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The COVID-19 virus is spreading across the world very rapidly. The World Health Organization (WHO) declared it a global pandemic on 11 March 2020. Early detection of this virus is necessary because of the unavailability of any specific drug. The researchers have developed different techniques for COVID-19 detection, but only a few of them have achieved satisfactory results. There are three ways for COVID-19 detection to date, those are real-time reverse transcription-polymerize chain reaction (RT-PCR), Computed Tomography (CT), and X-ray plays. In this work, we have proposed a less expensive computational model for automatic COVID-19 detection from Chest X-ray and CT-scan images. Our paper has a two-fold contribution. Initially, we have extracted deep features from the image dataset and then introduced a completely novel meta-heuristic feature selection approach, named Clustering-based Golden Ratio Optimizer (CGRO). The model has been implemented on three publicly available datasets, namely the COVID CT-dataset, SARS-Cov-2 dataset, and Chest X-Ray dataset, and attained state-of-the-art accuracies of 99.31%, 98.65%, and 99.44%, respectively. [ABSTRACT FROM AUTHOR]

Considering the current pandemic, caused by the spreading of the novel Coronavirus disease, there is the urgent need for methods to quickly and automatically diagnose infection. To assist pathologists and radiologists in the detection of the novel coronavirus, in this paper we propose a two-tiered method, based on formal methods (to the best of authors knowledge never previously introduced in this context), aimed to (i) detect whether the patient lungs are healthy or present a generic pulmonary infection; (ii) in the case of the previous tier, a generic pulmonary disease is detected to identify whether the patient under analysis is affected by the novel Coronavirus disease. The proposed approach relies on the extraction of radiomic features from medical images and on the generation of a formal model that can be automatically checked using the model checking technique. We perform an experimental analysis using a set of computed tomography medical images obtained by the authors, achieving an accuracy of higher than 81% in disease detection. [ABSTRACT FROM AUTHOR]

Since December 2019, a new form of coronavirus, SARS-CoV-2, has spread from China to the whole word, raising concerns regarding Coronavirus Disease 2019 (COVID-19) endangering public health and life. Over 1.5 million deaths related with COVID-19 have been recorded worldwide, with wide variations among countries affected by the pandemic and continuously growing numbers. The aim of this paper was to provide an overview of the literature cases of deaths involving COVID-19 and to evaluate the application of the COVID-19 Significance Score (CSS) in the classification of SARS CoV-2-related fatalities, comparing it with the Hamburg rating scale. The results obtained allowed us to highlight that CSS used after a complete accurate post-mortem examination, coupled to the retrieval of in vivo data, post-mortem radiology, histology and toxicology, as well as to additional required analyses (e.g., electronic microscopy) is a useful and concise tool in the assessment of the cause of death and the role played by this virus. A shared use of this scale might hopefully lower the inhomogeneities in forensic evaluation of SARS CoV-2-related fatalities. [ABSTRACT FROM AUTHOR]

COVID-19 is a fast-growing disease all over the world, but facilities in the hospitals are restricted. Due to unavailability of an appropriate vaccine or medicine, early identification of patients suspected to have COVID-19 plays an important role in limiting the extent of disease. Lung computed tomography (CT) imaging is an alternative to the RT-PCR test for diagnosing COVID-19. Manual segmentation of lung CT images is time consuming and has several challenges, such as the high disparities in texture, size, and location of infections. Patchy ground-glass and consolidations, along with pathological changes, limit the accuracy of the existing deep learning-based CT slices segmentation methods. To cope with these issues, in this paper we propose a fully automated and efficient deep learning-based method, called LungINFseg, to segment the COVID-19 infections in lung CT images. Specifically, we propose the receptive-field-aware (RFA) module that can enlarge the receptive field of the segmentation models and increase the learning ability of the model without information loss. RFA includes convolution layers to extract COVID-19 features, dilated convolution consolidated with learnable parallel-group convolution to enlarge the receptive field, frequency domain features obtained by discrete wavelet transform, which also enlarges the receptive field, and an attention mechanism to promote COVID-19-related features. Large receptive fields could help deep learning models to learn contextual information and COVID-19 infection-related features that yield accurate segmentation results. In our experiments, we used a total of 1800+ annotated CT slices to build and test LungINFseg. We also compared LungINFseg with 13 state-of-the-art deep learning-based segmentation methods to demonstrate its effectiveness. LungINFseg achieved a dice score of 80.34 % and an intersection-over-union (IoU) score of 68.77 % —higher than the ones of the other 13 segmentation methods. Specifically, the dice and IoU scores of LungINFseg were 10 % better than those of the popular biomedical segmentation method U-Net. [ABSTRACT FROM AUTHOR]

Emerging deep learning (DL) approaches with edge computing have enabled the automation of rich information extraction, such as complex events from camera feeds. Due to the low speed and accuracy of object detection, some objects are missed and not detected. As objects constitute simple events, missing objects result in missing simple events, thus the number of detected complex events. As the main objective of this paper, an integrated cloud and edge computing architecture was designed and developed to reduce missing simple events. To achieve this goal, we deployed multiple smart cameras (i.e., cameras which connect to the Internet and are integrated with computerised systems such as the DL unit) in order to detect complex events from multiple views. Having more simple events from multiple cameras can reduce missing simple events and increase the number of detected complex events. To evaluate the accuracy of complex event detection, the F-score of risk behaviour regarding COVID-19 spread events in video streams was used. The experimental results demonstrate that this architecture delivered 1.73 times higher accuracy in event detection than that delivered by an edge-based architecture that uses one camera. The average event detection latency for the integrated cloud and edge architecture was 1.85 times higher than that of only one camera. However, this finding was insignificant with regard to the current case study. Moreover, the accuracy of the architecture for complex event matching with more spatial and temporal relationships showed significant improvement in comparison to the edge computing scenario. Finally, complex event detection accuracy considerably depended on object detection accuracy. Regression-based models, such as you only look once (YOLO), were able to provide better accuracy than region-based models. [ABSTRACT FROM AUTHOR]

The Coronavirus disease 2019 (COVID-19) has become one of the threats to the world. Computed tomography (CT) is an informative tool for the diagnosis of COVID-19 patients. Many deep learning approaches on CT images have been proposed and brought promising performance. However, due to the high complexity and non-transparency of deep models, the explanation of the diagnosis process is challenging, making it hard to evaluate whether such approaches are reliable. In this paper, we propose a visual interpretation architecture for the explanation of the deep learning models and apply the architecture in COVID-19 diagnosis. Our architecture designs a comprehensive interpretation about the deep model from different perspectives, including the training trends, diagnostic performance, learned features, feature extractors, the hidden layers, the support regions for diagnostic decision, and etc. With the interpretation architecture, researchers can make a comparison and explanation about the classification performance, gain insight into what the deep model learned from images, and obtain the supports for diagnostic decisions. Our deep model achieves the diagnostic result of 94.75%, 93.22%, 96.69%, 97.27%, and 91.88% in the criteria of accuracy, sensitivity, specificity, positive predictive value, and negative predictive value, which are 8.30%, 4.32%, 13.33%, 10.25%, and 6.19% higher than that of the compared traditional methods. The visualized features in 2-D and 3-D spaces provide the reasons for the superiority of our deep model. Our interpretation architecture would allow researchers to understand more about how and why deep models work, and can be used as interpretation solutions for any deep learning models based on convolutional neural network. It can also help deep learning methods to take a step forward in the clinical COVID-19 diagnosis field. [ABSTRACT FROM AUTHOR]

