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Infectious diseases are among the leading causes of mortality worldwide. A new coronavirus named severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) was identified in Wuhan, China in 2019, and the World Health Organization (WHO) declared its outbreak, coronavirus disease 2019 (COVID-19), as a global pandemic in 2020. COVID-19 can spread quickly from person to person. One of the most challenging issues is to identify the infected individuals and prevent potential spread of SARS-CoV-2. Recently, anti-SARS-CoV-2 immunoglobulin M (IgM) and immunoglobulin G (IgG) antibody tests using immunochromatographic methods have been used as a complement to current detection methods and have provided information of the approximate course of COVID-19 infection. However, blood sampling causes pain and poses risks of infection at the needle puncture site. In this study, a novel patch sensor integrating porous microneedles and an immunochromatographic assay (PMNIA) was developed for the rapid detection of anti-SARS-CoV-2 IgM/IgG in dermal interstitial fluid (ISF), which is a rich source of protein biomarkers, such as antibodies. Biodegradable porous microneedles (MNs) made of polylactic acid were fabricated to extract ISF from human skin by capillary effect. The extracted ISF was vertically transported and flowed into the affixed immunoassay biosensor, where specific antibodies could be detected colorimetrically on-site. Anti-SARS-CoV-2 IgM/IgG antibodies were simultaneously detected within 3 min in vitro. Moreover, the limit of detection of anti-SARS-CoV-2 IgM and IgG concentrations was as low as 3 and 7 ng/mL, respectively. The developed device integrating porous MNs and immunochromatographic biosensors is expected to enable minimally invasive, simple, and rapid anti-SARS-CoV-2 IgM/IgG antibody testing. Furthermore, the compact size of the MN and biosensor-integrated device is advantageous for its widespread use. The proposed device has great potential for rapid screening of various infectious diseases in addition to COVID-19 as an effective complementary method with other diagnostic tests. [ABSTRACT FROM AUTHOR]

The 2019 outbreak of coronavirus disease (COVID‐19) in Wuhan (Hubei province of China) has given rise to a pandemic spread of virus, more than 240 million incidences and a death toll larger than 5 million people. COVID‐19 has set off large efforts in research, therapy and patient care, as well as public and private debates in every imaginable form. A number of scientists used the publication platforms provided by the Federation of the European Biochemical Societies (FEBS) to present their research data, reviews, opinions and other contributions relating to COVID‐19 and severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Here, I highlight the recent COVID‐19 papers which have been published and collected in a Virtual Issue in FEBS Letters, and discuss their implications towards understanding the molecular, biochemical and cellular mechanisms of SARS‐CoV‐2 infections, vaccine development and antiviral discovery strategies. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic originating in the Wuhan province of China in late 2019 has impacted global health, causing increased mortality among elderly patients and individuals with comorbid conditions. During the passage of the virus through affected populations, it has undergone mutations, some of which have recently been linked with increased viral load and prognostic complexities. Several of these variants are point mutations that are difficult to diagnose using the gold standard quantitative real-time PCR (qRT-PCR) method and necessitates widespread sequencing which is expensive, has long turn-around times, and requires high viral load for calling mutations accurately. Here, we repurpose the high specificity of Francisella novicida Cas9 (FnCas9) to identify mismatches in the target for developing a lateral flow assay that can be successfully adapted for the simultaneous detection of SARS-CoV-2 infection as well as for detecting point mutations in the sequence of the virus obtained from patient samples. We report the detection of the S gene mutation N501Y (present across multiple variant lineages of SARS-CoV-2) within an hour using lateral flow paper strip chemistry. The results were corroborated using deep sequencing on multiple wild-type (n = 37) and mutant (n = 22) virus infected patient samples with a sensitivity of 87% and specificity of 97%. The design principle can be rapidly adapted for other mutations (as shown also for E484K and T716I) highlighting the advantages of quick optimization and roll-out of CRISPR diagnostics (CRISPRDx) for disease surveillance even beyond COVID-19. This study was funded by Council for Scientific and Industrial Research, India. [ABSTRACT FROM AUTHOR]

In this review paper efforts are made to present the Impact of COVID-19 on various economic activities like production, consumption and distribution. This is known to everyone that COVID-19 (Corona Virus Disease) spreadout in December 2019 from Wuhan in China and effected a lot of countries resulting in death of many. Therefore, to minimise the risk, many countries opted lockdown as a precaution to control its spread which effected the lives of ordinary people as well as the economy as a whole, GDP, Functions of Economy and a lot of activities were effected very badly. It had a huge impact on the consumption pattern of population as well as supply chains and production of various goods and services were also impacted as transportation was at halt and movement of people, products, services etc. was not possible from one place to another. There was a heavy down in employment, production, distribution and other activities as well. Hence, this paper will help the readers know the collective impact along with the sector wise effect of COVID-19 on various economic activities. For better understanding we have taken three major economic activities that is Production, Consumption and distribution. Along with these the basic introduction of COVID-19, lockdown and its impact on economy is stated. [ABSTRACT FROM AUTHOR]

Background: In December of 2019, a new disease which is caused by SARS-CoV-2, as an epidemic disease out of Wuhan, China, began to circulate. On March 11, 2020, the Republic of Turkey Ministry of Health had announced the first case from Turkey. The aim of this study is to analyze the scientific publications in the field of COVID-19 included in Science Citation Index Expanded (SCIE) from Turkey and to establish a theoretical background for future studies in the health literature with obtained valuable information about the publications. We searched all papers published in the field of COVID-19 by using the terms of "COVID-19," "2019-n-CoV," "SARS-CoV-2," "Coronavirus disease 19," and "2019 novel coronavirus" as scientific nomenclatures of COVID-19 in the topic search section of the software. Results: Overall, 47,368 papers, indexed by SCI-E, were found related to COVID-19 between January 1, 2020, and December 13, 2020. Of these, 931 were from Turkey. In terms of specialities, the most contribution was from the Medicine General Internal followed by Dermatology. Most of the publications were article. English was the most preferred language in papers. Dermatological Theraphy published the most paper. Conclusions: Applying this kind of analysis on an intermittent basis gives a general perspective for contribution of a countries to scientific publications and useful for the further studies. [ABSTRACT FROM AUTHOR]

