROM AUTHOR]

While a human challenge study holds the prospect of accelerating the development of a vaccine for the coronavirus SARS-CoV-2, it may be opposed due to risks of harm to participants and researchers. Given the increasing number of human deaths and severe disruption to lives worldwide, we argue that a SARS-CoV-2 challenge study is ethically justifiable as its social value substantially outweighs the risks. Such a study should therefore be seriously considered as part of the global research response towards the COVID-19 pandemic. In this paper, we contribute to the debate by addressing the misperception that a challenge study for the coronavirus would lower scientific and ethical standards for vaccine research and development, and examine how it could be ethically conducted. We also set out information that needs to be disclosed to prospective participants to obtain their consent. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic caused by SARS-CoV-2 remains a significant issue for global health, economics and society. A wealth of data has been generated since its emergence in December 2019, and it is vital for clinicians to keep up with this data from across the world at a time of uncertainty and constantly evolving guidelines and clinical practice. Here we provide an update for clinicians on the recent developments in the virology, diagnostics, clinical presentation, viral shedding, and treatment options for COVID-19 based on current literature. We considered published peer-reviewed papers and non-peer-reviewed pre-print manuscripts on COVID19 and related aspects with an emphasis on clinical management aspects. We describe the virological characteristics of SARS-CoV-2 and the clinical course of COVID-19 with an emphasis on diagnostic challenges, duration of viral shedding, severity markers and current treatment options. The key challenge in managing COVID-19 remains patient density. However, accurate diagnosis as well as early identification and management of high-risk severe cases are important for many clinicians. For improved management of cases, there is a need to understand test probability of serology, qRT-PCR and radiological testing, and the efficacy of available treatment options that could be used in severe cases with a high risk of mortality. [ABSTRACT FROM AUTHOR]

The full impact of the coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), on the field of hematopoietic cell transplantation (HCT) is unknown. This perspective paper reviews the following: current COVID-19 epidemiology, diagnosis, and potential therapies; care considerations unique to HCT recipients; and the concept of a learning network to assimilate emerging guidelines and best practices and to optimize patient outcomes through facilitating shared learning and experience across transplantation centers. [ABSTRACT FROM AUTHOR]

• The latest summary of the COVID-19 outbreak in China; • There might be an oral-fecal transmission of the virus; • Aggregates and consolidates the epidemiology, clinical manifestations, diagnosis, treatments and preventions of this new type of coronavirus. There is a current worldwide outbreak of the novel coronavirus Covid-19 (coronavirus disease 2019; the pathogen called SARS-CoV-2; previously 2019-nCoV), which originated from Wuhan in China and has now spread to 6 continents including 66 countries, as of 24:00 on March 2, 2020. Governments are under increased pressure to stop the outbreak from spiraling into a global health emergency. At this stage, preparedness, transparency, and sharing of information are crucial to risk assessments and beginning outbreak control activities. This information should include reports from outbreak site and from laboratories supporting the investigation. This paper aggregates and consolidates the epidemiology, clinical manifestations, diagnosis, treatments and preventions of this new type of coronavirus. [ABSTRACT FROM AUTHOR]

Health education campaigns often aim to create awareness by increasing objective knowledge about pathogens, such as COVID-19. However, the present paper proposes that confidence in one's knowledge more than knowledge is a significant factor that leads to a laxer attitude toward COVID-19 and hence lower support for protective measures and reduced intention to comply with preemptive behaviors. We tested two hypotheses in three studies conducted between 2020 and 2022. In Study 1, we assessed participants' level of knowledge and confidence, as well as attitudes toward COVID-19. In Study 2, we tested the relation between fear of COVID-19 and protective behaviors. In Study 3, we used an experimental approach to show the causal effect of overconfidence on fear of COVID-19. In addition to manipulating overconfidence and measuring fear of COVID-19, we also measured prophylactic behaviors. In Study 1, more overconfident participants had a laxer attitude toward COVID-19. While knowledge had an increasing effect on worry, confidence in said knowledge significantly decreased worry about COVID-19. In Study 2, participants who were more worried about COVID-19 were more likely to engage in protective behaviors (e.g., wearing masks). In Study 3, we show that when overconfidence was experimentally diminished, fear of COVID-19 increased. The results support our claim that the effect of overconfidence on attitudes toward COVID-19 is causal in nature. Moreover, the results show that people with higher fear of COVID-19 are more likely to wear masks, use hand sanitizers, avoid crowded places or social gatherings, and get vaccinated. Managing adherence to public health measures is critical when it comes to highly infectious diseases. Our findings suggest that efficient information campaigns to increase adherence to public health measures should focus on calibrating people's confidence in their knowledge about COVID-19 to prevent the spread of the virus. • While knowledge increases fear of COVID-19, confidence has the opposite effect. • Fear of COVID-19 increases the adherence to public safety measures. • Health education campaigns should aim to calibrate confidence to increase adherence. [ABSTRACT FROM AUTHOR]