The concept of information is foundational to many disciplines yet also problematic and contested. This article contributes to the understanding of information through discussion of the findings of the interdisciplinary Difference That Makes a Difference (DTMD) project. DTMD used international conferences and workshops to bring together individuals from a wide range of disciplines to share how their field understands information, to engage in interdisciplinary conversations, and to contribute to edited publications. A simple answer to the question 'what is information?' is not forthcoming, but, it is argued, should no more be expected than would be an answer to 'what is matter?'. Nevertheless, through exploration of the areas of consensus that emerged from the bottom-up process of interdisciplinary dialogue, this paper offers ten assertions about the nature of information narratives for further debate. The assertions range from 'information requires a body', through 'information always has meaning' and 'information cannot be stored or communicated' to 'information is always shaped by power, authority and hierarchy'. This article finishes by illustrating and testing the assertions against an information case study of a team of medical experts disseminating information to the general public about the COVID-19 virus. [ABSTRACT FROM AUTHOR]

The importance of tourism for development is widely recognized. Travel restrictions imposed to contain the spread of COVID-19 have brought tourism to a halt. Tourism is one of the key sectors driving change in Africa and is based exclusively on natural assets, with wildlife being the main attraction. Economic activities, therefore, are clustered around conservation and protected areas. We used night-time light data as a proxy measure for economic activity to assess change due to the pandemic. Our analysis shows that overall, 75 percent of the 8427 protected areas saw a decrease in light intensity in varying degrees in all countries and across IUCN protected area categories, including in popular protected area destinations, indicating a reduction in tourism-related economic activities. As countries discuss COVID-19 recovery, the methods using spatially explicit data illustrated in this paper can assess the extent of change, inform decision-making, and prioritize recovery efforts. [ABSTRACT FROM AUTHOR]

The spread of the COVID-19 since the end of 2019 has reached an epidemic level and has quickly become a global public health crisis. During this period, the responses for COVID-19 were highly diverse and decentralized across countries and regions. Understanding the dynamics of human mobility change at high spatial temporal resolution is critical for assessing the impacts of non-pharmaceutical interventions (such as stay-at-home orders, regional lockdowns and travel restrictions) during the pandemic. However, this requires collecting traffic data at scale, which is time-consuming, cost-prohibitive and often not available (e.g., in underdeveloped countries). Therefore, spatiotemporal analysis through processing periodical remote-sensing images is very beneficial to enable efficient monitoring at the global scale. In this paper, we present a novel study that utilizes high temporal Planet multispectral images (from November 2019 to September 2020, on average 7.1 days of frequency) to detect traffic density in multiple cities through a proposed morphology-based vehicle detection method and evaluate how the traffic data collected in such a manner reflect mobility pattern changes in response to COVID-19. Our experiments at city-scale detection, demonstrate that our proposed vehicle detection method over this 3 m resolution data is able to achieve a detection level at an accuracy of 68.26% in most of the images, and the observations' trends coincide with existing public data of where available (lockdown duration, traffic volume, etc.), further suggesting that such high temporal Planet data with global coverage (although not with the best resolution), with well-devised detection algorithms, can sufficiently provide traffic details for trend analysis to better facilitate informed decision making for extreme events at the global level. [ABSTRACT FROM AUTHOR]

São Paulo is the most populous state in Brazil, home to around 22% of the country's population. The total number of Covid-19-infected people in São Paulo has reached more than 1 million, while its total death toll stands at 25% of all the country's fatalities. Joining the Brazilian academia efforts in the fight against Covid-19, in this paper we describe a unified framework for monitoring and forecasting the Covid-19 progress in the state of São Paulo. More specifically, a freely available, online platform to collect and exploit Covid-19 time-series data is presented, supporting decision-makers while still allowing the general public to interact with data from different regions of the state. Moreover, a novel forecasting data-driven method has also been proposed, by combining the so-called Susceptible-Infectious-Recovered-Deceased model with machine learning strategies to better fit the mathematical model's coefficients for predicting Infections, Recoveries, Deaths, and Viral Reproduction Numbers. We show that the obtained predictor is capable of dealing with badly conditioned data samples while still delivering accurate 10-day predictions. Our integrated computational system can be used for guiding government actions mainly in two basic aspects: real-time data assessment and dynamic predictions of Covid-19 curves for different regions of the state. We extend our analysis and investigation to inspect the virus spreading in Brazil in its regions. Finally, experiments involving the Covid-19 advance in other countries are also given. [ABSTRACT FROM AUTHOR]

The factors affecting the penetration of certain diseases such as COVID-19 in society are still unknown. Internet of Things (IoT) technologies can play a crucial role during the time of crisis and they can provide a more holistic view of the reasons that govern the outbreak of a contagious disease. The understanding of COVID-19 will be enriched by the analysis of data related to the phenomena, and this data can be collected using IoT sensors. In this paper, we show an integrated solution based on IoT technologies that can serve as opportunistic health data acquisition agents for combating the pandemic of COVID-19, named CIoTVID. The platform is composed of four layers—data acquisition, data aggregation, machine intelligence and services, within the solution. To demonstrate its validity, the solution has been tested with a use case based on creating a classifier of medical conditions using real data of voice, performing successfully. The layer of data aggregation is particularly relevant in this kind of solution as the data coming from medical devices has a very different nature to that coming from electronic sensors. Due to the adaptability of the platform to heterogeneous data and volumes of data; individuals, policymakers, and clinics could benefit from it to fight the propagation of the pandemic. [ABSTRACT FROM AUTHOR]

This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process. To this end, we adopted advanced deep network architectures and proposed a transfer learning strategy using custom-sized input tailored for each deep architecture to achieve the best performance. We conducted extensive sets of experiments on two CT image datasets, namely, the SARS-CoV-2 CT-scan and the COVID19-CT. The results show superior performances for our models compared with previous studies. Our best models achieved average accuracy, precision, sensitivity, specificity, and F1-score values of 99.4 % , 99.6 % , 99.8 % , 99.6 % , and 99.4 % on the SARS-CoV-2 dataset, and 92.9 % , 91.3 % , 93.7 % , 92.2 % , and 92.5 % on the COVID19-CT dataset, respectively. For better interpretability of the results, we applied visualization techniques to provide visual explanations for the models' predictions. Feature visualizations of the learned features show well-separated clusters representing CT images of COVID-19 and non-COVID-19 cases. Moreover, the visualizations indicate that our models are not only capable of identifying COVID-19 cases but also provide accurate localization of the COVID-19-associated regions, as indicated by well-trained radiologists. [ABSTRACT FROM AUTHOR]

To safely protect workplaces and the workforce during and after the COVID-19 pandemic, a scalable integrated sensing solution is required in order to offer real-time situational awareness and early warnings for decision-makers. However, an information-based solution for industry reopening is ineffective when the necessary operational information is locked up in disparate real-time data silos. There is a lot of ongoing effort to combat the COVID-19 pandemic using different combinations of low-cost, location-based contact tracing, and sensing technologies. These ad hoc Internet of Things (IoT) solutions for COVID-19 were developed using different data models and protocols without an interoperable way to interconnect these heterogeneous systems and exchange data on people and place interactions. This research aims to design and develop an interoperable Internet of COVID-19 Things (IoCT) architecture that is able to exchange, aggregate, and reuse disparate IoT sensor data sources in order for informed decisions to be made after understanding the real-time risks in workplaces based on person-to-place interactions. The IoCT architecture is based on the Sensor Web paradigm that connects various Things, Sensors, and Datastreams with an indoor geospatial data model. This paper presents a study of what, to the best of our knowledge, is the first real-world integrated implementation of the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) and IndoorGML standards to calculate the risk of COVID-19 online using a workplace reopening case study. The proposed IoCT offers a new open standard-based information model, architecture, methodologies, and software tools that enable the interoperability of disparate COVID-19 monitoring systems with finer spatial-temporal granularity. A workplace cleaning use case was developed in order to demonstrate the capabilities of this proposed IoCT architecture. The implemented IoCT architecture included proximity-based contact tracing, people density sensors, a COVID-19 risky behavior monitoring system, and the contextual building geospatial data. [ABSTRACT FROM AUTHOR]