Since 2019n-CoV has swept the whole world, people's daily life is affected seriously and the public space system is facing to major challenges. Community public space should be re-evaluated and optimized as people change the way they use it. This research conducted a nationwide questionnaire survey in China, which collected the opinions of residents on community public space from the perspective of epidemic prevention and social needs to select research indicators. The questionnaire data were processed through the frequency analysis method, precedence chart and TOPSIS. We found: (1) under the background of the epidemic, people pay more attention to the satisfaction of leisure activities in community public space and the control of social spacing during activities; (2) the current assessment result of community public space risk of 2019-nCoV exposure is 0.386, which is relatively high; and (3) the exposure risk of the community can be effectively reduced by controlling for the two indicators of social distance and social facility sterilization. When the social distance is 1.8–3 m and the facility sterilization is once a day, the community public space minimizes exposure risk. In view of the above results, this paper selects the Wuhan start-up area of Wuhan, China, as an example for carrying out the optimization design of community public space based on the epidemic and the design the space optimization model for the two indicators of social distance and facility disinfection from both software and hardware aspects. [ABSTRACT FROM AUTHOR]

Background: Since the COVID-19 pandemic outbreak, diabetes mellitus (DM) has been at the core of the confirmed risk factors for fatal or critical care unittreated COVID-19 and COVID-19 related complications. Although relevant studies on DM have developed rapidly during the COVID-19 pandemic. However, the aforementioned research results have not been systematically quantified by means of bibliometric analysis. Purpose: The purpose of this study is to provide a comprehensive analysis of the current status and trends of publications related to DM research during the COVID19 epidemic. Methods: A bibliometric analysis was performed using the Web of Science database. In this study, we used citespace, R software and R-Bibliometrix to analyze keywords, most-cited authors, most-cited countries, most-cited global documents, and co-occurrence and co-citation networks. Results: A total of 1688 publications was included in this study. Investigators from the United States contributed the most publications. The United States, China and Europe have the most collaboration with the other countries/regions. A total of 3355 institutions made contributions to this study. Of the top 10 institutions with the most publications, N8 Research Partnership showed the most centrality. Among the top 10 journals, Diabetes Research and Clinical Practice published the most articles. Among authors included, Khunti Kamlesh is rated first with 27 papers and has the highest centrality. The most frequently co-cited article is entitled "Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study". The most popular keywords included diabetes, mortality, diabetes, outcome, occurrences, risk, and type 1 diabetes. Conclusion: This bibliometric study provides an overall picture of DM research and research trends during the COVID-19 pandemic and provides a basis for researchers to develop their next research strategies. [ABSTRACT FROM AUTHOR]

Background: In early infected or severe coronavirus disease 2019 (COVID-19) patients, circulating NK cells are consistently reduced, despite being highly activated or exhausted. The aim of this paper was to establish whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein (SP) may directly trigger NK cells and through which receptor(s). Methods: SP-stimulated human NK cells have been evaluated for the expression of activation markers, cytokine release, and cytotoxic activity, as well as for gene expression profiles and NF-κB phosphorylation, and they have been silenced with specific small interfering RNAs. Results: SPs from the Wuhan strain and other variants of concern (VOCs) directly bind and stimulate purified NK cells by increasing activation marker expression, cytokine release, and cytolytic activity, prevalently in the CD56brightNK cell subset. VOC-SPs differ in their ability to activate NK cells, G614, and Delta-Plus strains providing the strongest activity in the majority of donors. While VOC-SPs do not trigger ACE2, which is not expressed on NK cells, or other activating receptors, they directly and variably bind to both Toll-like receptor 2 (TLR2) and TLR4. Moreover, SP-driven NK cell functions are inhibited upon masking such receptors or silencing the relative genes. Lastly, VOC-SPs upregulate CD56dimNK cell functions in COVID-19 recovered, but not in non-infected, individuals. Conclusions: TLR2 and TLR4 are novel activating receptors for SP in NK cells, suggesting a new role of these cells in orchestrating the pathophysiology of SARS-CoV-2 infection. The pathogenic relevance of this finding is highlighted by the fact that free SP providing NK cell activation is frequently detected in a SARS-CoV-2 inflamed environment and in plasma of infected and long-COVID-19 subjects. [ABSTRACT FROM AUTHOR]

The rise of e-commerce, bolstered by advances in information and communication technology (ICT), has made it possible for consumers to shop online without the need to physically travel. The unexpected emergence of COVID-19 further accelerated this shift towards online shopping. This paper compares virtual versus physical access to goods, drawing from dual access theory. It aims to offer a comprehensive understanding of the disparities in accessibility between digital and brick-and-mortar shopping experiences. Our results indicate that, when considering the complete private costs — including the intrinsic costs of shopping and those incurred en-route like travel and delivery fees — online shopping typically offers greater accessibility and is more cost-effective than its in-store counterpart. While physical access to shopping displays a pattern where the central city has a distinct advantage over the suburbs, virtual access presents a more uniform distribution throughout the city. [ABSTRACT FROM AUTHOR]

First described in December 2019 in Wuhan (China), COVID-19 disease rapidly spread worldwide, constituting the biggest pandemic in the last 100 years. Even if SARS-CoV-2, the agent responsible for COVID-19, is mainly associated with pulmonary injury, evidence is growing that this virus can affect many organs, including the heart and vascular endothelial cells, and cause haemostasis, CNS, and kidney and gastrointestinal tract abnormalities that can impact in the disease course and prognosis. In fact, COVID-19 may affect almost all the organs. Hence, SARS-CoV-2 is essentially a systemic infection that can present a large number of clinical manifestations, and it is variable in distribution and severity, which means it is potentially life-threatening. The goal of this comprehensive review paper in the series is to give an overview of non-pulmonary involvement in COVID-19, with a special focus on underlying pathophysiological mechanisms and clinical presentation. [ABSTRACT FROM AUTHOR]