The current pandemic of the acute severe respiratory syndrome coronavirus 2 (SARS-CoV-2) killed about 6.4 million and infected more than 600 million individuals by august of 2022, and researchers worldwide are searching for fast and selective approaches for this virus detection. Colorimetric biosensors are an excellent alternative because they are sensitive, simple, fast, and low-cost for rapid detection of SARS-CoV-2 compared to standard Enzyme-linked immunosorbent assay (ELISA) and Polymerase Chain Reaction (PCR) techniques. This study systematically searched and reviewed literature data related to colorimetric biosensors in detecting SARS-CoV-2 viruses, recovered from the Scopus (n = 16), Web of Science (n = 19), PubMed (n = 19), and Science Direct (n = 17) databases totalizing n = 71 articles. Data were analyzed for the type of nanomaterial, biorecognition material at the detection limit (LOD), and devices designed for diagnostics. The most applied nanomaterial were gold nanoparticles, in their original form and hybrid in quantum dots and core-shell. In addition, we show high specificity in point-of-care (POC) diagnostic devices as a faster and cheaper alternative for clinical diagnosis. Finally, the highlights of the colorimetric biosensor developed for diagnostic devices applied in swabs, surgical masks, and lateral flow immunoassays were presented. [Display omitted] • Gold-based nanomaterials were the most applied in biosensors for SARS-CoV-2 detection. • The ACE2 enzyme was used as a biorecognition material of the biosensor, which did not need previa amplification. • The main applications of biosensors in POC are LFIA, mLFA, surgical masks, optodiagnostics and paper colorimetric. [ABSTRACT FROM AUTHOR]

• A deep learning COVID-19 diagnostic system is developed using state-of-the-art deep learning architectures. • A detailed analysis of different methods used for detecting COVID-19 is presented in this paper. • Chest X-rays from different sources are collected to build a robust classification model. • The developed diagnostic model provided efficient results, even with a limited number of input images. • The results produced in this work are validated by an expert radiologist. In recent months, a novel virus named Coronavirus has emerged to become a pandemic. The virus is spreading not only humans, but it is also affecting animals. First ever case of Coronavirus was registered in city of Wuhan, Hubei province of China on 31st of December in 2019. Coronavirus infected patients display very similar symptoms like pneumonia, and it attacks the respiratory organs of the body, causing difficulty in breathing. The disease is diagnosed using a Real-Time Reverse Transcriptase Polymerase Chain reaction (RT-PCR) kit and requires time in the laboratory to confirm the presence of the virus. Due to insufficient availability of the kits, the suspected patients cannot be treated in time, which in turn increases the chance of spreading the disease. To overcome this solution, radiologists observed the changes appearing in the radiological images such as X-ray and CT scans. Using deep learning algorithms, the suspected patients' X-ray or Computed Tomography (CT) scan can differentiate between the healthy person and the patient affected by Coronavirus. In this paper, popular deep learning architectures are used to develop a Coronavirus diagnostic systems. The architectures used in this paper are VGG16, DenseNet121, Xception, NASNet, and EfficientNet. Multiclass classification is performed in this paper. The classes considered are COVID-19 positive patients, normal patients, and other class. In other class, chest X-ray images of pneumonia, influenza, and other illnesses related to the chest region are included. The accuracies obtained for VGG16, DenseNet121, Xception, NASNet, and EfficientNet are 79.01%, 89.96%, 88.03%, 85.03% and 93.48% respectively. The need for deep learning with radiologic images is necessary for this critical condition as this will provide a second opinion to the radiologists fast and accurately. These deep learning Coronavirus detection systems can also be useful in the regions where expert physicians and well-equipped clinics are not easily accessible. [ABSTRACT FROM AUTHOR]

