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This paper provides a comprehensive summary of evidence to explore and position the role of serology testing in the context of coronavirus disease 19 (COVID-19) immunization and policy response in the Asia-Pacific (APAC) region. The document builds on a review of academic literature and existing policies followed by a process of discussion, validation, and feedback by a group of six experts. Six countries and territories—Australia, Hong Kong, India, Indonesia, Thailand, and Taiwan—were sampled to highlight the differing contexts and scenarios in the region. The review includes an overview of (1) the impact of the COVID-19 pandemic, including the emergence of Variants of Concern (VOCs), especially Omicron, (2) the introduction of immunization, (3) the available testing options and potential use of serology testing, (4) the landscape of guidelines and recommendations for their use, and (5) the barriers and challenges to implementing serology testing as a tool to support COVID-19 immunization. Based on the findings, the co-authors propose a set of recommendations to resolve knowledge gaps, to include the use of serology testing as part of the policy response, and to ensure adequate means of implementation. This paper's target audience includes members of the academic community, medical societies, health providers and practitioners, and decision-makers. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has resulted in millions of fatalities worldwide. The case of pediatric cancer patients stands out since, despite being considered a population at risk, few studies have been carried out concerning symptom detection or the description of the mechanisms capable of modifying the course of the COVID-19 disease, such as the interaction and response between the virus and the treatment given to cancer patients. By synthesizing existing studies, this paper aims to expose the treatment challenges for pediatric patients with COVID-19 in an oncology context. Additionally, this updated review includes studies that utilized the antiviral agents Remdesivir and PaxlovidTM in pediatric cancer patients. There is no specific treatment designed exclusively for pediatric cancer patients dealing with COVID-19, and it is advisable to avoid self-medication to prevent potential side effects. Managing COVID-19 in pediatric cancer patients is indeed a substantial challenge. New strategies, such as chemotherapy application rooms, have been implemented for children with cancer who were positive for COVID-19 but asymptomatic since the risk of disease progression is greater than the risk of complications from SARS-CoV-2. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic affected nearly all strata of life, including scientific activities. Implementation of nationwide lockdowns, closures of universities, and other measures significantly limiting social mobility precluded field and laboratory research from being pursued at a pre-pandemic pace. On the other hand, working from home could translate into higher productivity of researchers in terms of publication output, ultimately affecting the journals' metrics. This effect has been well evidenced in the biomedical field, but whether it also occurred in aquatic sciences has not been explored. Therefore, this study aimed to analyze and compare various metrics of journals classified in the Scopus evaluations in 2019 (encompassing 2016–2019, the pre-pandemic period) and 2022 (covering 2019–2022, the pandemic period) within three subjects: aquatic science, oceanography, and water science and technology. In general, the number of journals classified into the considered categories increased in 2022 by 12%, and the majority (78%) experienced an increase in the CiteScore metric. The total published articles and received citations also significantly increased in 2022 by 17.5% and 63.5%, respectively, and were even higher in the case of open access journals—69% and 145%, respectively. The overall share of cited articles in the considered period was higher by 5% than in the 2019 Scopus evaluation and by 7% in the case of open access periodicals. However, source normalized impact per paper (SNIP) and the SCImago journal rank (SJR) values decreased by 7% and 8%, respectively. The highest share of citations was generated by Water Research, Water, and the Journal of Hydrology, and the highest number of articles was published by Water, Frontiers in Marine Science, and Desalination and Water Treatment. Only five of the COVID-19-related papers published in journals with the highest CiteScore and total number of citations received more than 200 citations, but their contribution to citations received by the journals was not high. In summary, the present study's findings indicate the high potential of the field dedicated to water sciences to adapt in a period challenged by an infectious disease pandemic. However, they do not exclude the adverse effect that this period could have on the activities of individual scientists and research teams. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the journal Brain Sciences titled "Neurological and Psychiatric Disorders in the COVID-19 Era." The issue aims to expand knowledge about the impact of COVID-19 on neurological and psychiatric disorders and how the outbreak has affected their management. The issue includes six original scientific papers. One study found an association between pulmonary embolism in COVID-19 patients and neurological complications such as stroke and seizures. Another study reviewed the possible mechanisms of SARS-CoV-2 damage in the body, including neurological disorders. A systematic review identified risk factors for depressive symptoms during COVID-19-related restrictions. Another study evaluated long-term neuropsychiatric sequelae in COVID-19 survivors and found that a significant portion still experienced symptoms. A review focused on the impact of COVID-19 on neurology training, and the final paper examined emotional distress in COVID-19 patients admitted to a rehabilitation unit. The special issue aims to provide new insights for future research initiatives. [Extracted from the article]

The evolutionary dynamics of viruses, particularly exemplified by SARS-CoV-2 during the ongoing COVID-19 pandemic, underscore the intricate interplay between genetics, host adaptation, and viral spread. This paper delves into the genetic evolution of SARS-CoV-2, emphasizing the implications of viral variants on global health. Initially emerging from the Wuhan-Hu-1 lineage, SARS-CoV-2 rapidly diversified into numerous variants, each characterized by distinct mutations in the spike protein and other genomic regions. Notable variants such as B.1.1.7 (α), B.1.351 (β), P.1 (γ), B.1.617.2 (δ), and the Omicron variant have garnered significant attention due to their heightened transmissibility and immune evasion capabilities. In particular, the Omicron variant has presented a myriad of subvariants, raising concerns about its potential impact on public health. Despite the emergence of numerous variants, the vast majority have exhibited limited expansion capabilities and have not posed significant threats akin to early pandemic strains. Continued genomic surveillance is imperative to identify emerging variants of concern promptly. While genetic adaptation is intrinsic to viral evolution, effective public health responses must be grounded in empirical evidence to navigate the evolving landscape of the pandemic with resilience and precision. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, microbiological controls neglected the spread of viruses through the air. Techniques to identify this threat required additional research to enable control measures to be introduced to protect against the spread of disease through this route. Due to the very high level of risk occurring during research on the COVID-19 and SARS-CoV-2 viruses, it seems necessary to use analogous microorganisms that will allow, through an experiment, to validate or challenge a method that stops the spread of infectious microorganisms, without unnecessary risk to research staff. The presented work was carried out to assess the possibility of using airborne microorganisms that are safe for humans for this type of research. The work presents the selection process of bacteria and viruses (bacteriophages) that have the greatest potential for use in experimental studies on airborne-droplet transmission indoors, especially in hospital facilities. In the study, it was assumed that determining the survival rates of groups of organisms would allow them to be used as a proxy for studying more dangerous bacteria and viruses. Survival studies of selected microorganisms were carried out, and the paper selected microorganisms with the highest survival rate in a given environment. [ABSTRACT FROM AUTHOR]