Since the outbreak of the global COVID-19 pandemic in Wuhan, China, in 2019, its impact has been seen worldwide. Early identification of COVID-19 is very crucial, as it keeps the infected people isolated from other people, thus minimizing the risk of further transmission. The standard diagnostic approach is based on RT-PCR. However, due to the scarcity of PCR kits in some regions and the costs associated with this technique, there is a growing demand for alternative solutions. Recently, diagnosis of COVID-19 by medical imaging has been recognized as a valid clinical practice. Meanwhile, the massive increase in COVID-19 cases has put considerable pressure on radiologists responsible for interpreting these scans. This paper introduces an automated detection approach as a rapid alternative for COVID-19 diagnosis. We present a deep CNN model to differentiate between normal and pneumonia cases, as well as patients with COVID-19. Our approach is based on EfficientNet-B7 architecture and improved with Squeeze and Excitation block as an attention mechanism. In addition, we propose an innovative architecture that combines CNN with SVM to achieve the best performance. Experimental results show that the proposed framework provides better performance than existing SOTA methods, with an average accuracy of 97.50%, while the precision and recall of COVID-19 are both 100%. [ABSTRACT FROM AUTHOR]

BACKGROUND: Coronaviruses (CoVs) belong to the RNA viruses family. The viruses in this family are known to cause mild respiratory disease in humans. The origin of the novel SARSCOV2 virus that caused the coronavirus-19 disease (COVID-19) is the Wuhan city in China from where it disseminated to cause a global pandemic. Although lungs are the predominant target organ for Coronavirus Disease-19 (COVID-19), since its outbreak, the disease is known to affect heart, blood vessels, kidney, intestine, liver and brain. This review aimed to summarize the catastrophic impacts of Coronavirus disease-19 on heart and liver along with its mechanisms of pathogenesis. METHODS: The information used in this review was obtained from relevant articles published on PubMed, Google Scholar, Google, WHO website, CDC and other sources. Key searching statements and phrases related to COVID-19 were used to retrieve information. Original research articles, review papers, research letters and case reports were used as a source of information. RESULTS: Besides causing severe lung injury, COVID-19 has also been reported to affect and cause dysfunction of many other organs. COVID-19 infection can affect people by downregulating membrane-bound active angiotensin-converting enzyme (ACE). People who have deficient ACE2 expression are more vulnerable to COVID-19 infection. The patients' pre-existing co-morbidities are major risk factors that predispose individuals to severe COVID-19. CONCLUSION: The disease severity and its broad spectrum phenotype is a result of combined direct and indirect pathogenic factors. Therefore, protocols that harmonize many therapeutic preferences should be the best alternatives to de-escalate the disease and obviate deaths caused as a result of multiple organ damage and dysfunction induced by the disease. [ABSTRACT FROM AUTHOR]

The search-engine-based surveillance methods for the early warning and prediction of infectious diseases cannot achieve search engine keywords automatic filtering and real-time updating, lead to powerless for the early warning of emerging infectious diseases. The aim of this study is to develop an artificial intelligence (AI) method for search-engine-based surveillance to improve the early warning ability for emerging infectious diseases. The 32 keywords (444 million search queries) that may be related to the coronavirus disease (COVID-19) outbreak was collected from December 18, 2019 to February 11, 2020 from Baidu's search engine database. The graph convolution network (GCN) model was used to select search engine keywords automatically, and then, multiple linear regression was performed to explore the relationship between the daily query frequencies of keywords and daily new cases. The GCN model was used to automatically select keywords. The prediction trend of the GCN model was highly consistent with the true curve with a mean absolute error of 81.65. Three keywords including "epidemic", "mask" and "coronavirus" were selected. The selection keywords in the search queries were highly correlated with the daily number of confirmed cases (r = 0.96, 0.94, and 0.89; P < 0.01). An abnormal initial peak (3.05 times the normal volume) in queries appeared on December 31, 2019, which could have served as an early warning signal for an outbreak. Of particular concern, 17.5% of query volume originated from the Hubei Province, 51.15% of which was from Wuhan City. The coefficients of determination (R2) of our constructed model were 0.88, 0.88, 0.84, 0.77, 0.77, 0.75, 0.73, and 0.73 for a time lag of 0–7 days, respectively, using selection keywords. The model we constructed was used in the Beijing Xinfadi outbreak as an independent test dataset, which successfully predicted the daily numbers of cases for the following days and detected an early signal during the Beijing Xinfadi outbreak (R2 = 0.79). In this paper search-engine-based surveillance based on the AI method was established for the early detection of the COVID-19 epidemic for the first time. The model achieves automatic filtering and real-time updating of search engine keywords and can effectively detect the early signals of emerging infectious diseases. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 in 2020 caused extensive impact on household income and foods consumption. However, little attention has been paid to the immediate impact of income loss on animal-source foods consumption in the early stage of the COVID-19 pandemic. This paper aims to narrow this gap, and a total of 1301 valid samples of household food consumption surveys in Wuhan and Nanjing were obtained through specially designed online questionnaires. The surveys show that there were 69.6% (Wuhan) and 42.2% (Nanjing) of surveyed households whose animal-source foods consumption were affected, and there were 47.4% (Wuhan) and 18.9% (Nanjing) of surveyed households who suffered income loss. Furthermore, this paper makes an empirical study on the linkage between income loss and animal-source foods consumption. The results show that the pandemic affected household income, resulting in an immediate impact on animal-source foods consumption. This immediate impact may have been due to the combination of price increases, income loss and insufficient savings, which led to a "perfect storm" for animal-source foods consumption. Moreover, household income loss affected various animal-source foods consumption differently. For households suffering income losses, the odds of pork, beef and mutton, poultry, aquatic products, eggs and dairy products consumption being affected were increased by a factor of 1.894, 2.140, 2.773, 2.345, 1.802, 2.835, respectively, holding other variables constant. The results may be related to residents' consumption habits and food prices. During the COVID-19 pandemic, the reduction of animal-source foods consumption may have led to a state of tension concerning an increase in the development of nutrition intake and health, which may have led to increased food security risks. [ABSTRACT FROM AUTHOR]