• Use Machine Learning models to predict the mortality of COVID19 patients. • Analyse patient case studies which may help in better personalised treatment. • Apply Shapley Values to interpret the Machine Learning models predictions. • Perform what-if analysis to see how patient mortality outcomes change. In this paper we apply a series of Machine Learning models to a recently published unique dataset on the mortality of COVID19 patients. We use a dataset consisting of blood samples of 375 patients admitted to a hospital in the region of Wuhan, China. There are 201 patients who survived hospitalisation and 174 patients who died whilst in hospital. The focus of the paper is not only on seeing which Machine Learning model is able to obtain the absolute highest accuracy but more on the interpretation of what the Machine Learning models provides. We find that age , days in hospital , Lymphocyte and Neutrophils are important and robust predictors when predicting a patients mortality. Furthermore, the algorithms we use allows us to observe the marginal impact of each variable on a case-by-case patient level, which might help practicioneers to easily detect anomalous patterns. This paper analyses the global and local interpretation of the Machine Learning models on patients with COVID19. [ABSTRACT FROM AUTHOR]

The rapid spread of coronavirus (COVID-19) has a significant impact on the world economy, especially on the financial market. Investors are panicking about the future. This paper considers industry data and aims to investigate the impact of the pandemic on China's stock market. The Asymmetric-GARCH-BEKK model and complex network theory were combined to construct the interaction networks. From the perspective of spillover effect, we investigated the time varying co-movement during the pandemic. The results indicate that the outbreak of COVID-19 weakens the mean spillover, but enhances the volatility spillover among China's stock market. However, both mean spillover and volatility spillover decreased rapidly during the period of regular epidemic prevention and control. We also found that different industries have various sensitivity to the COVID-19 pandemic. • The outbreak of COVID-19 has enhanced volatility spillover among China's stock market. • The spillover effect among China's stock market decreased rapidly in middle and late stages of the pandemic. • Ten industries shown different sensitivity to the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

• So far, a particular therapeutic option has not been approved for COVID-19. • Antiviral agents and immune modulating strategies has been studied for COVID-19 treatment. • Current treatments are diverse in each patient and have various outcomes. • Some novel approach such as cell based therapy, and immunotherapy may have significant potential in the COVID-19 treatment. Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) is a highly pathogenic and transmissible virus. Infection caused by SARS-CoV-2 known as Coronavirus disease 2019 (COVID-19) can be severe, especially among high risk populations affected of underlying medical conditions. COVID-19 is characterized by the severe acute respiratory syndrome, a hyper inflammatory syndrome, vascular injury, microangiopathy and thrombosis. Antiviral drugs and immune modulating methods has been evaluated. So far, a particular therapeutic option has not been approved for COVID-19 and a variety of treatments have been studied for COVID-19 including, current treatment such as oxygen therapy, corticosteroids, antiviral agents until targeted therapy and vaccines which are diverse in each patient and have various outcomes. According to the findings of different in vitro and in vivo studies, some novel approach such as gene editing, cell based therapy, and immunotherapy may have significant potential in the treatment of COVID-19. Based on these findings, this paper aims to review the different strategies of treatment against COVID-19 and provide a summary from traditional and newer methods in curing COVID-19. [ABSTRACT FROM AUTHOR]