The worldwide spread of the COVID-19 pandemic in 2020 forced most countries to intervene with policies and actions—including lockdowns, social-distancing and smart working measures—aimed at mitigating the health system and socio-economic disruption risks. The electricity sector was impacted as well, with performance largely reflecting the changes in the industrial and commercial sectors operations and in the social behavior patterns. The most immediate consequences concerned the power demand profiles, the generation mix composition and the electricity price trends. As a matter of fact, the electricity sectors experienced a foretaste of the future, with higher renewable energy penetration and concerns for security of supply. This paper presents a systemic approach toward assessing the impacts of the COVID-19 pandemic on the power sector. This is aimed at supporting decision making—particularly for policy makers, regulators, and system operators—by quantifying shorter term effects and identifying longer term impacts of the pandemic waves on the power system. Various metrics are defined in different areas—system operation, security, and electricity markets—to quantify those impacts. The methodology is finally applied to the European power system to produce a comparative assessment of the effects of the lockdown in the European context. [ABSTRACT FROM AUTHOR]

Simulation learning plays an important role in social work education, allowing students to explore how theory and practice parameters can be integrated into actual situations they are likely to experience in the field. The arrival of COVID-19 and the sudden cessation of in-field practicum opportunities raised challenges for students to gain needed practice experience. Simulation offers an opportunity to enhance learning in place of some direct experience when that is not available. This paper reports on a simulation development practicum, where students, not able to be in an agency, sought out ways to achieve learning through the development and implementation of simulation learning. This was combined with a literature review. Results showed that student-generated simulation could be used to support direct practice learning. This project also illustrated that social work simulation can be used to help students safely explore areas of practice that they may not be exposed to in practicum through scenarios that cause them to examine how to work with clients where cross-cultural needs exist, and challenge ethical dilemmas in a 'real-world' situation while being required to face their biases. [ABSTRACT FROM AUTHOR]

This paper examines people's privacy concerns, perceptions of social benefits, and acceptance of various COVID-19 control measures that harness location information using data collected through an online survey in the U.S. and South Korea. The results indicate that people have higher privacy concerns for methods that use more sensitive and private information. The results also reveal that people's perceptions of social benefits are low when their privacy concerns are high, indicating a trade-off relationship between privacy concerns and perceived social benefits. Moreover, the acceptance by South Koreans for most mitigation methods is significantly higher than that by people in the U.S. Lastly, the regression results indicate that South Koreans (compared to people in the U.S.) and people with a stronger collectivist orientation tend to have higher acceptance for the control measures because they have lower privacy concerns and perceive greater social benefits for the measures. These findings advance our understanding of the important role of geographic context and culture as well as people's experiences of the mitigation measures applied to control a previous pandemic. [ABSTRACT FROM AUTHOR]

With the wide availability of low-cost proximity sensors, a large body of research focuses on digital person-to-person contact tracing applications that use proximity sensors. In most contact tracing applications, the impact of SARS-CoV-2 spread through touching contaminated surfaces in enclosed places is overlooked. This study is focused on tracing human contact within indoor places using the open OGC IndoorGML standard. This paper proposes a graph-based data model that considers the semantics of indoor locations, time, and users' contexts in a hierarchical structure. The functionality of the proposed data model is evaluated for a COVID-19 contact tracing application with scalable system architecture. Indoor trajectory preprocessing is enabled by spatial topology to detect and remove semantically invalid real-world trajectory points. Results show that 91.18% percent of semantically invalid indoor trajectory data points are filtered out. Moreover, indoor trajectory data analysis is innovatively empowered by semantic user contexts (e.g., disinfecting activities) extracted from user profiles. In an enhanced contact tracing scenario, considering the disinfecting activities and sequential order of visiting common places outperformed contact tracing results by filtering out unnecessary potential contacts by 44.98 percent. However, the average execution time of person-to-place contact tracing is increased by 58.3%. [ABSTRACT FROM AUTHOR]

The world has faced a coronavirus outbreak, which, in addition to lung complications, has caused other serious problems, including cardiovascular. There is still no explanation for the mechanisms of coronavirus that trigger dysfunction of the cardiac autonomic nervous system (ANS). We believe that the complex mechanisms that change the status of ANS could only be solved by advanced multidimensional analysis of many variables, obtained both from the original cardiovascular signals and from laboratory analysis and detailed patient history. The aim of this paper is to analyze different measures of entropy as potential dimensions of the multidimensional space of cardiovascular data. The measures were applied to heart rate and systolic blood pressure signals collected from 116 patients with COVID-19 and 77 healthy controls. Methods that indicate a statistically significant difference between patients with different levels of infection and healthy controls will be used for further multivariate research. As a result, it was shown that a statistically significant difference between healthy controls and patients with COVID-19 was shown by sample entropy applied to integrated transformed probability signals, common symbolic dynamics entropy, and copula parameters. Statistical significance between serious and mild patients with COVID-19 can only be achieved by cross-entropies of heart rate signals and systolic pressure. This result contributes to the hypothesis that the severity of COVID-19 disease is associated with ANS disorder and encourages further research. [ABSTRACT FROM AUTHOR]

SARS-CoV-2 infection has spread all over the world in the last year, causing millions of COVID-19 cases among humans with a large variability of symptoms and signs, including those on the skin. Among these, a contemporary cluster of chilblain-like lesions with no certain relationship with the infection has been reported. The aim of this paper is to delineate a profile of chilblain-like lesions and to establish the state-of-the-art knowledge about this new phenomenon. [ABSTRACT FROM AUTHOR]

Background. Data in the literature report that a number of studies have attempted to identify the exact location of the cortical olfaction representation, searching for evidence suggesting that sniffing odors can initiate a primary activation of the piriform cortex and the insula. Nowadays, due to the SARS-CoV-2 (COVID-19) outbreak, the functional study of the olfactory system could offer a better understanding of the physiopathology of olfactory perception, elucidating better the possible site(s) of damage induced by the COVID-19 infection. The aim of this paper was to evaluate brain maps generated from functional Magnetic Resonance Imaging (fMRI) data, collected from healthy individuals in response to the same olfactory stimulus. Methods. A total of 45 healthy volunteers, without history and/or no clinical signs of sinonasal disease and without history and/or presence of olfactory dysfunction underwent fMRI assessment. Subjects were presented with the same odorous stimuli at specific intervals. fMRI generated brain maps were used in the identification of different cortical areas, involved in the stimuli perception. Results. The fMRI brain maps showed that odorous stimuli activate primarily the left anterior insula (in 35/45 cases or 77.8%). Other activated areas include: the low temporal gyri, the middle and superior temporal gyri, the frontal and piriform cortex, the anterior cingulate gyrus, the parahippocampal gyrus, the temporopolar area, the para-insular area, the subcentral area, the supramarginal gyrus, the occipital cortex and the cerebellum. Conclusions. fMRI resulted as a safe and reliable means to study the perception of olfaction in the cortex. The data of this study suggest that the anterior insula is the main stimulated area when olfactory stimuli are present. This area is always activated, despite the hand and nostril dominance. [ABSTRACT FROM AUTHOR]