The recently discovered coronavirus, SARS-CoV-2, which was detected in Wuhan, China, has spread worldwide and is still being studied at the end of 2019. Detection of COVID-19 at an early stage is essential to provide adequate healthcare to affected patients and protect the uninfected community. This paper aims to design and develop a novel ensemble-based classifier to predict COVID-19 cases at a very early stage so that appropriate action can be taken by patients, doctors, health organizations, and the government. In this paper, a synthetic dataset of COVID-19 is generated by a dataset generation algorithm. A novel ensemble-based classifier of machine learning is employed on the COVID-19 dataset to predict the disease. A convex hull-based approach is also applied to the data to improve the proposed novel, ensemble-based classifier's accuracy and speed. The model is designed and developed through the python programming language and compares with the most popular classifier, i.e., Decision Tree, ID3, and support vector machine. The results indicate that the proposed novel classifier provides a more significant precision, kappa static, root means a square error, recall, F-measure, and accuracy. [ABSTRACT FROM AUTHOR]

A new mathematical model of Coronavirus (2019-nCov) using piecewise hybrid fractional order derivatives is given in this paper. Moreover, in order to be consistent with the physical model problem, a new parameter μ is presented. The boundedness, existence, and positivity of the solutions for the proposed model are discussed. Two improved numerical methods are presented in this paper. The Caputo proportional constant nonstandard modified Euler–Maruyama method is introduced to study the fractional stochastic model, and the Grünwald–Letnikov nonstandard finite difference method is presented to study the hybrid fractional order deterministic model. Comparative studies with real data from Spain and Wuhan are presented. [ABSTRACT FROM AUTHOR]

Corona disease outbreak occurred in 2019 from Wuhan, China and the virus was identified to be a member of the Coronaviridae family. the disease was named as COVID-19 by WHO. It started as an epidemic and later turned into pandemic affecting world wide. the present study was aimed to conduct a systematic review on most prevalent symptoms associated with COVID-19 and to perform a Meta analysis on Comorbidities associated with COVID-19 and assess its severity in underlying conditions. A literature search was conducted using search engines like Pubmed, Web of Science, elsevier COVID-19 and litCovid databases between January 2020 and August 2020. Forest plot analysis was done using Random effects model to estimate odds ratio and 95% confidence interval estimate. 83 papers with a sample size of 19,454 were analysed statistically after adopting inclusion and exclusion criteria. Our results showed the most prevalent symptoms to be Cough, fever, dyspnoea and myalgia in interval plot analysis. When assessed for comorbidities, Hypertension (27.02%), Diabetes (16.61%), Cardiovascular disease (12.75%) and Chronic pulmonary obstructive diseases(19.9%) are found to be the most risk factors in comparison with others. However a finer experimental research needs to be confirmed with the findings. [ABSTRACT FROM AUTHOR]

Introduction: In December 2019, a cluster of atypical cases of pneumonia was reported in Wuhan, China, which was later designated as Coronavirus disease 2019 (COVID-19) by the World Health Organization (WHO) on Feb 11, 2020. We all are facing a global pandemic, and it is very important to be clear that there is no correct roadmap to navigate this difficult situation. It is imperative to state that this global pandemic impacted the spine care services of our institute. In the present study, we have assessed the spine surgeries performed by orthopedic surgeons in terms of volume and etiologies during the COVID-19 pandemic and compared the data with a pre-COVID period. Materials and Methods: We retrospectively collected data from all patients who underwent spinal surgeries at our institute under the department of orthopedics from August 20, 2019 to August 20, 2020 (a total of 12 months duration). The data was then divided into two groups--pre-COVID period (August 20, 2019-February 19, 2020-6 months) and during the COVID pandemic (February 20, 2020-August 20, 2021-6 months). Results: A total of 140 patients underwent surgery at our institute from August 20, 2019 to August 20, 2020. Of these, 91 patients underwent surgery during the pre-COVID period, and 49 patients underwent surgery during the COVID pandemic. In this devastating phase of the pandemic, our department's total number of surgeries significantly declined to 46.15%. The routine surgeries performed during the pandemic phase show a steep fall from 59.34% in the pre-COVID period to 10.20%. Conclusion: This paper is meant to focus attention on the exorbitant reduction in the operative workflow of the spine patients during the COVID-19 pandemic at a tertiary healthcare institute. It is the need of the hour that orthopedic surgeons maintain equilibrium while providing the best possible treatment to their patients and limiting the spread of the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Background: Since its first report in Wuhan, China, in December 2019, COVID-19 has become a pandemic, affecting millions of people worldwide. Although the virus primarily affects the respiratory tract, gastrointestinal symptoms are also common. The aim of this narrative review is to provide an overview of the pathophysiology and clinical manifestations of gastrointestinal COVID-19. Methods: We conducted a systematic electronic search of English literature up to January 2023 using Medline, Scopus, and the Cochrane Library, focusing on papers that analyzed the role of SARS-CoV-2 in the gastrointestinal tract. Results: Our review highlights that SARS-CoV-2 directly infects the gastrointestinal tract and can cause symptoms such as diarrhea, nausea/vomiting, abdominal pain, anorexia, loss of taste, and increased liver enzymes. These symptoms result from mucosal barrier damage, inflammation, and changes in the microbiota composition. The exact mechanism of how the virus overcomes the acid gastric environment and leads to the intestinal damage is still being studied. Conclusions: Although vaccination has increased the prevalence of less severe symptoms, the long-term interaction with SARS-CoV-2 remains a concern. Understanding the interplay between SARS-CoV-2 and the gastrointestinal tract is essential for future management of the virus. [ABSTRACT FROM AUTHOR]