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for coronavirus disease-19 (COVID-19). This virus has caused a global pandemic, marked by several mutations leading to multiple waves of infection. This paper proposes a comprehensive and integrative mathematical approach to the third wave of COVID-19 (Omicron) in the Kingdom of Saudi Arabia (KSA) for the period between 16 December 2022 and 8 February 2023. It may help to implement a better response in the next waves. For this purpose, in this article, we generate a new mathematical transmission model for coronavirus, particularly during the third wave in the KSA caused by the Omicron variant, factoring in the impact of vaccination. We developed this model using a fractal-fractional derivative approach. It categorizes the total population into six segments: susceptible, vaccinated, exposed, asymptomatic infected, symptomatic infected, and recovered individuals. The conventional least-squares method is used for estimating the model parameters. The Perov fixed point theorem is utilized to demonstrate the solution's uniqueness and existence. Moreover, we investigate the Ulam–Hyers stability of this fractal–fractional model. Our numerical approach involves a two-step Newton polynomial approximation. We present simulation results that vary according to the fractional orders (γ) and fractal dimensions (θ), providing detailed analysis and discussion. Our graphical analysis shows that the fractal-fractional derivative model offers more biologically realistic results than traditional integer-order and other fractional models. [ABSTRACT FROM AUTHOR]

During the SARS-CoV-2 pandemic, the air quality and infection risk in classrooms were the focus of many investigations. Despite general recommendations for sufficient ventilation, quantitative analyses were often lacking due to the large number of combinations of boundary conditions. Hence, in this paper, we describe a computational fluid dynamics model that predicts the time-resolved airflow for a typical 45 min classroom scenario. We model 28 students and a teacher, each emitting CO2 and an individual aerosol. We investigated 13 ventilation setups with window or mechanical ventilation and different positions and operating conditions of an additional air purifier. The ventilation performance is assessed by evaluating the ventilation effectiveness, aerosol removal effectiveness, local air exchange efficiency and overall inhaled aerosol mass of the occupants, which is a measure of the infection risk. If the window is opened according to the "20-5-20" recommendation, the incoming airflow reduces both the CO2 and aerosol concentration whilst decreasing the thermal comfort at low ambient temperatures. An active air purifier enhances aerosol removal, but, depending on the position, the local air exchange efficiency and individual aerosol inhalation vary. If mechanical ventilation with 700 m3/h is utilised, the CO2 concentration is kept below 1250 ppm while also effectively removing aerosol from the classroom. [ABSTRACT FROM AUTHOR]

The emergence of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has sparked intense research on its spike protein, which is essential for viral entrance into host cells. Viral reproduction and transmission, host immune response regulation, receptor recognition and host cell entrance mechanisms, as well as structural and functional effects have all been linked to mutations in the spike protein. Spike protein mutations can also result in immune evasion mechanisms that impair vaccine effectiveness and escape, and they are linked to illness severity and clinical consequences. Numerous studies have been conducted to determine the effects of these mutations on the spike protein structure and how it interacts with host factors. These results have important implications for the design and development of medicines and vaccines based on spike proteins as well as for the assessment of those products' efficiency against newly discovered spike protein mutations. This paper gives a general overview of how spike protein mutations are categorized and named. It further looks at the links between spike protein mutations and clinical outcomes, illness severity, unanswered problems, and future research prospects. Additionally, explored are the effects of these mutations on vaccine effectiveness as well as the possible therapeutic targeting of spike protein mutations. [ABSTRACT FROM AUTHOR]

The coronavirus disease-2019 (COVID-19) pandemic has had a profound impact on the world, causing loss of life, economic damage, and social disruption. Individuals with chronic kidney disease (CKD) are prone to complications and increased mortality related to COVID-19. Efforts have been made to increase understanding of the effects of COVID-19 in individuals with CKD. This paper aims to gather and discuss the state-of-the-art in the COVID-19 and CKD literature, involving the early history of COVID-19, the immunological aspects of CKD (such as abnormalities in neutrophilicand dendritic cells functions), the mechanisms of kidney injury by SARS-CoV-2 (for example, viral tropism to kidney tissue and direct cytotoxicity), the clinical course of the disease and consequences for CKD individuals (including kidney transplant recipients), and the emerging treatments (such as the use of Remdesivir, nirmatrelvir, and monoclonal neutralizing antibodies) and immunization for the CKD population. [ABSTRACT FROM AUTHOR]

The coronavirus disease (COVID-19) pandemic has placed a massive impact on global civilization. Finding effective treatments and drugs for these viral diseases was crucial. This paper outlined and highlighted key elements of recent advances in nonthermal biocompatible plasma (NBP) technology for antiviral applications. We searched for papers on NBP virus inactivation in PubMed ePubs, Scopus, and Web of Science databases. The data and relevant information were gathered in order to establish a mechanism for NBP-based viral inactivation. NBP has been developed as a new, effective, and safe strategy for viral inactivation. NBP may be used to inactivate viruses in an ecologically friendly way as well as activate animal and plant viruses in a number of matrices. The reactive species have been shown to be the cause of viral inactivation. NBP-based disinfection techniques provide an interesting solution to many of the problems since they are simply deployable and do not require the resource-constrained consumables and reagents required for traditional decontamination treatments. Scientists are developing NBP technology solutions to assist the medical community in dealing with the present COVID-19 outbreak. NBP is predicted to be the most promising strategy for battling COVID-19 and other viruses in the future. [ABSTRACT FROM AUTHOR]

Vaccination is an important tool in the fight against the COVID-19 pandemic in patients with haematologic malignancies. The paper provides an analysis of the course of breakthrough SARS-CoV-2 infection in a group of vaccinated patients with haematological malignancy and a comparison with a historical cohort of 96 non-vaccinated patients with haematologic malignancies and bone marrow failure syndromes (two patients) in the treatment of COVID-19. A severe or critical course of COVID-19 was significantly less frequent in the group of vaccinated patients (10.2% vs. 31.4%, p = 0.003). The need for hospitalisation due to COVID-19 was significantly lower in vaccinated patients (27.1% vs. 72.6%, p < 0.0001) and the duration of hospitalisation was significantly shorter (10 vs. 14 days, p = 0.045). Vaccinated patients were insignificantly less likely to require oxygen therapy during infection. COVID-19 mortality was significantly higher in non-vaccinated patients (15.6% vs. 5.1%, p = 0.047). The paper demonstrated a significant positive effect of vaccination against COVID-19 on a less severe clinical course of infection, lower need for hospitalisation and mortality. However, the results need to be evaluated even in the context of new antivirals and monoclonal antibodies against SARS-CoV-2 or virus mutations with different biological behaviour. [ABSTRACT FROM AUTHOR]