Radiological methodologies, such as chest x-rays and CT, are widely employed to help diagnose and monitor COVID-19 disease. COVID-19 displays certain radiological patterns easily detectable by X-rays of the chest. Therefore, radiologists can investigate these patterns for detecting coronavirus disease. However, this task is time-consuming and needs lots of trial and error. One of the main solutions to resolve this issue is to apply intelligent techniques such as deep learning (DL) models to automatically analyze the chest X-rays. Nevertheless, fine-tuning of architecture and hyperparameters of DL models is a complex and time-consuming procedure. In this paper, we propose an effective method to detect COVID-19 disease by applying convolutional neural network (CNN) to the chest X-ray images. To improve the accuracy of the proposed method, the last Softmax CNN layer is replaced with a K -nearest neighbors (KNN) classifier which takes into account the agreement of the neighborhood labeling. Moreover, we develop a novel evolutionary algorithm by improving the basic version of competitive swarm optimizer. To this end, three powerful evolutionary operators: Cauchy Mutation (CM), Evolutionary Boundary Constraint Handling (EBCH), and tent chaotic map are incorporated into the search process of the proposed evolutionary algorithm to speed up its convergence and make an excellent balance between exploration and exploitation phases. Then, the proposed evolutionary algorithm is used to automatically achieve the optimal values of CNN's hyperparameters leading to a significant improvement in the classification accuracy of the proposed method. Comprehensive comparative results reveal that compared with current models in the literature, the proposed method performs significantly more efficient. • A novel deep neuroevolution algorithm for detecting COVID-19 from X-ray images. • Proposing a modified version of the Competitive Swarm Optimizer algorithm. • Tuning the hyperparameters and architecture of Convolutional Neural Networks. • Producing excellent results by proposed framework comparing to 23 benchmarks. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is associated with an increased prevalence of mental health problems and addictive behaviors. There is a growing theoretical and empirical evidence that individual differences in interoceptive anxiety-related processes are a one set of vulnerability factors that are important in understanding the impact of pandemic-related mental health problems and addictive behavior. However, there has not been a comprehensive effort to explore this rapidly growing body of research and its implications for public health. In this paper, we discuss why interoceptive anxiety-related processes are relevant to understanding mental health and addictive behaviors during the COVID-19 pandemic. We then provide a narrative review of the available COVID-19 literature linking interoceptive fear and anxiety-related processes (e.g., anxiety sensitivity, health anxiety, and COVID-19 anxiety, fear, and worry) to mental health and addictive behaviors. We then propose a novel transdiagnostic theoretical model that highlights the role of interoceptive anxiety-related processes in mental health and addictive behavior in the context of the present and future pandemics. In the final section, we utilize this conceptualization to underscore clinical implications and provide guidance for future research initiatives in the management of COVID-19 mental health and addictive behaviors and inform the public health field for future pandemics. • Narrative review of impact of interoceptive processes during COVID-19. • Interoceptive processes linked with mental health problems during COVID-19. • Interoceptive processes linked with addictive behaviors during COVID-19. [ABSTRACT FROM AUTHOR]

The increased use of digital tools such as smart phones, Internet of Things devices, cameras, and microphones, has led to the produuction of big data. Large data dimensionality, redundancy, and irrelevance are inherent challenging problems when it comes to big data. Feature selection is a necessary process to select the optimal subset of features when addressing such problems. In this paper, the authors propose a novel Binary Coronavirus Disease Optimization Algorithm (BCOVIDOA) for feature selection, where the Coronavirus Disease Optimization Algorithm (COVIDOA) is a new optimization technique that mimics the replication mechanism used by Coronavirus when hijacking human cells. The performance of the proposed algorithm is evaluated using twenty-six standard benchmark datasets from UCI Repository. The results are compared with nine recent wrapper feature selection algorithms. The experimental results demonstrate that the proposed BCOVIDOA significantly outperforms the existing algorithms in terms of accuracy, best cost, the average cost (AVG), standard deviation (STD), and size of selected features. Additionally, the Wilcoxon rank-sum test is calculated to prove the statistical significance of the results. [ABSTRACT FROM AUTHOR]

A novel virus, called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly become a pandemic called Coronavirus disease 2019 (COVID-19). According to the World Health Organization, COVID-19 was first detected in Wuhan city in December 2019 and has affected 216 countries with 9473214 confirmed cases and 484249 deaths globally as on June 26th, 2020. Also, this outbreak continues to grow in many countries like the United States of America (U.S.), Brazil, India, and Russia. To ensure rapid surveillance and better decision-making by government authorities in different countries, it is vital to identify alive and emerging hotspots within a country promptly. State-of-the-art methods based on space-time scan statistics (like SaTScan) are not geographically robust. Also, due to the enumeration of many Spatio-temporal cylinders, the computation cost of Spatio-temporal SaTScan (ST-SaTScan) is very high. In the applications like COVID-19 where we need to detect the emerging hotspots daily as soon as the new count of cases gets updated, ST-SaTScan seems inefficient. Therefore, this paper proposes a Particle Swarm Optimizer-based scheme to timely detect geographically robust, alive, and emerging COVID-19 hotspots in a country. Timely detection can help government officials design better control strategies like increasing testing in hotspots, imposing stricter containment rules, or setting up temporary hospital beds. Performance of ST-SaTScan and proposed scheme have been analyzed for four worst-hit U.S. states for the incubation period of 14 days between June 11th, 2020, and June 24th, 2020. Results indicate that the proposed scheme detects hotspots of a higher likelihood ratio (a measure to indicate the significance of hotspot) than ST-SaTScan in significantly less time. We also applied the proposed scheme to detect the emerging COVID-19 hotspots in all states of the U.S. During the study period, the proposed scheme has detected 104 emerging COVID-19 hotspots. • An efficient PSO based scheme called ST-PSO-GRHD is proposed to detect geographically robust, emerging COVID-19 hotspots. • Prompt detection of emerging COVID-19 hotspots will help officials ensure timely measures for early containment of disease. • Performance of ST-PSO-GRHD is compared with ST-SaTScan using COVID-19 data of four worst hit US states. • Comparative analysis indicates that ST-PSO-GRHD detects more significant hotspots in significantly less time than ST-SaTScan. • COVID-19 hotspots of entire US (June 11 to June 24, 2020) are identified to prioritize rapid testing, restrictions, etc. [ABSTRACT FROM AUTHOR]