In 2019, the infectious coronavirus disease 2019 (COVID-19) was first reported in Wuhan, China. It has then become a public health problem in the world. This pandemic is having a heavy impact on the lives of people in our country. All countries are trying to control the spread of this disease. To solve the problem, each person needs to wear masks in a public place. Therefore, we propose a model capable of distinguishing between masked and nonmasked faces using a convolutional neural network (CNN) based on deep learning (DL)—MobileNetV2 in this paper. The model can detect people who are not wearing masks. It has an accuracy of up to 99.37%. The model will be applied in places such as schools, offices, and so on to monitor the wearing masks. [ABSTRACT FROM AUTHOR]

Simple Summary: This paper proposes a modified SEIR model to study COVID-19 in Wuhan. The modified model is calibrated by the public number of COVID-19 hospitalization cases in Wuhan. The paper further uses this model to estimate the earliest date of COVID-19 infection in Wuhan, which is in agreement with some existing results. Based on SEIR (susceptible–exposed–infectious–removed) epidemic model, we propose a modified epidemic mathematical model to describe the spread of the coronavirus disease 2019 (COVID-19) epidemic in Wuhan, China. Using public data, the uncertainty parameters of the proposed model for COVID-19 in Wuhan were calibrated. The uncertainty of the control basic reproduction number was studied with the posterior probability density function of the uncertainty model parameters. The mathematical model was used to inverse deduce the earliest start date of COVID-19 infection in Wuhan with consideration of the lack of information for the initial conditions of the model. The result of the uncertainty analysis of the model is in line with the observed data for COVID-19 in Wuhan, China. The numerical results show that the modified mathematical model could model the spread of COVID-19 epidemics. [ABSTRACT FROM AUTHOR]

This paper displays a Systemic Functional Linguistic and Critical Discourse Analysis of Boris Johnson's first public speech on COVID-19. Covid-19 is a very dangerous infectious disease caused by the last discovered virus of the Coronavirus strain. This virus began in Wuhan's Chinese city in December 2019. COVID-19 has spread from Wuhan to the rest of the world. It has now turned into a pandemic affecting the whole world. Halliday's (2004) model of systemic functional linguistics (meta-functions), relying on interpersonal and ideational metafunctions, and Van Dijk's ideology and discourse (2000) model depending on the argumentation categories, are the adopted models of analysis. The paper's main objectives are to analyze the speech of Prime Minister Boris critically to uncover the used ideologies to advise, persuade and control the people's beliefs and actions. In addition, this paper aims to identify the interpersonal and ideational meta-functions in the selected speeches of the chosen figure stating their frequencies, then finding out how these features uncover the ideological strategies used to affect all people; Finding the argumentation categories that are used by the prime minister to support the ideas and actions presented. The paper presents a theoretical background of discourse, Critical discourse analysis, dominant, and ideology. Explains the adopted models; Analyzes the speech critically. [ABSTRACT FROM AUTHOR]

In this paper, we propose a fractional order Coronavirus (COVID-19) model incorporating non-pharmaceutical interventions and vaccine hesitancy. The proposed model was calibrated with data from literature and validated with reported daily cases of COVID-19 from Wuhan, China. We derived the reproduction number and demonstrated that it is an important threshold parameter for disease persistence and extinction. We examined the relationship between the reproduction number and model parameters. Our findings underscore the importance of awareness and vaccine uptake on mitigating the spread of COVID-19. [ABSTRACT FROM AUTHOR]

A bacterial infection in the lungs can cause viral pneumonia, a disease. Later the middle of December 2019, there have been multiple episodes of pneumonia in Wuhan City, China, with no known cause; it has since been discovered that this pneumonia is actually a new respiratory condition brought on by coronavirus infection. Humans who have lung abnormalities are more likely to develop high-risk conditions; this risk can be decreased with much quicker and more effective therapy. The symptoms of Covid-19 pneumonia are similar to those of viral pneumonia; they are not distinctive. X-ray or Computed Tomography (CT) scan images are used to identify lung abnormalities. Even for a skilled radiologist, it might be challenging to identify Covid-19/Viral pneumonia by looking at the X-ray images. For prompt and effective treatment, accurate diagnosis is essential. In this epidemic condition, delayed diagnosis can cause the number of cases to double, hence a suitable tool is required is necessary for the early identification of Covid-19. This paper highlights various AI techniques as a part of our contribution to swift identification and curie Covid-19 to front-line corona. The safety of Covid-19 people who have viral pneumonia is a concern. Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), two AI technologies from Deep Learning (DL), were utilized to identify Covid-19/Viral pneumonia. The Algorithm is taught utilizing non-public local hospitals or Covid-19 wards, as well as X-ray images of healthy lungs, fake lungs from viral pneumonia, and ostentatious lungs from Covid-19 that are all publicly available. The model is also validated over a lengthy period of time using the transfer learning technique. The results correspond with clinically tested positive Covid-19 patients who underwent Swap testing conducted by medical professionals, giving us an accuracy of 78 to 82 percent. We discovered that each DL model has a unique expertise after testing the various models. [ABSTRACT FROM AUTHOR]