Aims: Angiotensin-converting enzyme 2 (ACE2) plays an important role in the entry of coronaviruses into host cells. The current paper described how carnosine, a naturally occurring supplement, can be an effective drug candidate for coronavirus disease (COVID-19) on the basis of molecular docking and modeling to host ACE2 cocrystallized with nCoV spike protein. Methods: First, the starting point was ACE2 inhibitors and their structure–activity relationship (SAR). Next, chemical similarity (or diversity) and PubMed searches made it possible to repurpose and assess approved or experimental drugs for COVID-19. Parallel, at all stages, the authors performed bioactivity scoring to assess potential repurposed inhibitors at ACE2. Finally, investigators performed molecular docking and modeling of the identified drug candidate to host ACE2 with nCoV spike protein. Results: Carnosine emerged as the best-known drug candidate to match ACE2 inhibitor structure. Preliminary docking was more optimal to ACE2 than the known typical angiotensin-converting enzyme 1 (ACE1) inhibitor (enalapril) and quite comparable to known or presumed ACE2 inhibitors. Viral spike protein elements binding to ACE2 were retained in the best carnosine pose in SwissDock at 1.75 Angstroms. Out of the three main areas of attachment expected to the protein–protein structure, carnosine bound with higher affinity to two compared to the known ACE2 active site. LibDock score was 92.40 for site 3, 90.88 for site 1, and inside the active site 85.49. Conclusion: Carnosine has promising inhibitory interactions with host ACE2 and nCoV spike protein and hence could offer a potential mitigating effect against the current COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The ongoing COVID-19 pandemic has raised numerous questions concerning the shape and range of state interventions the goals of which are to reduce the number of infections and deaths. The lockdowns, which have become the most popular response worldwide, are assessed as being an outdated and economically inefficient way to fight the disease. However, in the absence of efficient cures and vaccines, there is a lack of viable alternatives. In this paper we assess the economic consequences of the epidemic prevention and control schemes that were introduced in order to respond to the COVID-19 pandemic. The analyses report the results of epidemic simulations that were obtained using the agent-based modelling methods under the different response schemes and their use in order to provide conditional forecasts of the standard economic variables. The forecasts were obtained using the dynamic stochastic general equilibrium model (DSGE) with the labour market component. [ABSTRACT FROM AUTHOR]

Emetine is a potent antiviral that acts on many viruses in the low-nM range, with several studies in animals and humans demonstrating antiviral activity. Historically, emetine was used to treat patients with Spanish influenza, in the last stages of the pandemic in the early 1900s. Some of these patients were "black" with cyanosis. Emetine rapidly reversed the cyanosis and other symptoms of this disease in 12–24 h. However, emetine also has been shown to have anti-inflammatory properties and it appears it is these anti-inflammatory properties that were responsible for the effects seen in patients with Spanish influenza. Emetine, in the past, has also been used in 10s to 100s of millions of people at a dose of ~60 mg daily to treat amoebiasis. Based on viral inhibition data we can calculate a likely SARS-CoV2 antiviral dose of ~1/10th the amoebiasis dose, which should dramatically reduce the risk of any side effects. While there are no anti-inflammatory dose response data available, based on the potential mode of action, the anti-inflammatory actions may also occur at low doses. This paper also examines the toxicity of emetine seen in clinical practice and that seen in the laboratory, and discusses the methods of administration aimed at reducing side effects if higher doses were found to be necessary. While emetine is a "pure drug" as it is extracted from ipecac, some of the differences between emetine and ipecac are also discussed. [ABSTRACT FROM AUTHOR]

The disease caused by the new coronavirus (COVID-19) has been plaguing the world for months and the number of cases are growing more rapidly as the days go by. Therefore, finding a way to identify who has the causative virus is impressive, in order to find a way to stop its proliferation. In this paper, a complete and applied study of convolutional support machines will be presented to classify patients infected with COVID-19 using X-ray data and comparing them with traditional convolutional neural network (CNN). Based on the fitted models, it was possible to observe that the convolutional support vector machine with the polynomial kernel ( C S V M P o l ) has a better predictive performance. In addition to the results obtained based on real images, the behavior of the models studied was observed through simulated images, where it was possible to observe the advantages of support vector machine (SVM) models. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic is considered a public health emergency of international concern. The 2019 novel coronavirus (2019-nCoV) or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that caused this pandemic has spread rapidly to over 200 countries, and has drastically affected public health and the economies of states at unprecedented levels. In this context, efforts around the world are focusing on solving this problem in several directions of research, by: (i) exploring the origin and evolution of the phylogeny of the SARS-CoV-2 viral genome; (ii) developing nanobiosensors that could be highly effective in detecting the new coronavirus; (iii) finding effective treatments for COVID-19; and (iv) working on vaccine development. In this paper, an overview of the progress made in the development of nanobiosensors for the detection of human coronaviruses (SARS-CoV, SARS-CoV-2, and Middle East respiratory syndrome coronavirus (MERS-CoV) is presented, along with specific techniques for modifying the surface of nanobiosensors. The newest detection methods of the influenza virus responsible for acute respiratory syndrome were compared with conventional methods, highlighting the newest trends in diagnostics, applications, and challenges of SARS-CoV-2 (COVID-19 causative virus) nanobiosensors. [ABSTRACT FROM AUTHOR]

At least since March 2020, the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) pandemic and the multi-organ coronavirus disease 2019 (COVID-19) are keeping a firm grip on the world. Although most cases are mild, older patients and those with co-morbidities are at increased risk of developing a cytokine storm, characterized by a systemic inflammatory response leading to acute respiratory distress syndrome and organ failure. The present paper focuses on the small molecule MP1032, describes its mode of action, and gives rationale why it is a promising option for the prevention/treatment of the SARS-CoV-2-induced cytokine storm. MP1032 is a phase-pure anhydrous polymorph of 5-amino-2,3-dihydro-1,4-phthalazinedione sodium salt that exhibits good stability and bioavailability. The physiological action of MP1032 is based on a multi-target mechanism including localized, self-limiting reactive oxygen species (ROS) scavenging activities that were demonstrated in a model of lipopolysaccharide (LPS)-induced joint inflammation. Furthermore, its immune-regulatory and PARP-1-modulating properties, coupled with antiviral effects against SARS-CoV-2, have been demonstrated in various cell models. Preclinical efficacy was elucidated in LPS-induced endotoxemia, a model with heightened innate immune responses that shares many similarities to COVID-19. So far, during oral clinical development with three-month daily administrations, no serious adverse drug reactions occurred, highlighting the outstanding safety profile of MP1032. [ABSTRACT FROM AUTHOR]

The rapid spread of the new Coronavirus Disease 2019 (COVID-19) has actually become the newest challenge for the healthcare system since, to date, there is not an effective treatment. Among all drugs tested, Hydroxychloroquine (HCQ) has attracted significant attention. This systematic review aims to analyze preclinical and clinical studies on HCQ potential use in viral infection and chronic diseases. A systematic search of Scopus and PubMed databases was performed to identify clinical and preclinical studies on this argument; 2463 papers were identified and 133 studies were included. Regarding HCQ activity against COVID-19, it was noticed that despite the first data were promising, the latest outcomes highlighted the ineffectiveness of HCQ in the treatment of viral infection. Several trials have seen that HCQ administration did not improve severe illness and did not prevent the infection outbreak after virus exposure. By contrast, HCQ arises as a first-line treatment in managing autoimmune diseases such as rheumatoid arthritis, lupus erythematosus, and Sjögren syndrome. It also improves glucose and lipid homeostasis and reveals significant antibacterial activity. [ABSTRACT FROM AUTHOR]