The emergence of the COVID-19 (coronavirus) pandemic in late 2019 and early 2020 presented new and urgent challenges to mental health services and legislators around the world. This special issue of the International Journal of Law and Psychiatry explores mental health law, mental capacity law, and medical and legal ethics in the context of COVID-19. Papers are drawn from India, Australia, the United Kingdom, Ireland, Germany, Portugal, and the United States. Together, these articles demonstrate the complexity of psychiatric and legal issues prompted by COVID-19 in terms of providing mental health care, protecting rights, exercising decision-making capacity, and a range of other topics. While further work is needed in many of these areas, these papers provide a strong framework for addressing key issues and meeting the challenges that COVID-19 and, possibly, other outbreaks are likely to present in the future. [ABSTRACT FROM AUTHOR]

With the onset of COVID-19 restrictions and the slow relaxing of many restrictions, it is imperative that we understand what this means for the performance of the transport network. In going from almost no commuting, except for essential workers, to a slow increase in travel activity with working from home (WFH) continuing to be both popular and preferred, this paper draws on two surveys, one in late March at the height of restrictions and one in late May as restrictions are starting to be partially relaxed, to develop models for WFH and weekly one-way commuting travel by car and public transport. We compare the findings as one way to inform us of the extent to which a sample of Australian residents have responded through changes in WFH and commuting. While it is early days to claim any sense of a new stable pattern of commuting activity, this paper sets the context for ongoing monitoring of adjustments in travel activity and WFH, which can inform changes required in the revision of strategic metropolitan transport models as well as more general perspectives on future transport and land use policy and planning. • Two wave study examining work from home (WFH) and commute behaviour through COVID-19 pandemic. • New models for WFH and impact on commuting activity. • Evidence of changes WFH and commuting from immediately after restrictions to their initial easing • Small decreases in WFH and increases in car-based commuting starting to occur. • WFH has been a positive experience and many would to WFH more than before COVID-19, if they have an appropriate space. • Policy makers should encourage employer support for working from home and help identify and reduce barriers to doing so. [ABSTRACT FROM AUTHOR]

This paper explores news media discourse about COVID-19 during the spring of 2020 in Sweden, aiming to provide an understanding of how moralising discourse is employed in narratives about public health risks and responses. We investigate print news media content about the corona virus and COVID-19 during the early stages of the outbreak, guided analytically by framework focusing on the relationship between moral panics and moral regulation. We direct attention, first, to how both moral majorities and villains, i.e., 'folk devils', and heroes are constructed in the news. Secondly, we look at how visions for interventions are produced discursively in relation to such constructions. Our findings suggest that moralising discourse largely target risk behaviours and health care claims of middle-class groups. We also find that news media discourse about the pandemic in Sweden is marked by attacks on government interventions that are distinctly different from observations in other contexts. In conclusion, we discuss these observations in relation the political and discursive context, and the potential impact of moralising discourse on the legitimacy of public health interventions and the welfare state. Finally, we also discuss how our findings can inform theoretical discussions about political populism, moralising discourse and public health. • Moralising discourse marked Swedish news media narratives about COVID-19. • Calls for interventions by 'moral entrepreneurs' targeted risks among 'folk devils'. • It was different in character to discourse emerging in other contexts. • It was also characterised by traits associated with medical populism. [ABSTRACT FROM AUTHOR]

• Deep Neural Networks Ensemble for the detection of COVID-2019 from CT-Scans. • Feature extraction and Clustering to group specific lobes images. • Integration of existing CT scans datasets. Research on Coronavirus Disease 2019 (COVID-19) detection methods has increased in the last months as more accurate automated toolkits are required. Recent studies show that CT scan images contain useful information to detect the COVID-19 disease. However, the scarcity of large and well balanced datasets limits the possibility of using detection approaches in real diagnostic contexts as they are unable to generalize. Indeed, the performance of these models quickly becomes inadequate when applied to samples captured in different contexts (e.g., different equipment or populations) from those used in the training phase. In this paper, a novel ensemble-based approach for more accurate COVID-19 disease detection using CT scan images is proposed. This work exploits transfer learning using pre-trained deep networks (e.g., VGG, Xception, and ResNet) evolved with a genetic algorithm, combined into an ensemble architecture for the classification of clustered images of lung lobes. The study is validated on a new dataset obtained as an integration of existing ones. The results of the experimental evaluation show that the ensemble classifier ensures effective performance, also exhibiting better generalization capabilities. [ABSTRACT FROM AUTHOR]