The recent pandemic caused by the Coronavirus Disease (COVID-19) first surfaced in Wuhan, China in December 2019. This paper presents an expert system for the diagnosis of COVID-19 based on its symptoms to aid people in taking precautionary measures. When experts are not available, an expert system that can effectively diagnose the disease is crucial. It takes the place of one or more experts in decision-making and problem-solving. An expert system for diagnosis of COVID-19 is a system developed to recognize early COVID-19 symptoms that individuals may experience by allowing users to directly check the disease with results that can serve as a foundation for additional testing. This study's primary goal is to identify useful COVID-19 detection patterns or knowledge by examining the historical data we have obtained from the Kaggle dataset. The patterns are presented as rules, which are given to the expert system after consultation with a domain expert. A total of 1,048,575 pieces of data were used for model training and testing. To detect COVID-19 disease, we employ a PART rule-based algorithm, which performed 92.47% accurately in a 10-fold cross-validation test. We can therefore draw the conclusion that the algorithm produces a promising result and that the expert system aids in the diagnosis of the disease. The system offers a suggestion in line with the identified symptom. [ABSTRACT FROM AUTHOR]

Mortality is one of the most important epidemiological measures and a key indicator of the effectiveness of potential treatments or interventions. In this paper, a permutation test method of variance analysis is proposed to test the null hypothesis that the real-time fatality rates of multiple groups were equal during the epidemic period. In light of large-scale simulation studies, the proposed test method can accurately identify the differences between different groups and display satisfactory performance. We apply the proposed method to the real dataset of the COVID-19 epidemic in mainland China (excluding Hubei), Hubei Province (excluding Wuhan), and Wuhan from 31 January 2020 to 30 March 2020. By comparing the differences in the disease severity for differential cities, we show that the severity of the early disease of COVID-19 may be related to the effectiveness of interventions and the improvement in medical resources. [ABSTRACT FROM AUTHOR]

In December 2019, the novel Coronavirus, also known as 2019-nCoV or SARS-CoV-2 or COVID-19, was first recognized as a deadly disease in Wuhan, China. In this paper, we analyze two different nonclassical Coronavirus models to observe the outbreaks of this disease. Caputo and Caputo–Fabrizio (C–F) fractional derivatives are considered to simulate the given epidemic models by using two separate methods. We perform all required graphical simulations with the help of real data to demonstrate the behavior of the proposed systems. We observe that the given schemes are highly effective and suitable to analyze the dynamics of Coronavirus. We find different natures of the given model classes for both Caputo and C-F derivative sense. The main contribution of this study is to propose a novel framework of modeling to show how the fractional-order solutions can describe disease dynamics much more clearly as compared to integer-order operators. The motivation to use two different fractional derivatives, Caputo (singular-type kernel) and Caputo–Fabrizio (exponential decay-type kernel) is to explore the model dynamics under different kernels. The applications of two various kernel properties on the same model make this study more effective for scientific observations. [ABSTRACT FROM AUTHOR]

COVID-19, a viral respiratory illness, is caused by Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2), which was first identified in Wuhan, China, in 2019 and rapidly spread worldwide. Testing and isolation were essential to control the virus's transmission due to the severity of the disease. In this context, there is a global interest in the feasibility of employing nano-biosensors, especially those using graphene as a key material, for the real-time detection of the virus. The exceptional properties of graphene and the outstanding performance of nano-biosensors in identifying various viruses prompted a feasibility check on this technology. This paper focuses on the recent advances in using graphene-based electrochemical biosensors for sensing the SARS-CoV-2 virus. Specifically, it reviews various types of electrochemical biosensors, including amperometric, potentiometric, and impedimetric biosensors, and discusses the current challenges associated with biosensors for SARS-CoV-2 detection. The conclusion of this review discusses future directions in the field of electrochemical biosensors for SARS-CoV-2 detection, underscoring the importance of continued research and development in this domain. [ABSTRACT FROM AUTHOR]

Objective: This paper used meta-regression to analyze the heterogenous factors contributing to the prevalence rate of mental health symptoms of the general and frontline healthcare workers (HCWs) in China under the COVID-19 crisis. Method: We systematically searched PubMed, Embase, Web of Science, and Medrxiv and pooled data using random-effects meta-analyses to estimate the prevalence rates, and ran meta-regression to tease out the key sources of the heterogeneity. Results: The meta-regression results uncovered several predictors of the heterogeneity in prevalence rates among published studies, including severity (e.g., above severe vs. above moderate, p < 0.01; above moderate vs. above mild, p < 0.01), type of mental symptoms (PTSD vs. anxiety, p = 0.04), population (frontline vs. general HCWs, p < 0.01), sampling location (Wuhan vs. Non-Wuhan, p = 0.04), and study quality (p = 0.04). Conclusion: The meta-regression findings provide evidence on the factors contributing to the prevalence rate of mental health symptoms of the general and frontline healthcare workers (HCWs) to guide future research and evidence-based medicine in several specific directions. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display%5frecord.php?RecordID=220592, identifier: CRD42020220592. [ABSTRACT FROM AUTHOR]

Background: A new coronavirus was first identified in Wuhan, China in December 2019. Since the times of the 1918 influenza pandemic, malnutrition has been known as a risk factor for severity and mortality from viral pneumonia. Similarly, the recently identified SARS-Cov2 infection (COVID-19) and related pneumonia may be closely linked to malnutrition. Therefore, this study will contribute to new knowledge and awareness of the recording and evaluation of each COVID-19 patient's nutritional status by assessing the effect of malnutrition on ICU admission and death of COVID-19 patients in developing countries. Method: We conducted a prospective cohort study in adult COVID-19 patients admitted to selected COVID-19 Isolation and Treatment Centers, Addis Ababa, Ethiopia. Baseline data of the patients were collected using interviewer-administered structured questionnaire and data on the adverse outcomes of follow up were extracted from follow up chart. The main clinical outcomes (ICU admission and death) were captured every week of follow up. We ran a multivariate Cox's regression analysis to determine the relationship between malnutrition at admission and its effect on ICU admission and death. Results: A total of 581 COVID-19 patients were enrolled. From the total of recruited patients, 346 (59.6%) were males and 235 (40.4%) were females. The mean age of the respondents was 55 years (16.45) years. The Cox proportional hazard model controlled for sex, age group, number of co-morbidities, and number of medications found that malnutrition at admission was associated with ICU admission and death. When compared to well-nourished patients, the rate of ICU admission was significantly associated and found to be higher among underweight [(adjusted hazard ratio (AHR) = 10.02, 95% CI: (8.64–12.10)] and overweight [(AHR = 7.7, 95% CI: (6.41–9.62)] patients. The rate of survival probability was significantly associated and was found to be better among well-nourished patients (AHR = 0.06, 95% CI : (0.01–0.44) when compared with malnourished COVID-19 patients. Conclusion: Malnutrition at the time of admission was shown to increase the risk of ICU admission and mortality among COVID-19 patients. Therefore, it is vital to evaluate patients' nutritional condition early in their admission and provide timely intervention to minimize the effects on patients and the healthcare system. [ABSTRACT FROM AUTHOR]