The goal of this paper is to analyze how the historical episode of the so-called Plague of Athens between the years 430 and 426 BC seems to have been the first phenomenon classified as an epidemic by Hippocrates, and the historian Thucydides described its cultural, social, political and religious consequences. However, such a crisis generated the need for a new culture, and consequently a new theological mentality, as a cultural driver that made it possible to transform the Asclepiad Sanctuary of Kos into the first hospital in the West to integrate spirituality and science as ways to promote the healing of culture in order to achieve the ideal of health. The adopted method was a semantic analysis of the classic texts that help contextualize the Hippocratic view of the epidemic, spirituality, and health, and how these questions were received by Christianity at the time. The reception of this experience by Christianity, despite suffering some tension, also expands this Greek ideal and constitutes a true heritage of ancient wisdom that can be revisited in the time of the new pandemic, COVID-19. The perspective assumed here is interdisciplinary, putting in dialogue Theology and Health Sciences. [ABSTRACT FROM AUTHOR]

Since the widespread of severe acute respiratory syndrome of coronavirus 2 (SARS-CoV-2) disease, the utilization of face masks has become omnipresent all over the world. Face masks are believed to contribute to an adequate protection against respiratory infections spread through micro-droplets among the infected person to non-infected others. However, due to the very high demands of face masks, especially the N95-type mask typically worn by medical workers, the public faces a shortage of face masks. Many papers have been published recently that focus on developing new and facile techniques to reuse and reinforce commercially available face masks. For instance, the N95 mask uses a polymer-based (membrane) filter inside, and the filter membrane can be replaced if needed. Another polymer sputtering technique by using a simple cotton candy machine could provide a cheap and robust solution for face mask fabrication. This review discuss the novel approaches of face mask reuse and reinforcement specifically by using membrane-based technology. Tuning the polymeric properties of face masks to enhance filterability and virus inactivity is crucial for future investigation. [ABSTRACT FROM AUTHOR]

Background: COVID-19 pathophysiology and the predictive factors involved are not fully understood, but lymphocytes dysregulation appears to play a role. This paper aims to evaluate lymphocyte subsets in the pathophysiology of COVID-19 and as predictive factors for severe disease. Patient and methods: A prospective cohort study of patients with SARS-CoV-2 bilateral pneumonia recruited at hospital admission. Demographics, medical history, and data regarding SARS-CoV-2 infection were recorded. Patients systematically underwent complete laboratory tests, including parameters related to COVID-19 as well as lymphocyte subsets study at the time of admission. Severe disease criteria were established at admission, and patients were classified on remote follow-up according to disease evolution. Linear regression models were used to assess associations with disease evolution, and Receiver Operating Characteristic (ROC) and the corresponding Area Under the Curve (AUC) were used to evaluate predictive values. Results: Patients with critical COVID-19 showed a decrease in CD3+CD4+ T cells count compared to non-critical (278 (485 IQR) vs. 545 (322 IQR)), a decrease in median CD4+/CD8+ ratio (1.7, (1.7 IQR) vs. 3.1 (2.4 IQR)), and a decrease in median CD4+MFI (21,820 (4491 IQR) vs. 26,259 (3256 IQR)), which persisted after adjustment. CD3+CD8+ T cells count had a high correlation with time to hospital discharge (PC = −0.700 (−0.931, −0.066)). ROC curves for predictive value showed lymphocyte subsets achieving the best performances, specifically CD3+CD4+ T cells (AUC = 0.756), CD4+/CD8+ ratio (AUC = 0.767), and CD4+MFI (AUC = 0.848). Conclusions: A predictive value and treatment considerations for lymphocyte subsets are suggested, especially for CD3CD4+ T cells. Lymphocyte subsets determination at hospital admission is recommended. [ABSTRACT FROM AUTHOR]

There is a current pandemic of a new type of coronavirus, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The number of confirmed infected cases has been rapidly increasing. This paper analyzes the characteristics of SARS-CoV-2 in comparison with Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV), Middle East Respiratory Syndrome Coronavirus (MERS-CoV) and influenza. COVID-19 is similar to the diseases caused by SARS-CoV and MERS-CoV virologically and etiologically, but closer to influenza in epidemiology and virulence. The comparison provides a new perspective for the future of the disease control, and offers some ideas in the prevention and control management strategy. The large number of infectious people from the origin, and the highly infectious and occult nature have been two major problems, making the virus difficult to eradicate. We thus need to contemplate the possibility of long-term co-existence with COVID-19. [ABSTRACT FROM AUTHOR]

(1) Background: Until now, several reports about pregnant women with confirmed coronavirus disease 2019 (COVID-19) have been published. However, there are no comprehensive systematic reviews collecting all case series studies on data regarding adverse pregnancy outcomes, especially association with treatment modalities. (2) Objective: We aimed to synthesize the most up-to-date and relevant available evidence on the outcomes of pregnant women with laboratory-confirmed infection with COVID-19. (3) Methods: PubMed, Scopus, MEDLINE, Google scholar, and Embase were explored for studies and papers regarding pregnant women with COVID-19, including obstetrical, perinatal, and neonatal outcomes and complications published from 1 January 2020 to 4 May 2020. Systematic review and search of the published literature was done using the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA). (4) Results: In total, 11 case series studies comprising 104 pregnant women with COVID-19 were included in our review. Fever (58.6%) and cough (30.7%) were the most common symptoms. Other symptoms included dyspnea (14.4%), chest discomfort (3.9%), sputum production (1.0%), sore throat (2.9%), and nasal obstruction (1.0%). Fifty-two patients (50.0%) eventually demonstrated abnormal chest CT, and of those with ground glass opacity (GGO), 23 (22.1%) were bilateral and 10 (9.6%) were unilateral. The most common treatment for COVID-19 was administration of antibiotics (25.9%) followed by antivirals (17.3%). Cesarean section was the mode of delivery for half of the women (50.0%), although no information was available for 28.8% of the cases. Regarding obstetrical and neonatal outcomes, fetal distress (13.5%), pre-labor rupture of membranes (9.6%), prematurity (8.7%), fetal death (4.8%), and abortion (2.9%) were reported. There are no positive results of neonatal infection by RT-PCR. (5) Conclusions: Although we have found that pregnancy with COVID-19 has significantly higher maternal mortality ratio compared to that of pregnancy without the disease, the evidence is too weak to state that COVID-19 results in poorer maternal outcome due to multiple factors. The number of COVID-19 pregnancy outcomes was not large enough to draw a conclusion and long-term outcomes are yet to be determined as the pandemic is still unfolding. Active and intensive follow-up is needed in order to provide robust data for future studies. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has tremendously changed private and professional interactions and behaviors worldwide. The effects of this pandemic and the actions taken have changed our healthcare systems, which consequently has affected medical education and surgical training. In the face of constant disruptions of surgical education and training during this pandemic outbreak, structured and innovative concepts and adapted educational curricula are important to ensure a high quality of medical treatment. While efforts were undertaken to prevent viral spreading, it is important to analyze and assess the effects of this crisis on medical education, surgical training and teaching at large and certainly in the field of surgical oncology. Against this background, in this paper we introduce practical and creative recommendations for the continuity of students' and residents' medical and surgical training and teaching. This includes virtual educational curricula, skills development classes, video-based feedback and simulation in the specialty field of surgical oncology. In conclusion, the effects of COVID 19 on Surgical Training and Teaching, certainly in the field of Surgical Oncology, are challenging. [ABSTRACT FROM AUTHOR]