Almost 80% of people confronting COVID-19 recover from COVID-19 disease without any particular treatments. They experience heterogeneous symptoms; a wide range of respiratory symptoms, cough, dyspnea, fever, and viral pneumonia. However, some others need urgent intervention and special treatment to get rid of this widespread disease. So far, there isn't any unique drug for the potential treatment of COVID 19. However, some available therapeutic drugs used for other diseases seem beneficial for the COVID-19 treatment. On the other hand, there is a robust global concern for developing an efficient COVID-19 vaccine to control the COVID-19 pandemic sustainably. According to the WHO report, since 8 October 2021, 320 vaccines have been in progress. 194 vaccines are in the pre-clinical development stage that 126 of them are in clinical progression. Here, in this paper, we have comprehensively reviewed the most recent and updated information about coronavirus and its mutations, all the potential therapeutic approaches for treating COVID-19, developed diagnostic systems for COVID- 19 and the available COVID-19 vaccines and their mechanism of action. [Display omitted] [ABSTRACT FROM AUTHOR]

• A novel multi-kernel-size spatial-channel attention model for COVID-19 detection • The proposed method enables early diagnosis from chest X-ray images. • Evaluation with real-world data demonstrates improved accuracy at 98.2 Novel coronavirus 2019 (COVID-19) has spread rapidly around the world and is threatening the health and lives of people worldwide. Early detection of COVID-19 positive patients and timely isolation of the patients are essential to prevent its spread. Chest X-ray images of COVID-19 patients often show the characteristics of multifocality, bilateral hairy glass turbidity, patchy network turbidity, etc. It is crucial to design a method to automatically identify COVID-19 from chest X-ray images to help diagnosis and prognosis. Existing studies for the classification of COVID-19 rarely consider the role of attention mechanisms on the classification of chest X-ray images and fail to capture the cross-channel and cross-spatial interrelationships in multiple scopes. This paper proposes a multi-kernel-size spatial-channel attention method to detect COVID-19 from chest X-ray images. Our proposed method consists of three stages. The first stage is feature extraction. The second stage contains two parallel multi-kernel-size attention modules: multi-kernel-size spatial attention and multi-kernel-size channel attention. The two modules capture the cross-channel and cross-spatial interrelationships in multiple scopes using multiple 1D and 2D convolutional kernels of different sizes to obtain channel and spatial attention feature maps. The third stage is the classification module. We integrate the chest X-ray images from three public datasets: COVID-19 Chest X-ray Dataset Initiative, ActualMed COVID-19 Chest X-ray Dataset Initiative, and COVID-19 radiography database for evaluation. Experimental results demonstrate that the proposed method improves the performance of COVID-19 detection and achieves an accuracy of 98.2%. [ABSTRACT FROM AUTHOR]

The novel coronavirus (COVID-19) is highly detrimental, and its death distribution peculiarity has severely affected people's health and the operations of businesses. COVID-19 has wholly undermined the global economy, including inflicting significant damage to the ever-emerging biomass supply chain; its sustainability is disintegrating due to the coronavirus. The biomass supply chain must be sustainable and robust enough to adapt to the evolving and fluctuating risks of the market due to the coronavirus or any potential future pandemics. However, no such study has been performed so far. To address this issue, investigating how COVID-19 influences a biomass supply chain is vital. This paper presents a dynamic risk assessment methodological framework to model biomass supply chain risks due to COVID-19. Using a dynamic Bayesian network (DBN) formalism, the impacts of COVID-19 on the performance of biomass supply chain risks have been studied. The proposed model has been applied to the biomass supply chain of a U.S.-based Mahoney Environmental® company in Washington, USA. The case study results show that it would take one year to recover from the maximum damage to the biomass supply chain due to COVID-19, while full recovery would require five years. Results indicate that biomass feedstock gate availability (FGA) is 2%, due to pandemic and lockdown conditions. Due to the availability of vaccination and gradual business reopenings, this availability increases to 92% in the second year. Results also indicate that the price of fossil-based fuel will gradually increase after one year of the pandemic; however, the market prices of fossil-based fuel will not revert to pre-coronavirus conditions even after nine years. K-fold cross-validation is used to validate the DBN. Results of validation indicate a model accuracy of 95%. It is concluded that the pandemic has caused risks to the sustainability of biomass feedstock, and the current study can help develop risk mitigation strategies. • Impact of coronavirus (COVID-19) on biomass supply chain is studied. • A case study of a US based biofuel company is presented. • A dynamic Bayesian network (DBN) model is developed to investigate the biomass supply chain's risk over ten years, considering pandemic and post-coronavirus situations. • The DBN model is able to predict risks and their behaviours under uncertainties. • Study identifies high preprocessing costs of biomass resource due to COVID-19. • Model validation indicates an accuracy of 95%. [ABSTRACT FROM AUTHOR]