Background: In December 2019, the first case of COVID-19 was described in Wuhan, China, and by July 2022, there were already 540 million confirmed cases. Due to the rapid spread of the virus, the scientific community has made efforts to develop techniques for the viral classification of SARS-CoV-2. Results: In this context, we developed a new proposal for gene sequence representation with Genomic Signal Processing techniques for the work presented in this paper. First, we applied the mapping approach to samples of six viral species of the Coronaviridae family, which belongs SARS-CoV-2 Virus. We then used the sequence downsized obtained by the method proposed in a deep learning architecture for viral classification, achieving an accuracy of 98.35%, 99.08%, and 99.69% for the 64, 128, and 256 sizes of the viral signatures, respectively, and obtaining 99.95% precision for the vectors with size 256. Conclusions: The classification results obtained, in comparison to the results produced using other state-of-the-art representation techniques, demonstrate that the proposed mapping can provide a satisfactory performance result with low computational memory and processing time costs. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for improving the efficacy of COVID-19 vaccines against emergent SARS-CoV-2 variants. We have evaluated the immunogenicity of unadjuvanted wild-type (WU S1-RS09cg) and variant-specific (Delta S1-RS09cg and OM S1-RS09cg) S1 subunit protein vaccines delivered either as a monovalent or a trivalent antigen in BALB/c mice. Our results show that a trivalent approach induced a broader humoral response with more coverage against antigenically distinct variants, especially when compared to monovalent Omicron-specific S1. This trivalent approach was also found to have increased or equivalent ACE2 binding inhibition, and increased S1 IgG endpoint titer at early timepoints, against SARS-CoV-2 spike variants when compared monovalent Wuhan, Delta, or Omicron S1. Our results demonstrate the utility of protein subunit vaccines against COVID-19 and provide insights into the impact of variant-specific COVID-19 vaccine approaches on the immune response in the current SARS-CoV-2 variant landscape. Particularly, our study provides insight into effects of further increasing valency of currently approved SARS-CoV-2 vaccines, a promising approach for improving protection to curtail emerging viral variants. [ABSTRACT FROM AUTHOR]

Introduction: Coronavirus disease 2019 (COVID-19) is a novel coronavirus that was frequently isolated from Wuhan, China in patients with respiratory tract infections. The most prevalent symptoms are fever and respiratory tract involvement. In this paper, seizures, an uncommon manifestation of the condition, is described. Case Discussion: A previously healthy 34-year-old female was taken to the emergency room with a widespread tonic-clonic seizure. Five days prior to admission, the patient complained of fever, cough, headache, widespread weakness, and myalgias. She experienced two episodes of convulsions around one hour ago. The MRI of the brain was normal, whereas the CT of the chest indicated localised ground-glass opacities. Using real-time PCR, the respiratory material was positive for COVID-19. The patient's symptoms improved after using anticonvulsive and antiviral drugs. Conclusions: To our knowledge, this is one of the rare case studies reporting a link between frequent seizures and COVID - 19. [ABSTRACT FROM AUTHOR]

The outbreak of Novel Coronavirus infection (COVID-19) has enormous economic consequences. It happened in the Wuhan city of China and rapidly spread across the world. Malaysia has suffered enormous economic losses. Extended lockdowns, flight disruptions and other measures taken to contain the infection, and releasing stimulus packages have caused considerable downturn in the economic growth. A genesis of the pandemic origin and transmission is necessary not only to mitigate the prevailing situation but also preventing a repeat of such an event in the future. The coronavirus outbreak is a wakeup call for insulating the society and economy, especially the most vulnerable sectors. Although the outbreak happened in December 2019, it continues to inflict health hazards and economic meltdown. The local bourse has plunged and the losses in the capital markets will have a significant impact on the psychographics of the general populace. As this phenomenon escalates, the financial structure of banks and firms would be rendered unstable, and the impact of this was a severe contraction in the economy and a high unemployment rate, also vulnerability of the tourism sector to pandemics is due to obvious reasons; people avoid visiting places in the grip of pandemic and tourism destinations close their doors to arrivals from the virus-affected countries. There is a need for international mechanisms for regulating the factors that lead to origin of the pandemic and applying available means for containing it should that happen. This paper highlights the causes of the pandemic to effectively link its consequences for Malaysia economic while also discussing the need to support its revival. An important measure emphasized in in this paper as a revival strategy is building resilience in the natural capital. This systems approach is necessary in dealing with the post-COVID-19 scenario and in better preparedness for any future economic hazards. [ABSTRACT FROM AUTHOR]