During the outbreak of COVID-19, many SARS-CoV-2 variants presented key amino acid mutations that influenced their binding abilities with angiotensin-converting enzyme 2 (hACE2) and neutralizing antibodies. For the B.1.617 lineage, there had been fears that two key mutations, i.e., L452R and E484Q, would have additive effects on the evasion of neutralizing antibodies. In this paper, we systematically investigated the impact of the L452R and E484Q mutations on the structure and binding behavior of B.1.617.1 using deep learning AlphaFold2, molecular docking and dynamics simulation. We firstly predicted and verified the structure of the S protein containing L452R and E484Q mutations via the AlphaFold2-calculated pLDDT value and compared it with the experimental structure. Next, a molecular simulation was performed to reveal the structural and interaction stabilities of the S protein of the double mutant variant with hACE2. We found that the double mutations, L452R and E484Q, could lead to a decrease in hydrogen bonds and higher interaction energy between the S protein and hACE2, demonstrating the lower structural stability and the worse binding affinity in the long dynamic evolutional process, even though the molecular docking showed the lower binding energy score of the S1 RBD of the double mutant variant with hACE2 than that of the wild type (WT) with hACE2. In addition, docking to three approved neutralizing monoclonal antibodies (mAbs) showed a reduced binding affinity of the double mutant variant, suggesting a lower neutralization ability of the mAbs against the double mutant variant. Our study helps lay the foundation for further SARS-CoV-2 studies and provides bioinformatics and computational insights into how the double mutations lead to immune evasion, which could offer guidance for subsequent biomedical studies. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic necessitated sensitive, fast, and inexpensive testing for the virus in 2020 prior to the widespread availability of vaccines. Early testing efforts were limited by bottlenecks on reagents, low-throughput testing options, and the slow return of test results. In this paper, we detail the testing pipeline we established at the University of Wisconsin-Madison for rapid, inexpensive, and sensitive surveillance testing for SARS-CoV-2, and we highlight the strengths of the platform that would allow it to be applied to other disease surveillance projects, SARS-CoV-2 variant testing, or future pandemics. This pipeline can be quickly established for further accreditation and clinical application. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease (SARS-CoV-2) has caused many infections and deaths throughout the world; the spread of the coronavirus pandemic is still ongoing and continues to affect healthcare systems and economies of countries worldwide. Mathematical models are used in many applications for infectious diseases, including forecasting outbreaks and designing containment strategies. In this paper, we study two types of SIR and SEIR models for the coronavirus. This study focuses on the discrete-time and fractional-order of these models; we study the stability of the fixed points and orbits using the Jacobian matrix and the eigenvalues and eigenvectors of each case; moreover, we estimate the parameters of the two systems in fractional order. We present a statistical study of the coronavirus model in two countries: Saudi Arabia, which has successfully recovered from the SARS-CoV-2 pandemic, and China, where the number of infections remains significantly high. [ABSTRACT FROM AUTHOR]

As the COVID-19 pandemic begins to abate and national public health systems are treating the SARS-CoV-2 virus as endemic, many public health measures are no longer mandated but remain recommended with voluntary participation. One of these is the wearing of fitted face masks, initially mandated to contain, or at least slow, the spread of SARS-CoV-2, which is primarily transmitted via aerosols emitted while breathing, coughing, or sneezing. Although the habit of once wearing fitted face masks recedes into memory for much of the population, so does the knowledge of the various types of masks that were once en vogue. To create a record for the future, this paper provides the first comprehensive documentation of the nature and range of fitted facemasks that circulated during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a SIVR model using the Laplace Adomian decomposition. This model focuses on a new trend in mathematical epidemiology dedicated to studying the characteristics of vaccination of infected communities. We analyze the epidemiological parameters using equilibrium stability and numerical analysis techniques. New mathematical strategies are also applied to establish our epidemic model, which is a pandemic model as well. In addition, we mathematically establish the chance for the next wave of any pandemic disease and show that a consistent vaccination strategy could control it. Our proposal is the first model introducing a vaccination strategy to actively infected cases. We are sure this work will serve as the basis for future research on COVID-19 and pandemic diseases since our study also considers the vaccinated population. [ABSTRACT FROM AUTHOR]

This paper attempts to identify the changes in religiosity among Catholic practitioners in Poland that occurred during the COVID-19 pandemic, based on the changes in the realization of religious practices by students (N = 354). A questionnaire prepared by the authors was used for the study. We sought to answer the question of the extent to which the socio-demographic characteristics of the subjects, a history of COVID-19, and the death of a family member due to SARS-CoV-2 infection differentiated the subjects' opinions about religiosity and their undertaking of religious practices. The paper also addresses the issue of media-facilitated religious practices. The study showed that the frequency of religious practices was influenced by the perceived religiosity of the family, the religiosity of the respondent, and the declaration concerning the belief in God. The same factors most often significantly differentiated respondents' opinions on COVID-19. No relationship was confirmed between having COVID-19 and subjects' beliefs about the pandemic or frequency of religious practice. Among the respondents, the pandemic did not intensify the practice of religion. In the face of danger, respondents did not turn to God; there was no revitalization of religion. The situation of limiting the physical experience communion of the Church was treated not as an acute undersupply but as an opportunity or a pretext to abandon the practice of religion. [ABSTRACT FROM AUTHOR]

Simple Summary: The COVID-19 pandemic significantly affected the clinical outcomes and management of patients with hematological malignancies, who are especially vulnerable to infections. At the time of the pandemic outbreak, when a highly virulent wild-type strain of SARS-CoV-2 (B.1) was responsible for the majority of infections, mortality rates among hematology-oncology patients was high. As no specific prevention or treatment was available at the time, patients were strictly advised to wear masks and practice physical distancing and hand hygiene. When anti-cancer treatment was needed, protocols with oral drugs on an outpatient basis were preferred to avoid hospitalization whenever possible. Despite this, many hemato-oncological patients died from COVID-19, secondary bacterial or fungal infections as well as from delayed or suboptimal antineoplastic treatment. Less virulent viral variants contributed to reducing the problem with COVID-19 in the general population, but patients with hematologic malignancies are still at high risk of suffering from SARS-CoV-2 or COVID-19 infection with a severe or long clinical course. These patients are advised to get vaccinated, wear a face mask and avoid poorly ventilated or crowded places. CDC guidelines recommend starting antiviral treatment as soon as possible in case of a positive SARS-CoV-2 test, i.e., within 5 days of the first symptoms. The COVID-19 pandemic is undoubtedly the most difficult health challenge of the 21st century with more than 600 million laboratory-confirmed SARS-CoV-2 infections and over 6.5 million deaths worldwide. The coronavirus pandemic contributed to rapid development of mRNA vaccines, which, along with new antiviral drugs, have been the subject of extensive research for many decades. Nevertheless, elderly, multi-morbid and immunocompromised patients continue to face a more severe clinical course and a higher risk of death from COVID-19, even now that the risk of COVID-19 in the general population is significantly reduced due to the introduction of global vaccination strategies. In this paper, we present the mechanisms of increased susceptibility to infectious complications and the evolution of the clinical course of COVID-19 in patients with hematological malignancies, taking into account the mutation of the virus and the introduction of vaccines and new antiviral drugs. We also present current recommendations for prophylactic and therapeutic management in patients with hematological malignancies. [ABSTRACT FROM AUTHOR]