One of the most effective ways to prevent the spread of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is to develop accurate and rapid diagnostic tests. There are a number of molecular, serological, and imaging methods that are used to diagnose this infection in hospitals and clinical settings. The purpose of this review paper is to present the available approaches for detecting SARS-CoV-2 and address the advantages and limitations of each detection method. This work includes studies from recent literature publications along with information from the manufacturer's manuals of commercially available SARS-CoV-2 diagnostic products. Furthermore, supplementary information from the Food & Drug Administration (FDA), Centers for Disease Control and Prevention (CDC), and World Health Organization (WHO) is cited. The viral components targeted for virus detection, the principles of each diagnostic technique, and the detection efficiency of each approach are discussed. The potential of using diagnostic tests that were originally developed for previous epidemic viruses is also presented. [ABSTRACT FROM AUTHOR]

Accurate diagnosis at an early stage of infection is essential for the successful management of any contagious disease. The coronavirus disease 2019 (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus is a pandemic that has affected 214 countries affecting more than 37.4 million people causing 1.07 million deaths as of the second week of October 2020. The primary diagnosis of the infection is done either by the molecular technique of RT-qPCR by detecting portions of the RNA of the viral genome or through immunodiagnostic tests by detecting the viral proteins or the antibodies produced by the host. As the demand for the test increased rapidly many naive manufacturers entered the market with novel kits and more and more laboratories also entered the diagnostic arena making the test result more error-prone. There are serious debates globally and regionally on the sensitivity and specificity of these tests and about the overall accuracy and reliability of the tests for decision making on control strategies. The significance of the test is also complexed by the presence of asymptomatic carriers, re-occurrence of infection in cured patients as well as by the varied incubation periods of the infection and shifting of the viral location in the host tissues. In this paper, we review the techniques available for SARS-CoV-2 diagnosis and probable factors that can reduce the sensitivity and specificity of the different test methods currently in vogue. We also provide a checklist of factors to be considered to avoid fallacious practices to reduce false positive and false negative results by the clinical laboratories. [ABSTRACT FROM AUTHOR]

Inflammation is a biological response to the activation of the immune system by various infectious or non-infectious agents, which may lead to tissue damage and various diseases. Gut commensal bacteria maintain a symbiotic relationship with the host and display a critical function in the homeostasis of the host immune system. Disturbance to the gut microbiota leads to immune dysfunction both locally and at distant sites, which causes inflammatory conditions not only in the intestine but also in the other organs such as lungs and brain, and may induce a disease state. Probiotics are well known to reinforce immunity and counteract inflammation by restoring symbiosis within the gut microbiota. As a result, probiotics protect against various diseases, including respiratory infections and neuroinflammatory disorders. A growing body of research supports the beneficial role of probiotics in lung and mental health through modulating the gut-lung and gut-brain axes. In the current paper, we discuss the potential role of probiotics in the treatment of viral respiratory infections, including the COVID-19 disease, as major public health crisis in 2020, and influenza virus infection, as well as treatment of neurological disorders like multiple sclerosis and other mental illnesses. [ABSTRACT FROM AUTHOR]

Simple Summary: One of the challenges facing the countries to contain the COVID-19 is to trace people that were in contact with an infected person. Failing to identify the possible infected people leads to unreported cases of the COVID-19, which results in massive infection among the population and even superinfection events. In this work, we study the impact of the lockdown implemented by three North African countries on reducing the infections in the pandemic's first wave. Then, we investigate the effect of the unreported cases in the increase of the number of infected people when each country relaxed the population's mobility in the "Eid" period, resulting in the second wave of the COVID-19. In this paper, we study a mathematical model investigating the impact of unreported cases of the COVID-19 in three North African countries: Algeria, Egypt, and Morocco. To understand how the population respects the restriction of population mobility implemented in each country, we use Google and Apple's mobility reports. These mobility reports help to quantify the effect of the population movement restrictions on the evolution of the active infection cases. We also approximate the number of the population infected unreported, the proportion of those that need hospitalization, and estimate the end of the epidemic wave. Moreover, we use our model to estimate the second wave of the COVID-19 Algeria and Morocco and to project the end of the second wave. Finally, we suggest some additional measures that can be considered to reduce the burden of the COVID-19 and would lead to a second wave of the spread of the virus in these countries. [ABSTRACT FROM AUTHOR]

Simple Summary: The introduction of immune checkpoint inhibitors (ICI) in 2011 revolutionized the management of many solid cancers and hematological malignancies. However, there are concerns regarding the use of ICI in the era of COVID-19. We present currently available information on the pros and cons of using ICI in cancer patients with respect to the risk of acquiring an infection by SARS-CoV2 and mortality from COVID-19. By means of the present paper, clinicians and researchers may update their knowledge on a highly topical clinical question—is the use of ICI in cancer patients with SARS-CoV2 infection harmful with respect to COVID-19 outcome? Whether cancer patients receiving immune checkpoint inhibitors (ICI) are at an increased risk of severe infection and mortality during the corona pandemic is a hotly debated topic that will continue to evolve. Here, we summarize and discuss current studies regarding COVID-19 and anti-cancer treatment with an emphasis on ICI. Importantly, several lines of evidence suggest that patients currently treated with ICI do not display an increased vulnerability to infection with SARS-CoV-2. Data regarding morbidity and mortality associated with COVID-19 in cancer patients receiving ICI are less clear and often conflicting. Although mostly based on experimental data, it is possible that ICI can promote the exacerbated immune response associated with adverse outcome in COVID-19 patients. On the other hand, mounting evidence suggests that ICI might even be useful in the treatment of viral infections by preventing or ameliorating T cell exhaustion. In this context, the right timing of treatment might be essential. Nevertheless, some cancer patients treated with ICI experience autoimmune-related side effects that require the use of immunosuppressive therapies, which in turn may promote a severe course of infection with SARS-CoV-2. Although there is clear evidence that withholding ICI will have more serious consequences, further studies are urgently needed in to better evaluate the effects of ICI in patients with COVID-19 and the use of ICI during the corona pandemic in general. [ABSTRACT FROM AUTHOR]

Functional Principal Component Analysis (FPCA) is an important dimension reduction technique to interpret the main modes of functional data variation in terms of a small set of uncorrelated variables. The principal components can not always be simply interpreted and rotation is one of the main solutions to improve the interpretation. In this paper, two new functional Varimax rotation approaches are introduced. They are based on the equivalence between FPCA of basis expansion of the sample curves and Principal Component Analysis (PCA) of a transformation of the matrix of basis coefficients. The first approach consists of a rotation of the eigenvectors that preserves the orthogonality between the eigenfunctions but the rotated principal component scores are not uncorrelated. The second approach is based on rotation of the loadings of the standardized principal component scores that provides uncorrelated rotated scores but non-orthogonal eigenfunctions. A simulation study and an application with data from the curves of infections by COVID-19 pandemic in Spain are developed to study the performance of these methods by comparing the results with other existing approaches. [ABSTRACT FROM AUTHOR]