The outbreak of the coronavirus (COVID-19) disease is overwhelming resources, economies and countries around the world. Millions of people have been infected and hundreds of thousands have succumbed to the virus. Research regarding the coronavirus pandemic is published every day. However, there is limited discourse regarding societal perception. Thus, this paper examines blame attribution concerning the origin and propagation of the coronavirus crisis according to public perception. Specifically, data were extracted from the social media platform Twitter concerning the coronavirus during the early stages of the outbreak and further investigated using thematic analysis. The findings revealed the public predominantly blames national governments for the coronavirus pandemic. In addition, the results documented the explosion of conspiracy theories among social media users regarding the virus' origin. In the early stages of the pandemic, the blame tendency was most frequent to conspiracy theories and restriction of information from the government, whilst in the later months, responsibility had shifted to political leaders and the media. The findings indicate an emerging government mistrust that may result in disregard of preventive health behaviours and the amplification of conspiracy theories, and an evolving dynamic of blame. This study argues for a transparent, continuing dialogue between governments and the public to stop the spread of the coronavirus. • Social media users predominantly blame national governments for the coronavirus pandemic. • There was an explosion of conspiracy theories regarding origin and propagation of coronavirus in social media. • Blame attribution among the public shifts as the pandemic progresses. • Early restriction of information regarding the coronavirus may have led to the exponential increase in conspiracy theories. [ABSTRACT FROM AUTHOR]

Coronavirus is an infectious life-threatening disease and is mainly transmitted through infected person coughs, sneezes, or exhales. This disease is a global challenge that demands advanced solutions to address multiple dimensions of this pandemic for health and wellbeing. Different types of medical and technological-based solutions have been proposed to control and treat COVID-19. Machine learning is one of the technologies used in Magnetic Resonance Imaging (MRI) classification whereas nature-inspired algorithms are also adopted for image optimization. In this paper, we combined the machine learning and nature-inspired algorithm for brain MRI images of COVID-19 patients namely Machine Learning and Nature Inspired Model for Coronavirus (MLNI-COVID-19). This model improves the MRI image classification and optimization for better diagnosis. This model will improve the overall performance especially the area of brain images that is neglected due to the unavailability of the dataset. COVID-19 has a serious impact on the patient brain. The proposed model will help to improve the diagnosis process for better medical decisions and performance. The proposed model is evaluated with existing algorithms and achieved better performance in terms of sensitivity, specificity, and accuracy. [ABSTRACT FROM AUTHOR]

• 7 coronaviruses are known to affect humans; all have ancestry in mammalian hosts • COVID-19 is the 3rd zoonotic epidemic & the 1st pandemic caused by a coronavirus • SARS-CoV-2 intermediate hosts & all susceptible animal species still remain unknown • The One Health approach is essential for understanding SARS-CoV-2 transmissibility The Coronavirus Disease 2019 (COVID-19) is the first known pandemic caused by a coronavirus. Its causative agent, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), appears to be capable of infecting different mammalian species. Recent detections of this virus in pet, zoo, wild, and farm animals have compelled inquiry regarding the zoonotic (animal-to-human) and reverse zoonotic (human-to-animal) transmissibility of SARS-CoV-2 with the potential of COVID-19 pandemic evolving into a panzootic. It is important to monitor the global spread of disease and to assess the significance of genomic changes to support prevention and control efforts during a pandemic. An understanding of the SARS-CoV-2 epidemiology provides opportunities to prevent the risk of repeated re-infection of humans and requires a robust One Health-based investigation. This review paper describes the known properties and the existing gaps in scientific knowledge about the zoonotic and reverse zoonotic transmissibility of the novel virus SARS-CoV-2 and the COVID-19 disease it causes. [ABSTRACT FROM AUTHOR]