Quick and reliable mass testing of infected people is an effective tool for the contingency of SARS-CoV-2. During the COVID-19 pandemic, Point-of-Care (POC) tests using Loop-Mediated Isothermal Amplification (LAMP) arose as a useful diagnostic tool. LAMP tests are a robust and fast alternative to Polymerase Chain Reaction (PCR), and their isothermal property allows easy incorporation into POC platforms. The main drawback of using colorimetric LAMP is the reported short-term stability of the pre-mixed reagents, as well as the relatively high rate of false-positive results. Also, low-magnitude amplification can produce a subtle color change, making it difficult to discern a positive reaction. This paper presents Hilab Molecular, a portable device that uses the Internet of Things and Artificial Intelligence to pre-analyze colorimetric data. In addition, we established manufacturing procedures to increase the stability of colorimetric RT-LAMP tests. We show that ready-to-use reactions can be stored for up to 120 days at −20 °C. Furthermore, we validated both the Hilab Molecular device and the Hilab RT-LAMP test for SARS-CoV-2 using 581 patient samples without any purification steps. We achieved a sensitivity of 92.93% and specificity of 99.42% (samples with CT ≤ 30) when compared to RT-qPCR. [ABSTRACT FROM AUTHOR]

COVID-19 is an ongoing global pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Although it primarily attacks the respiratory tract, inflammation can also affect the central nervous system (CNS), leading to chemo-sensory deficits such as anosmia and serious cognitive problems. Recent studies have shown a connection between COVID-19 and neurodegenerative diseases, particularly Alzheimer's disease (AD). In fact, AD appears to exhibit neurological mechanisms of protein interactions similar to those that occur during COVID-19. Starting from these considerations, this perspective paper outlines a new approach based on the analysis of the complexity of brain signals to identify and quantify common features between COVID-19 and neurodegenerative disorders. Considering the relation between olfactory deficits, AD, and COVID-19, we present an experimental design involving olfactory tasks using multiscale fuzzy entropy (MFE) for electroencephalographic (EEG) signal analysis. Additionally, we present the open challenges and future perspectives. More specifically, the challenges are related to the lack of clinical standards regarding EEG signal entropy and public data that can be exploited in the experimental phase. Furthermore, the integration of EEG analysis with machine learning still requires further investigation. [ABSTRACT FROM AUTHOR]

Bacterial co-infections increase the severity of respiratory viral infections and are frequent causes of mortality in COVID-19 infected subjects. During the COVID-19 period, especially at the beginning of the pandemic, an inappropriate use of broad-spectrum antibiotic treatments has been frequently described, mainly due to prolonged hospitalization, especially in intensive care unit departments, and the use of immune-suppressive treatments as steroids. This misuse has finally led to the occurrence of infections by multi-drug resistant (MDR) bacteria in hospitalized COVID-19 patients. Although different reports assessed the prevalence of Gram-negative infections in COVID-19 infected patients, scarce data are currently available on bloodstream infections caused by Pseudomonas aeruginosa in hospitalized COVID-19 patients. The aim of our systematic review is to describe data on this specific population and to discuss the possible implications that these co-infections could have in the management of COVID-19 pandemics in the future. We systematically analysed the current literature to find all the relevant articles that describe the occurrence of P. aeruginosa bloodstream infections in COVID-19 patients. We found 40 papers that described in detail P. aeruginosa HAIs-BSI in COVID-19 patients, including 756,067 patients overall. The occurrence of severe infections due to MDR bacteria had a significant impact in the management of hospitalized patients with COVID-19 infections, leading to a prolonged time of hospitalization and to a consequent increase in mortality. In the near future, the increased burden of MDR bacteria due to the COVID-19 pandemic might partially be reduced by maintaining the preventive measures of infection control implemented during the acute phase of the COVID-19 pandemic. Finally, we discuss how the COVID-19 pandemic changed the role of antimicrobial stewardship in healthcare settings, according to the isolation of MDR bacteria and how to restore on a large scale the optimization of antibiotic strategies in COVID-19 patients. [ABSTRACT FROM AUTHOR]

In this paper, we present the model of the interaction between the spread of disease and the spread of information about the disease in multilayer networks. Next, based on the characteristics of the SARS-CoV-2 virus pandemic, we evaluated the influence of information blocking on the virus spread. Our results show that blocking the spread of information affects the speed at which the epidemic peak appears in our society, and affects the number of infected individuals. [ABSTRACT FROM AUTHOR]

The oral cavity is a unique environment that consists of teeth surrounded by periodontal tissues, oral mucosae with minor salivary glands, and terminal parts of major salivary glands that open into the oral cavity. The cavity is constantly exposed to viral and microbial pathogens. Recent studies indicate that components of the plasminogen (Plg)/plasmin (Pm) system are expressed in tissues of the oral cavity, such as the salivary gland, and contribute to microbial infection and inflammation, such as periodontitis. The Plg/Pm system fulfills two major functions: (a) the destruction of fibrin deposits in the bloodstream or damaged tissues, a process called fibrinolysis, and (b) non-fibrinolytic actions that include the proteolytic modulation of proteins. One can observe both functions during inflammation. The virus that causes the coronavirus disease 2019 (COVID-19) exploits the fibrinolytic and non-fibrinolytic functions of the Plg/Pm system in the oral cavity. During COVID-19, well-established coagulopathy with the development of microthrombi requires constant activation of the fibrinolytic function. Furthermore, viral entry is modulated by receptors such as TMPRSS2, which is necessary in the oral cavity, leading to a derailed immune response that peaks in cytokine storm syndrome. This paper outlines the significance of the Plg/Pm system for infectious and inflammatory diseases that start in the oral cavity. [ABSTRACT FROM AUTHOR]

Mutations allow viruses to continuously evolve by changing their genetic code to adapt to the hosts they infect. It is an adaptive and evolutionary mechanism that helps viruses acquire characteristics favoring their survival and propagation. The COVID-19 pandemic declared by the WHO in March 2020 is caused by the SARS-CoV-2 virus. The non-stop adaptive mutations of this virus and the emergence of several variants over time with characteristics favoring their spread constitute one of the biggest obstacles that researchers face in controlling this pandemic. Understanding the mutation mechanism allows for the adoption of anticipatory measures and the proposal of strategies to control its propagation. In this study, we focus on the mutations of this virus, and we propose the SARSMutOnto ontology to model SARS-CoV-2 mutations reported by Pango researchers. A detailed description is given for each mutation. The genes where the mutations occur and the genomic structure of this virus are also included. The sub-lineages and the recombinant sub-lineages resulting from these mutations are additionally represented while maintaining their hierarchy. We developed a Python-based tool to automatically generate this ontology from various published Pango source files. At the end of this paper, we provide some examples of SPARQL queries that can be used to exploit this ontology. SARSMutOnto might become a 'wet bench' machine learning tool for predicting likely future mutations based on previous mutations. [ABSTRACT FROM AUTHOR]