Infection by the severe acute respiratory syndrome (SARS) coronavirus-2 (SARS-CoV-2) is the causative agent of a new disease (COVID-19). The risk of severe COVID-19 is increased by certain underlying comorbidities, including asthma, cancer, cardiovascular disease, hypertension, diabetes, and obesity. Notably, exposure to hormonally active chemicals called endocrine-disrupting chemicals (EDCs) can promote such cardio-metabolic diseases, endocrine-related cancers, and immune system dysregulation and thus, may also be linked to higher risk of severe COVID-19. Bisphenol A (BPA) is among the most common EDCs and exerts its effects via receptors which are widely distributed in human tissues, including nuclear oestrogen receptors (ERα and ERβ), membrane-bound oestrogen receptor (G protein-coupled receptor 30; GPR30), and human nuclear receptor oestrogen-related receptor gamma. As such, this paper focuses on the potential role of BPA in promoting comorbidities associated with severe COVID-19, as well as on potential BPA-induced effects on key SARS-CoV-2 infection mediators, such as angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2). Interestingly, GPR30 appears to exhibit greater co-localisation with TMPRSS2 in key tissues like lung and prostate, suggesting that BPA exposure may impact on the local expression of these SARS-CoV-2 infection mediators. Overall, the potential role of BPA on the risk and severity of COVID-19 merits further investigation. [ABSTRACT FROM AUTHOR]

This paper firstly studies an SIR (susceptible-infectious-recovered) epidemic model without demography and with no disease mortality under both total and under partial quarantine of the susceptible subpopulation or of both the susceptible and the infectious ones in order to satisfy the hospital availability requirements on bed disposal and other necessary treatment means for the seriously infectious subpopulations. The seriously infectious individuals are assumed to be a part of the total infectious being described by a time-varying proportional function. A time-varying upper-bound of those seriously infected individuals has to be satisfied as objective by either a total confinement or partial quarantine intervention of the susceptible subpopulation. Afterwards, a new extended SEIR (susceptible-exposed-infectious-recovered) epidemic model, which is referred to as an SEIAR (susceptible-exposed-symptomatic infectious-asymptomatic infectious-recovered) epidemic model with demography and disease mortality is given and focused on so as to extend the above developed ideas on the SIR model. A proportionally gain in the model parameterization is assumed to distribute the transition from the exposed to the infectious into the two infectious individuals (namely, symptomatic and asymptomatic individuals). Such a model is evaluated under total or partial quarantines of all or of some of the subpopulations which have the effect of decreasing the number of contagions. Simulated numerical examples are also discussed related to model parameterizations of usefulness related to the current COVID-19 pandemic outbreaks. [ABSTRACT FROM AUTHOR]

Simple Summary: Since the inception of COVID-19, the international and national agencies across the globe have issued several guidelines and recommendations for the public as well as to the healthcare professionals. The fast spreading nature of this disease has brought various changes in the screening and treatment protocols in medicine and dentistry. Oral radiology is an integral part of oral screening and various dental treatment strategies. During oral radiology procedures, oral radiologists/radiology technicians come in direct contact with the patient as well as their body fluids such as saliva. It increases the risk of disease transmission from patients to oral radiologist and vice versa. Hence, proper infection control protocol should be implemented with new guidelines. This paper explains the practices to be followed in the oral radiology clinics at various stages whether in commercial set up or academic institute. Additionally, it explains the various precautions and new recommendations to be taken during various radiographic techniques. To add more, emphasis the importance of safe distancing, barrier protection and decontamination to be followed in dental clinics to prevent the spread of infection. Furthermore, the feasibility and procedure of online teaching methods for oral radiology clinics has been discussed. The pandemic of Coronavirus disease (COVID-19) has emerged as a global catastrophe that is plaguing mankind. In the past eight months since the world discovered about COVID-19, we learned a lot about server acute respiratory syndrome coronavirus 2 (SARS CoV-2) and perhaps there is much more to discover and understand about the virus. With the current understanding of the disease, we assume it will remain in an active state of transmission and progression among the community for a long time. Thus, it is advisable to adopt the disease's prevention protocol in our daily and work routine. During this pandemic patient requiring dental treatment cannot be neglected and the role of dental imaging is crucial in delivering treatment. Hence, this article attempts to provide an evidence-based compilation about the mode of transmission and clinical features of COVID-19. It also throws light on the potential source of disease transmission in the dental radiology setting. In addition, it suggests preventive measures to curb the infection and infrastructural model of the clinical setting that will assist in achieving control over the disease transmission. This article intends to project a strategy about protocols, infrastructure, and daily activities in a dental radiology office that institutions can adopt with modifications according to their local scenario. [ABSTRACT FROM AUTHOR]

Because of the lack of reliable information on the spread parameters of COVID-19, there is an increasing demand for new approaches to efficiently predict the dynamics of new virus spread under uncertainty. The study presented in this paper is based on the Case-Based Reasoning method used in statistical analysis, forecasting and decision making in the field of public health and epidemiology. A new mathematical Case-Based Rate Reasoning model (CBRR) has been built for the short-term forecasting of coronavirus spread dynamics under uncertainty. The model allows for predicting future values of the increase in the percentage of new cases for a period of 2–3 weeks. Information on the dynamics of the total number of infected people in previous periods in Italy, Spain, France, and the United Kingdom was used. Simulation results confirmed the possibility of using the proposed approach for constructing short-term forecasts of coronavirus spread dynamics. The main finding of this study is that using the proposed approach for Russia showed that the deviation of the predicted total number of confirmed cases from the actual one was within 0.3%. For the USA, the deviation was 0.23%. [ABSTRACT FROM AUTHOR]

The popularity of deep reinforcement learning (DRL) applications in economics has increased exponentially. DRL, through a wide range of capabilities from reinforcement learning (RL) to deep learning (DL), offers vast opportunities for handling sophisticated dynamic economics systems. DRL is characterized by scalability with the potential to be applied to high-dimensional problems in conjunction with noisy and nonlinear patterns of economic data. In this paper, we initially consider a brief review of DL, RL, and deep RL methods in diverse applications in economics, providing an in-depth insight into the state-of-the-art. Furthermore, the architecture of DRL applied to economic applications is investigated in order to highlight the complexity, robustness, accuracy, performance, computational tasks, risk constraints, and profitability. The survey results indicate that DRL can provide better performance and higher efficiency as compared to the traditional algorithms while facing real economic problems in the presence of risk parameters and the ever-increasing uncertainties. [ABSTRACT FROM AUTHOR]

The Corona Virus Disease 2019 (COVID-19) appeared in Wuhan, China, at the end of 2019, spreading from there across China and within weeks across the whole world. In order to control the rapid spread of the virus, the Chinese government implemented a national lockdown policy. It restricted human mobility and non-essential economic activities, which, as a side effect, resulted in the reduction of the emission of pollutants and thus the improvement of the air quality in many cities in China. In this paper, we report on a study on the changes in air quality in the Guanzhong Basin during the COVID-19 lockdown period. We compared the concentrations of PM2.5, PM10, SO2, NO2, CO and O3 obtained from ground-based monitoring stations before and after the COVID-19 outbreak. The analysis confirmed that the air quality in the Guanzhong Basin was significantly improved after the COVID-19 outbreak. During the emergency response period with the strictest restrictions (Level-1), the concentrations of PM2.5, PM10, SO2, NO2 and CO were lower by 37%, 30%, 29%, 52% and 33%, respectively, compared with those before the COVID-19 outbreak. In contrast, O3 concentrations increased substantially. The changes in the pollutant concentrations varied between cities during the period of the COVID-19 pandemic. The highest O3 concentration changes were observed in Xi'an, Weinan and Xianyang city; the SO2 concentration decreased substantially in Tongchuan city; the air quality had improved the most in Baoji City. Next, to complement the sparsely distributed air quality ground-based monitoring stations, the geographic and temporally weighted regression (GTWR) model, combined with satellite observations of the aerosol optical depth (AOD) and meteorological factors was used to estimate the spatial and temporal distributions of PM2.5 and PM10 concentrations with a resolution of 6 km × 6 km before and after the COVID-19 outbreak. The model was validated by a comparison with ground-based observations from the air quality monitoring network in five cities in the Guanzhong Basin with excellent statistical metrics. For PM2.5 and PM10 the correlation coefficients R2 were 0.86 and 0.80, the root mean squared errors (RMSE) were 11.03 µg/m3 and 14.87 µg/m3 and the biases were 0.19 µg/m3 and −0.27 µg/m3, which led to the conclusion that the GTWR model could be used to estimate the PM concentrations in locations where monitoring data were not available. Overall, the PM concentrations in the Guanzhong Basin decreased substantially during the lockdown period, with a strong initial decrease and a slower one thereafter, although the spatial distributions remained similar. [ABSTRACT FROM AUTHOR]