In medical imaging procedures for the detection of coronavirus, apart from medical tests, approval of diagnosis has special significance. Imaging procedures are also useful for detecting the damage caused by COVID-19. Chest X-ray imaging is frequently used to diagnose COVID-19 and different pneumonias. This paper presents a task-specific framework to detect coronavirus in X-ray images. Binary classification of three different labels (healthy, bacterial pneumonia, and COVID-19) was performed on two differentiated data sets in which corona is stated as positive. First-order statistics, gray level co-occurrence matrix, gray level run length matrix, and gray level size zone matrix were analyzed to form fifteen sub-data sets and to ascertain the necessary radiomics. Two normalization methods are compared to make the data meaningful. Furthermore, five feature ranking approaches (Bhattacharyya , entropy , Roc , t-test , and Wilcoxon) are mentioned to provide necessary information to a state-of-the-art classifier based on Gauss-map-based chaotic particle swarm optimization and neural networks. The proposed framework was designed according to the analyses about radiomics, normalization approaches, and filter-based feature ranking methods. In experiments, seven metrics were evaluated to objectively determine the results: accuracy, area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, g-mean, precision, and f-measure. The proposed framework showed promising scores on two X-ray-based data sets, especially with the accuracy and area under the ROC curve rates exceeding 99% for the classification of coronavirus vs. others. [Display omitted] • A comprehensive study presenting a novel COVID-19 framework. • A detailed study analyzing the coronavirus vs. noncoronavirus specification. • Assays with radiomic combinations, normalization and feature ranking methods. • The first study using a novel optimized NN-based model on COVID-19 distinction. [ABSTRACT FROM AUTHOR]

The current ongoing outbreak of Coronavirus Disease 2019 (COVID-19) has globally affected the lives of more than one hundred million people. RT-PCR based molecular test is recommended as the gold standard method for diagnosing current infections. However, transportation and processing of the clinical sample for detecting virus require an expert operator and long processing time. Testing device enables on-site virus detection could reduce the sample-to-answer time, which plays a central role in containing the pandemic. In this work, we proposed an intelligent face mask, where a flexible immunosensor based on high density conductive nanowire array, a miniaturized impedance circuit, and wireless communication units were embedded. The sub-100 nm size and the gap between the neighbored nanowires facilitate the locking of nanoscale virus particles by the nanowire arrays and greatly improve the detection efficiency. Such a point-of-care (POC) system was demonstrated for coronavirus 'spike' protein and whole virus aerosol detection in simulated human breath. Detection of viral concentration as low as 7 pfu/mL from the atomized sample of coronavirus aerosol mimic was achieved in only 5 min. The POC systems can be readily applied for preliminary screening of coronavirus infections on-site and may help to understand the COVID-19 progression while a patient is under prescribed therapy. • This paper presents an intelligent face mask for the exhaled coronavirus aerosols screening within a few minutes. • It integrates a high density conductive nanowire array-based immunosensor and a miniaturized wireless impedance circuit. • The matching relation between the geometry of the nanowires and the target virus aerosol are designed for the indentification directly. • Through the enrichment of the exhaled viral aerosols by the face mask, it canbe achieve the preliminary screening virus infections. [ABSTRACT FROM AUTHOR]

COVID-19 outbreak can impose serious negative impacts on the infrastructures of societies including the healthcare systems. Despite the increasing research efforts, false positive or negative results that may be associated with serologic or even RT-PCR tests, inappropriate or variable immune response, and high rates of mutations in coronavirus may negatively affect virus detection process and effectiveness of the vaccines or drugs in development. Nanotechnology-based research attempts via developing state-of-the-art techniques such as nanomechatronics ones and advanced materials including the sensors for detecting the pathogen loads at very low concentrations or site-specific delivery of therapeutics, and real-time protections against the pandemic outbreaks by nanorobots can provide outstanding biomedical breakthroughs. Considering the unique characteristics of pathogens particularly the newly-emerged ones and avoiding the exaggerated optimism or simplistic views on the prophylactic and therapeutic approaches including the one-size-fits-all ones or presenting multiple medications that may be associated with synergistic toxicities rather than enhanced efficiencies might pave the way towards the development of more appropriate treatment strategies with reduced safety concerns. This paper highlights the significance of nanoplatforms against the viral disorders and their capabilities of genome editing that may facilitate taking more appropriate measures against SARS-CoV-2. [Display omitted] [ABSTRACT FROM AUTHOR]