An outbreak of a novel form of pneumonia due to a coronavirus occurred in Wuhan City, Hubei Province, China, in December 2019, and the disease was officially named coronavirus 2019 (COVID-19). COVID-19 is an infectious disease caused by the SARS-CoV-2 virus which attacks the human respiratory system. In critical cases of COVID-19, special treatment is administered in the ICU using a mechanical ventilator. However, since the results are less than optimal, additional interventions such as orienting the patient in the prone position can be employed, as this can reduce the pressure on the dorsal lung area so that alveolar recruitment can be improved. This study examines the effect of the prone position on increasing pulmonary ventilation. This study is a literature review study employing a narrative method and was conducted by tracing and analyzing articles relevant to the topic. This literature review searched three databases, namely, PubMed, Google Scholar, and ProQuest using the keywords "COVID-19 OR intubated COVID-19" AND "prone position OR mechanical ventilator" AND "Lung ventilation". From these databases, 1062 journals were found and filtered based on inclusion and exclusion criteria, resulting in the inclusion of 10 journals. Then, they were filtered again using a critical appraisal instrument called "JBI Critical Appraisal", which was conducted by two people, N.P. and A.S.; consequently, the results of 5 journals were obtained for further analysis; cumulatively, these papers represented a total population of 1789 patients. It was concluded that the prone position can improve pulmonary ventilation by up to 50% and reduce the total length of stay by up to 5 days in the hospital, as long as the patients do not possess any comorbid diseases. [ABSTRACT FROM AUTHOR]

The exploration of the dependency structure of the Chinese and EU carbon trading markets is crucial to the construction of a globally harmonized carbon market. In this paper, we studied the characteristics of structural interdependency between China's major carbon markets and the European Union (EU) carbon market before and after the launch of the national carbon emissions trading scheme (ETS) and the occurrence of the new coronavirus (COVID-19) by applying the C-vine copula method, with the carbon trading prices of the EU, Beijing, Shanghai, Guangdong, Shenzhen and Hubei as the research objects. The study shows that there exists a statistically significant dependence between the EU and the major carbon markets in China and their extremal dependences and dependence structures are different at different stages. After the launch of the national carbon ETS, China has become more independent in terms of interdependency with the EU carbon market, and is more relevant between domestic carbon markets. Most importantly, we found that the dependence between the EU and Chinese carbon markets has increased following the outbreak of COVID-19, and tail dependency structures existed before the launch of the national carbon ETS and during the outbreak of the COVID-19. The results of this study provide a basis for the understanding of the linkage characteristics of carbon trading prices between China and the EU at different stages, which in turn can help market regulators and investors to formulate investment decisions and policies. [ABSTRACT FROM AUTHOR]

The ionic E-nanochannel (viroporin) is the weak point of SARS-CoV-2, the virus responsible for the (still threatening) COVID-19 since it is vital to the virus's budding and propagation. Therefore, targeting it to disable its functions ought to incapacitate, or at least weaken, the virus. The ionic currents inside this channel could be affected and disturbed by direct physical attack via the actions of external fields. The paper presents the first step towards the application of such methods in the fight against the current pandemic, numerical simulations of external fields' impact on ionic currents through viral channels. These simulations—based on the actual, detailed physical nanostructure of ionic channels, measured experimentally and reported in the literature—show that external physical fields can diminish the channel's currents and that the lower the channel's selectivity, the stronger the effect. Simulations suggest that SARS-CoV-2 E-viroporin is almost non-selective, which means that the whole virus ought to be highly vulnerable to the actions of external physical fields, much more vulnerable than the much more selective human cell ionic channels. If corroborated by experiment, this observation may result in an innovative method of dealing with the recent pandemic caused by SARS-CoV-2 and other similar viruses. [ABSTRACT FROM AUTHOR]

Towards the end of 2019 in Wuhan, suspicions of a new dangerous virus circulating in the air began to arise. It was the start of the world pandemic coronavirus disease 2019 (COVID-19). Since then, considerable research data and review papers about this virus have been published. Hundreds of researchers have shared their work in order to achieve a better comprehension of this disease, all with the common goal of overcoming this pandemic. The coronavirus is structurally similar to influenza A. Both are RNA viruses and normally associated with comparable infection symptoms. In this review, different case studies targeting polymeric materials were appraised to highlight them as an indispensable tool to fight these RNA viruses. In particular, the main focus was how polymeric materials, and their versatile features could be applied in different stages of viral disease, i.e., in protection, detection and treatment. [ABSTRACT FROM AUTHOR]

According to the World Health Organization updates, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a pandemic between 2019 and 2022, with millions of confirmed cases and deaths worldwide. There are various approaches to predicting the suspected, infected, and recovered (SIR) cases with different factual or epidemiological models. Some of the recent approaches to predicting the COVID-19 outbreak have had positive impacts in specific nations. Results show that the SIR model is a significant tool to cast the dynamics and predictions of the COVID-19 outbreak compared to other epidemic models. In this paper, we employ the Monte Carlo simulation to predict the spread of COVID-19 in the United Arab Emirates. We study traditional SIR models in general and focus on a time-dependent SIR model, which has been proven more adaptive and robust in predicting the COVID-19 outbreak. We evaluate the time-dependent SIR model. Then, we implement a Monte Carlo model. The Monte Carlo model uses the parameters extracted from the Time-Dependent SIR Model. The Monte Carlo model exhibited a better prediction accuracy and resembles the data collected from the Ministry of Cabinet Affairs, United Arab Emirates, between April and July 2020. [ABSTRACT FROM AUTHOR]

Since the outbreak of COVID-19 disease, a bidirectional interaction between kidney disease and the progression of COVID-19 has been demonstrated. Kidney disease is an independent risk factor for mortality of patients with COVID-19 as well as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection leading to the development of acute kidney injury (AKI) and chronic kidney disease (CKD) in patients with COVID-19. However, the detection of kidney damage in patients with COVID-19 may not occur until an advanced stage based on the current clinical blood and urinary examinations. Some studies have pointed out the development of subclinical acute kidney injury (subAKI) syndrome with COVID-19. This syndrome is characterized by significant tubule interstitial injury without changes in the estimated glomerular filtration rate. Despite the complexity of the mechanism(s) underlying the development of subAKI, the involvement of changes in the protein endocytosis machinery in proximal tubule (PT) epithelial cells (PTECs) has been proposed. This paper focuses on the data relating to subAKI and COVID-19 and the role of PTECs and their protein endocytosis machinery in its pathogenesis. [ABSTRACT FROM AUTHOR]