The current COVID-19 pandemic is having a major impact on our daily lives. Social distancing is one of the measures that has been implemented with the aim of slowing the spread of the disease, but it is difficult for blind people to comply with this. In this paper, we present a system that helps blind people to maintain physical distance to other persons using a combination of RGB and depth cameras. We use a real-time semantic segmentation algorithm on the RGB camera to detect where persons are and use the depth camera to assess the distance to them; then, we provide audio feedback through bone-conducting headphones if a person is closer than 1.5 m. Our system warns the user only if persons are nearby but does not react to non-person objects such as walls, trees or doors; thus, it is not intrusive, and it is possible to use it in combination with other assistive devices. We have tested our prototype system on one blind and four blindfolded persons, and found that the system is precise, easy to use, and amounts to low cognitive load. [ABSTRACT FROM AUTHOR]

Social distancing and contact/exposure tracing are accepted to be critical strategies in the fight against the COVID-19 epidemic. They are both closely connected to the ability to reliably establish the degree of proximity between people in real-world environments. We proposed, implemented, and evaluated a wearable proximity sensing system based on an oscillating magnetic field that overcomes many of the weaknesses of the current state of the art Bluetooth based proximity detection. In this paper, we first described the underlying physical principle, proposed a protocol for the identification and coordination of the transmitter (which is compatible with the current smartphone-based exposure tracing protocols). Subsequently, the system architecture and implementation were described, finally an elaborate characterization and evaluation of the performance (both in systematic lab experiments and in real-world settings) were performed. Our work demonstrated that the proposed system is much more reliable than the widely-used Bluetooth-based approach, particularly when it comes to distinguishing between distances above and below the 2.0 m threshold due to the magnetic field's physical properties. [ABSTRACT FROM AUTHOR]

Background: To prioritize the development of antiviral compounds, it is necessary to compare their relative preclinical activity and clinical efficacy. Methods: We reviewed in vitro, animal model, and clinical studies of candidate anti-coronavirus compounds and placed extracted data in an online relational database. Results: As of August 2020, the Coronavirus Antiviral Research Database (CoV-RDB; covdb.stanford.edu) contained over 2800 cell culture, entry assay, and biochemical experiments, 259 animal model studies, and 73 clinical studies from over 400 published papers. SARS-CoV-2, SARS-CoV, and MERS-CoV account for 85% of the data. Approximately 75% of experiments involved compounds with known or likely mechanisms of action, including monoclonal antibodies and receptor binding inhibitors (21%), viral protease inhibitors (17%), miscellaneous host-acting inhibitors (10%), polymerase inhibitors (9%), interferons (7%), fusion inhibitors (5%), and host protease inhibitors (5%). Of 975 compounds with known or likely mechanism, 135 (14%) are licensed in the U.S. for other indications, 197 (20%) are licensed outside the U.S. or are in human trials, and 595 (61%) are pre-clinical investigational compounds. Conclusion: CoV-RDB facilitates comparisons between different candidate antiviral compounds, thereby helping scientists, clinical investigators, public health officials, and funding agencies prioritize the most promising compounds and repurposed drugs for further development. [ABSTRACT FROM AUTHOR]

Understanding of the pathogenesis of the coronavirus disease-2019 (COVID-19) remains incomplete, particularly in respect to the multi-organ dysfunction it may cause. We were the first to report the analogous biological and physiological features of COVID-19 pathogenesis and the harmful amplification loop between inflammation and tissue damage induced by the dysregulation of neutrophil extracellular traps (NETs) formation. Given the rapid evolution of this disease, the nature of its symptoms, and its potential lethality, we hypothesize that COVID-19 progresses under just such an amplifier loop, leading to a massive, uncontrolled inflammation process. Here, we describe in-depth the correlations of COVID-19 symptoms and biological features with those where uncontrolled NET formation is implicated in various sterile or infectious diseases. General clinical conditions, as well as numerous pathological and biological features, are analogous with NETs deleterious effects. Among NETs by-products implicated in COVID-19 pathogenesis, one of the most significant appears to be elastase, in accelerating virus entry and inducing hypertension, thrombosis and vasculitis. We postulate that severe acute respiratory syndrome-coronavirus 2 (SARS-CoV2) may evade innate immune response, causing uncontrolled NETs formation and multi-organ failure. In addition, we point to indicators that NETS-associated diseases are COVID-19 risk factors. Acknowledging that neutrophils are the principal origin of extracellular and circulating DNA release, we nonetheless, explain why targeting NETs rather than neutrophils themselves may in practice be a better strategy. This paper also offers an in-depth review of NET formation, function and pathogenic dysregulation, as well as of current and prospective future therapies to control NETopathies. As such, it enables us also to suggest new therapeutic strategies to fight COVID-19. In combination with or independent of the latest tested approaches, we propose the evaluation, in the short term, of treatments with DNase-1, with the anti-diabetic Metformin, or with drugs targeting elastase (i.e., Silvelestat). With a longer perspective, we also advocate a significant increase in research on the development of toll-like receptors (TLR) and C-type lectin-like receptors (CLEC) inhibitors, NET-inhibitory peptides, and on anti-IL-26 therapies. [ABSTRACT FROM AUTHOR]

(1) Background: The paper aims to review the available evidence regarding the health risk of the aerosolization induced by laparoscopy induced and impact of the COVID-19 pandemic upon minimally invasive surgery. (2) Materials and methods: A systematic review of the literature was performed on PubMed, Medline and Scopus until 10 July. (3) Results: Chemicals, carcinogens and biologically active materials, such as bacteria and viruses, have been isolated in surgical smoke. However, the only evidence of viral transmission through surgical smoke to medical staff is post-laser ablation of HPV-positive genital warts. The reports of SARS-CoV-2 infected patients who underwent laparoscopic surgery revealed the presence of the virus, when tested, in digestive wall and stools in 50% of cases but not in bile or peritoneal fluid. All surgeries did not result in contamination of the personnel, when protective measures were applied, including personal protective equipment (PPE) and filtration of the pneumoperitoneum. There are no comparative studies between classical and laparoscopic surgery. (4) Conclusions: Previously published data showed there is a possible infectious and toxic risk related to surgical smoke but not particularly proven for SARS-CoV-2. Implementing standardized filtration systems for smoke evacuation during laparoscopy, although increases costs, is necessary to increase the safety and it will probably remain a routine also in the future. [ABSTRACT FROM AUTHOR]