Currently, as an effect of the COVID-19 pandemic, bioinformatics, genomics, and biological computations are gaining increased attention. Genomes of viruses can be represented by character strings based on their nucleobases. Document similarity metrics can be applied to these strings to measure their similarities. Clustering algorithms can be applied to the results of their document similarities to cluster them. P systems or membrane systems are computation models inspired by the flow of information in the membrane cells. These can be used for various purposes, one of them being data clustering. This paper studies a novel and versatile clustering method for genomes and the utilization of such membrane clustering models using document similarity metrics, which is not yet a well-studied use of membrane clustering models. [ABSTRACT FROM AUTHOR]

The evolution of some epidemics, such as influenza, demonstrates common patterns both in different regions and from year to year. On the contrary, epidemics such as the novel COVID-19 show quite heterogeneous dynamics and are extremely susceptible to the measures taken to mitigate their spread. In this paper, we propose empirical dynamic modeling to predict the evolution of influenza in Spain's regions. It is a non-parametric method that looks into the past for coincidences with the present to make the forecasts. Here, we extend the method to predict the evolution of other epidemics at any other starting territory and we also test this procedure with Spanish COVID-19 data. We finally build influenza and COVID-19 networks to check possible coincidences in the geographical distribution of both diseases. With this, we grasp the uniqueness of the geographical dynamics of COVID-19. [ABSTRACT FROM AUTHOR]

On 31 December 2019, a cluster of pneumonia cases of unknown etiology was reported in Wuhan (China). The cases were declared to be Coronavirus Disease 2019 (COVID-19) by the World Health Organization (WHO). COVID-19 has been defined as SARS Coronavirus 2 (SARS-CoV-2). Some countries, e.g., Italy, France, and the United Kingdom (UK), have been subjected to frequent restrictions for preventing the spread of infection, contrary to other ones, e.g., the United States of America (USA) and Sweden. The restrictions afflicted the evolution of trends with several perturbations that destabilized its normal evolution. Globally, R t has been used to estimate time-varying reproduction numbers during epidemics. Methods: This paper presents a solution based on Deep Learning (DL) for the analysis and forecasting of epidemic trends in new positive cases of SARS-CoV-2 (COVID-19). It combined a neural network (NN) and an R t estimation by adjusting the data produced by the output layer of the NN on the related R t estimation. Results: Tests were performed on datasets related to the following countries: Italy, the USA, France, the UK, and Sweden. Positive case registration was retrieved between 24 February 2020 and 11 January 2022. Tests performed on the Italian dataset showed that our solution reduced the Mean Absolute Percentage Error (MAPE) by 28.44%, 39.36%, 22.96%, 17.93%, 28.10%, and 24.50% compared to other ones with the same configuration but that were based on the LSTM, GRU, RNN, ARIMA (1,0,3), and ARIMA (7,2,4) models, or an NN without applying the R t as a corrective index. It also reduced MAPE by 17.93%, the Mean Absolute Error (MAE) by 34.37%, and the Root Mean Square Error (RMSE) by 43.76% compared to the same model without the adjustment performed by the R t . Furthermore, it allowed an average MAPE reduction of 5.37%, 63.10%, 17.84%, and 14.91% on the datasets related to the USA, France, the UK, and Sweden, respectively. [ABSTRACT FROM AUTHOR]

From the beginning of the COVID-19 pandemic, researchers assessed the impact of the disease in terms of loss of life, medical load, economic damage, and other key metrics of resiliency and consequence mitigation; these studies sought to parametrize the critical components of a disease transmission model and the resulting analyses were informative but often lacked critical parameters or a discussion of parameter sensitivities. Using SARS-CoV-2 as a case study, we present a robust modeling framework that considers disease transmissibility from the source through transport and dispersion and infectivity. The framework is designed to work across a range of particle sizes and estimate the generation rate, environmental fate, deposited dose, and infection, allowing for end-to-end analysis that can be transitioned to individual and population health models. In this paper, we perform sensitivity analysis on the model framework to demonstrate how it can be used to advance and prioritize research efforts by highlighting critical parameters for further analyses. [ABSTRACT FROM AUTHOR]

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection has become a worldwide threat resulting in a pandemic in 2020. SARS-CoV-2 infection manifests itself as coronavirus disease 2019 (COVID-19) that is evidenced in a vast number of either specific or nonspecific symptoms. Except for typical (but nonspecific) symptoms such as fever, dry cough, or muscle weakness, the infected patients might also present atypical symptoms including neurological, dermatological, or ophthalmic manifestations. This paper summarizes the current state of knowledge regarding the onset, progression, and types of ophthalmic symptoms induced by SARS-CoV-2 infection recognized amongst the infected patients. [ABSTRACT FROM AUTHOR]

As of 27 December 2021, SARS-CoV-2 has infected over 278 million persons and caused 5.3 million deaths. Since the outbreak of COVID-19, different methods, from medical to artificial intelligence, have been used for its detection, diagnosis, and surveillance. Meanwhile, fast and efficient point-of-care (POC) testing and self-testing kits have become necessary in the fight against COVID-19 and to assist healthcare personnel and governments curb the spread of the virus. This paper presents a review of the various types of COVID-19 detection methods, diagnostic technologies, and surveillance approaches that have been used or proposed. The review provided in this article should be beneficial to researchers in this field and health policymakers at large. [ABSTRACT FROM AUTHOR]

A pneumonia outbreak was primarily reported in the fall of 2019 in Wuhan, Hubei province, China, with the identity SARS-CoV-2, a novel coronavirus. It quickly grew from a local epidemic to a global pandemic and was declared a public health emergency by the WHO. A total of three prominent waves were identified across the globe, with a slight temporal variability as per the geographical locations, and has impacted several sectors which connect the world. By March 2022, the coronavirus had infected 444.12 million people and claimed 6.01 million human lives worldwide, and these numbers have not yet stabilized. Our paper enlightens readers on the seven strains of human coronaviruses, with special emphasis on the three severe deadliest outbreaks (SARS-2002, MERS-2012, and COVID-19). This work attempts a comprehensive understanding of the coronavirus and its impact on the possible sectors that link the world through the economic chain, climate conditions, SDGs, recycling of the event, and mitigations. There are many points that are raised by the authors in the possible sectors, which are emerging or are as yet unnoticed and thus have not been taken into consideration. This comprehension will leave sets of new challenges and opportunities for the researchers in various streams, especially in earth sciences. Science-integrated research may help to prevent upcoming disasters as a by-product of (existing) epidemics in the form of coronavirus. [ABSTRACT FROM AUTHOR]

Ongoing social restrictions, including social distancing and lockdown, adopted by many countries to inhibit spread of the the COVID-19 epidemic, must attempt to find a trade-off between induced economic damage, healthcare system collapse, and the costs in terms of human lives. Applying and removing restrictions on a system with a given latency as represented by an epidemic outbreak (and formally comparable with mechanical inertia), may create critical instabilities, overshoots, and strong oscillations in the number of infected people around the desirable set-point, defined in a practical way as the maximum number of hospitalizations acceptable by a given healthcare system. A good understanding of the system reaction to any change of the input control variable can be reasonably achieved using a proportional-integral-derivative controller (PID), which is a widely used technique in various physics and technological applications. In this paper, this control theory to is proposed to be applied epidemiology, to understand the reaction of COVID-19 propagation to social restrictions and to reduce epidemic damages through the correct tuning of the containment policy. Regarding the synthesis of this interdisciplinary approach, the extended to the susceptible-infectious-recovered (SIR) model name "SIR-PID" is suggested. [ABSTRACT FROM AUTHOR]

The use of artificial neural networks (ANNs) is a great contribution to medical studies since the application of forecasting concepts allows for the analysis of future diseases propagation. In this context, this paper presents a study of the new coronavirus SARS-COV-2 with a focus on verifying the virus propagation associated with mitigation procedures and massive vaccination campaigns. There were two proposed methodologies in making predictions 28 days ahead for the number of new cases, deaths, and ICU patients of five European countries: Portugal, France, Italy, the United Kingdom, and Germany. A case study of the results of massive immunization in Israel was also considered. The data input of cases, deaths, and daily ICU patients was normalized to reduce discrepant numbers due to the countries’ size and the cumulative vaccination values by the percentage of population immunized (with at least one dose of the vaccine). As a comparative criterion, the calculation of the mean absolute error (MAE) of all predictions presents the best methodology, targeting other possibilities of use for the method proposed. The best architecture achieved a general MAE for the 1-to-28-day ahead forecast, which is lower than 30 cases, 0.6 deaths, and 2.5 ICU patients per million people. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), a member of the genus Betacoronavirus in the family Coronaviridae, possesses an unusually large single-stranded viral RNA (ssvRNA) genome of about ~29,811 nucleotides (nt) that causes severe and acute respiratory distress and a highly lethal viral pneumonia known as COVID-19. COVID-19 also presents with multiple ancillary systemic diseases and often involves cardiovascular, inflammatory, and/or neurological complications. Pathological viral genomes consisting of ssvRNA, like cellular messenger RNA (mRNA), are susceptible to attack, destruction, neutralization, and/or modulation by naturally occurring small non-coding RNAs (sncRNAs) within the host cell, some of which are known as microRNAs (miRNAs). This paper proposes that the actions of the 2650 known human miRNAs and other sncRNAs form the basis for an under-recognized and unappreciated innate-immune regulator of ssvRNA viral genome activities and have implications for the efficiency of SARS-CoV2 invasion, infection, and replication. Recent research indicates that both miRNA and mRNA abundance, speciation, and complexity varies widely amongst human individuals, and this may: (i) In part explain the variability in the innate-immune immunological and pathophysiological response of different human individuals to the initiation and progression of SARS-CoV-2 infection in multiple tissue types; and (ii) further support our understanding of human biochemical and genetic individuality and the variable resistance of individuals to ssvRNA-mediated viral infection and disease. This commentary will briefly address current findings and concepts in this fascinating research area of non-coding RNA and innate-immunity with special reference to natural host miRNAs, SARS-CoV-2, and the current COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

In comparison with other respiratory viruses, the current COVID-19 pandemic's rapid seizing the world can be attributed to indirect (contact) way of transmission of SARS-CoV-2 virus in addition to the regular airborne way. A significant part of indirect transmission is made through cash bank notes. SARS-CoV-2 remains on cash paper money for period around four times larger than influenza A virus and is absorbed by cash notes two and a half times more effectively than influenza A (our model). During the pandemic, cryptocurrencies have gained attractiveness as an "epidemiologically safe" means of transactions. On the basis of the authors' gallop polls performed online with social networks users in 44 countries in 2020–2021 (the total number of clear responses after the set repair 32,115), around 14.7% of surveyed participants engaged in cryptocurrency-based transactions during the pandemic. This may be one of the reasons of significant rise of cryptocurrencies rates since mid-March 2020 till the end of 2021. The paper discusses the reasons for cryptocurrency attractiveness during the COVID-19 pandemic. Among them, there are fear of SARS-CoV-2 spread via cash contacts and the ability of the general population to mine cryptocurrencies. The article also provides a breakdown of the polled audience profile to determine the nationalities that have maximal level of trust to saving and transacting money as cryptocurrencies. [ABSTRACT FROM AUTHOR]

Since December 2019, the novel coronavirus (SARS-CoV-2) and its associated illness COVID-19 have rapidly spread worldwide. The Mexican government has implemented public safety measures to minimize the spread of the virus. In this paper, we used statistical models in two stages to estimate the total number of coronavirus (COVID-19) cases per day at the state and national levels in Mexico. In this paper, we propose two types of models. First, a polynomial model of the growth for the first part of the outbreak until the inflection point of the pandemic curve and then a second nonlinear growth model used to estimate the middle and the end of the outbreak. Model selection was performed using Vuong's test. The proposed models showed overall fit similar to predictive models (e.g., time series and machine learning); however, the interpretation of parameters is simpler for decisionmakers, and the residuals follow the expected distribution when fitting the models without autocorrelation being an issue. [ABSTRACT FROM AUTHOR]

The paper describes problems with the transplantation process during the COVID-19 pandemic. Transplantation procedures and programs have been impacted by COVID-19. The number of transplants has fallen noticeably. The first part of the paper points out changes in service organization, in particular donor and recipient pre-transplant and peri-transplant management. If the patients during pre-transplant evaluation need to attend face-to-face appointments, such as blood testing or other investigations, the risk of contracting or spreading COVID-19 should be minimized. "Clear green areas", which are COVID-19-free pathways, are highly recommended in hospitals during transplant procedures. Diagnostic procedures concerning donors, including CT scans and coronavirus testing (nasopharyngeal swab), are necessary before transplant surgery. COVID-19 symptoms and risks of the transplant population are described. Detailed guidelines from transplant societies concerning changes in immunosuppression in infected recipients are discussed. Management of infected or suspected medical staff is mentioned. The paper ends with guidelines concerning vaccination against COVID-19 in transplant recipients. [ABSTRACT FROM AUTHOR]

Simple Summary: The accelerated pace of research into Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) necessitates periodic summaries of current research. The present paper reviews virus susceptibilities in species commonly in contact with humans and predictors of susceptibility. With few exceptions, species selected for review were those in contact with humans through the entertainment, pet, or agricultural trades, and for whom report—either anecdotal or published—exist regarding the SARS-CoV-2 virus and/or the resulting disease state COVID-19. The possibility of humans transmitting SARS-CoV-2 to animals and fear of animals transmitting the virus to humans endangers animal wellbeing; use of animals as research models also has welfare implications. As the search for appropriate animal models for SARS-CoV-2 continues, it is important to determine which species are most appropriate, so that the "three R's" of animal research (replacement, reduction, and refinement) may be put into practice. The present review suggests that ferrets, golden Syrian hamsters, and some Old World nonhuman primates may be the best animal models for COVID-19-related research, as these species display the greatest similarity in factors underlying viral infection, as well as clinical symptoms upon viral exposure most similar to those seen in humans. The accelerated pace of research into Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) necessitates periodic summaries of current research. The present paper reviews virus susceptibilities in species with frequent human contact, and factors that are best predictors of virus susceptibility. Species reviewed were those in contact with humans through entertainment, pet, or agricultural trades, and for whom reports (either anecdotal or published) exist regarding the SARS-CoV-2 virus and/or the resulting disease state COVID-19. Available literature was searched using an artificial intelligence (AI)-assisted engine, as well as via common databases, such as Web of Science and Medline. The present review focuses on susceptibility and transmissibility of SARS-CoV-2, and polymorphisms in transmembrane protease serine 2 (TMPRSS2) and angiotensin-converting enzyme 2 (ACE2) that contribute to species differences. Dogs and pigs appear to have low susceptibility, while ferrets, mink, some hamster species, cats, and nonhuman primates (particularly Old World species) have high susceptibility. Precautions may therefore be warranted in interactions with such species, and more selectivity practiced when choosing appropriate species to serve as models for research. [ABSTRACT FROM AUTHOR]

At the beginning of 2020, a suddenly appearing novel coronavirus (COVID-19) rapidly spread around the world. The outbreak of the COVID-19 pandemic in China occurred during the Spring Festival when a large number of migrants traveled between cities, which greatly increased the infection risk of COVID-19 across the country. Financially supported by the Wuhan government, and based on cellphone signaling data from Unicom (a mobile phone carrier) and Baidu location-based data, this paper analyzed the effects that city dwellers, non-commuters, commuters, and people seeking medical services had on the transmission risk of COVID-19 in the early days of the pandemic in Wuhan. The paper also evaluated the effects of the city lockdown policy on the spread of the pandemic outside and inside Wuhan. The results show that although the daily business activities in the South China Seafood Wholesale Market and nearby commuters' travel behaviors concentrated in the Hankou area, a certain proportion of these people were distributed in the Wuchang and Hanyang areas. The areas with relatively high infection risks of COVID-19 were scattered across Wuhan during the early outbreak of the pandemic. The lockdown in Wuhan closed the passageways of external transport at the very beginning, largely decreasing migrant population and effectively preventing the spread of the pandemic to the outside. However, the Wuhan lockdown had little effect on preventing the spread of the pandemic within Wuhan at that time. During this period, a large amount of patients who went to hospitals for medical services were exposed to a high risk of cross-infection without precaution awareness. The pandemic kept dispersing in three towns until the improvement of the capacity of medical treatment, the management of closed communities, the national support to Wuhan, and the implementation of a series of emergency responses at the same time. The findings in this paper reveal the spatiotemporal features of the dispersal of infection risk of COVID-19 and the effects of the prevention and control measures during the early days of the pandemic. The findings were adopted by the Wuhan government to make corresponding policies and could also provide supports to the control of the pandemic in the other regions and countries. [ABSTRACT FROM AUTHOR]

The COVID-19 epidemic has caused a large number of human losses and havoc in the economic, social, societal, and health systems around the world. Controlling such epidemic requires understanding its characteristics and behavior, which can be identified by collecting and analyzing the related big data. Big data analytics tools play a vital role in building knowledge required in making decisions and precautionary measures. However, due to the vast amount of data available on COVID-19 from various sources, there is a need to review the roles of big data analysis in controlling the spread of COVID-19, presenting the main challenges and directions of COVID-19 data analysis, as well as providing a framework on the related existing applications and studies to facilitate future research on COVID-19 analysis. Therefore, in this paper, we conduct a literature review to highlight the contributions of several studies in the domain of COVID-19-based big data analysis. The study presents as a taxonomy several applications used to manage and control the pandemic. Moreover, this study discusses several challenges encountered when analyzing COVID-19 data. The findings of this paper suggest valuable future directions to be considered for further research and applications. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic represents not just a global health crisis, but may signal the beginning of a new era of economic activity, the potential consequences of which we currently do not fully understand. In this context, the mid-to-long-range impacts of the pandemic on the energy sector have been studied extensively in the last few months. Despite these efforts, the pandemic still raises many open questions concerning the long-term operation and planning of power systems. For instance, how will the pandemic affect the integration of renewable energy sources? Should current power system expansion plans change in light of the COVID-19 pandemic? What new tools should be provided to support system operators during global health crises? It is the purpose of this paper to better understand the many aspects of these open questions by reviewing the relevant recent literature and by analyzing measured data. We point out the main challenges that the pandemic introduced by presenting patterns of electricity generation and demand, frequency deviations, and load forecasting. Moreover, we suggest directions for future research that may assist in coping with the mentioned challenges. We hope that this paper will trigger fruitful discussions and encourage further research on these important emerging topics. [ABSTRACT FROM AUTHOR]

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and these models are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models need to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to susceptible–infected–recovered (SIR) and susceptible-exposed-infectious-removed (SEIR) models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP; and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior across nations, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. This paper further suggests that a genuine novelty in outbreak prediction can be realized by integrating machine learning and SEIR models. [ABSTRACT FROM AUTHOR]

Post-SARS-CoV-2 telogen effluvium has been described in case reports of COVID-19 patients. We evaluated the prevalence of post-SARS-CoV-2 telogen effluvium in patients from a single medical center, exploring any causal links with the infection. Our hospital-based, cross-sectional study was conducted with patient participants discharged with a diagnosis of SARS-CoV-2 pneumonia from 1 March to 4 April 2020. All patients were evaluated by the same senior dermatologist; a clinical/dermatoscopic evaluation was performed. Alopecia was assessed in 31.3% of patients, with a significant difference in sex (females 73%, males 26.7%). The average time detected from the onset of the first symptoms to alopecia was 68.43 days. Overall, there were no significant associations between alopecia and COVID-19-related features (length of hospitalization, virologic positivity, or duration of fever), treatment characteristics, or laboratory findings. In this paper, we report that post-infection acute telogen effluvium occurs in a significant number of COVID-19 patients. The burden of this condition may impair the quality of life, with a significant impact on individuals. [ABSTRACT FROM AUTHOR]