At the end of 2019, the communicable disease COVID-19 was first detected in the city of Wuhan, China. It affected every country in the world. The Peruvian government announced quarantine and social distancing measures to prevent the spread of the virus. As a result of the measures implemented economic and social well-being of people were affected causing an increase in stress, depression and anxiety, which are emotional disorders that affect mental health. Added to this is the lack of access to mental health services. The objective is to develop a telemedicine mobile application prototype for mental health services based on android. The methodology applied was Rational Unified Process (RUP) methodology because it allows documenting, ordering and structuring software development. The result obtained was a prototype of a telemedicine mobile application with a design and functionalities that make access easy for patients and specialist to mental health services. The quality of the prototype was evaluated through the judgment of experts, who assessed its efficiency, usability and security Obtaining after the calculation, 4.66 average or mean; this indicates that according to the established quality range, the quality of the mobile prototype is high. In conclusion, it was possible to develop an efficient, easyto- use and safe mobile telehealth prototype that provides a solution facilitating patient access to mental health care services for their emotional, psychological and social well-being. [ABSTRACT FROM AUTHOR]

Scientists first identified Human Coronavirus in the year 1965. Then, a study was performed on different human and animal viruses, which were named based on their crown-like appearance. Human coronaviruses are responsible for many children's upper respiratory tract infections. At least five new human coronaviruses have been identified since 2003, including the severe acute respiratory syndrome (SARS) coronavirus, which caused significant morbidity and mortality.1 Per NL and the New Haven, the virus associated with upper and lower respiratory tract disease and likely common human pathogens are identified as the Group-I coronaviruses (SARS-CoV-I). The novel coronavirus (SARS-CoV-II), which appeared in Wuhan, China, in December 2019, is responsible for Coronavirus disease 2019 (COVID-19) and causes respiratory symptoms that can feel like a cold, flu, or pneumonia. Geographical transmission of the virus and the sudden increase in cases are much faster than SARS and Middle East respiratory syndrome (MERS). COVID-19 is the first global pandemic caused by a coronavirus causing outbreaks in 211 countries. The vaccine against COVID-19 is an effective prophylactic strategy for controlling and preventing the virus. The vaccine is being developed in about 90 institutions worldwide. This research paper focuses on COVID-19 vaccine development strategies implemented by various institutions and pharmaceutical companies worldwide and regulatory requirements for vaccine approval. [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]

In December 2019, Coronavirus Disease 2019 (COVID-19) was reported in Wuhan, China. Comprehensive strategies for quick identification, prevention, control, and remedy of COVID-19 have been implemented until today. Advances in various nanoparticle-based technologies, including organic and inorganic nanoparticles, have created new perspectives in this field. These materials were extensively used to control COVID-19 because of their specific attribution to preparing antiviral face masks, various safety sensors, etc. In this review, the most current nanoparticle-based technologies, applications, and achievements against the coronavirus were summarized and highlighted. This paper also offers nanoparticle preventive, diagnostic, and treatment options to combat this pandemic. [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]

COVID-19 is a disease caused by infection with the virus 2019-nCoV, a single-stranded RNA virus. During the infection and transmission processes, the virus evolves and mutates rapidly, though the disease has been quickly controlled in Wuhan by 'Fangcang' hospitals. To model the virulence evolution, in this paper, we formulate a new age structured epidemic model. Under the tradeoff hypothesis, two special scenarios are used to study the virulence evolution by theoretical analysis and numerical simulations. Results show that, before 'Fangcang' hospitals, two scenarios are both consistent with the data. After 'Fangcang' hospitals, Scenario I rather than Scenario II is consistent with the data. It is concluded that the transmission pattern of COVID-19 in Wuhan obey Scenario I rather than Scenario II. Theoretical analysis show that, in Scenario I, shortening the value of L (diagnosis period) can result in an enormous selective pressure on the evolution of 2019-nCoV. [ABSTRACT FROM AUTHOR]

Purpose: In order to control the corona virus disease-2019 (COVID-19) pandemic, many countries have adopted social quarantine policies, with older adults in Wuhan suffering the longest and most severe conditions. But few studies have explored the impact of this on the mental health of older adults in Wuhan. The purpose of this paper is to examine changes in the residential status and mental health of this group when 1 year after the social isolation policies in Wuhan.Method: A cross-sectional study with convenience sampling was conducted to assess the questionnaire of older adults in a total of 21 streets in 5 central and 2 distant urban districts of Wuhan. Using a self-compiled living status questionnaire, the Patient Health Questionnaire-9, the General Anxiety Disorder-7, the PTSD Checklist-Civilian Version, the UCLA Loneliness Scale and the Social Support Rating Scale, our survey evaluated the living status, depression, anxiety, post-traumatic stress symptoms, loneliness and social support of all the participants.Results: A total of 400 valid samples were obtained. One year after experiencing social isolation, older adults had not changed much from their pre-epidemic living status and mostly lived with their partners. They had satisfactory social support (33.86 ± 6.92) and low levels of depression (3.12 ± 4.30), anxiety (1.52 ± 3.19) and post-traumatic stress symptoms (21.41 ± 7.39), but there were moderate levels of loneliness (38.27 ± 9.31). Among them, depression, anxiety and post-traumatic stress symptoms were significantly higher (ps < 0.05) in older adults who were COVID-19 close contacts while experiencing social isolation.Conclusion: One year after experiencing Wuhan's harsh social isolation, older adults in the Wuhan community did not experience significant symptoms of depression, anxiety or post-traumatic stress, but loneliness has increased and the mental health of older adults who were COVID-19 close contacts needs attention. [ABSTRACT FROM AUTHOR]

COVID-19, the disease caused by the novel coronavirus (SARS-CoV-2), first emerged in Wuhan, China late in December 2019. Not long after, the virus spread worldwide and was declared a pandemic by the World Health Organization in March 2020. This caused many changes around the world and in the United States, including an educational shift towards online learning. In this paper, we seek to understand how the COVID-19 pandemic and the increase in online learning impact college students' emotional wellbeing. We use several machine learning and statistical models to analyze data collected by the Faculty of Public Administration at the University of Ljubljana, Slovenia in conjunction with an international consortium of universities, other higher education institutions, and students' associations. Our results indicate that features related to students' academic life have the largest impact on their emotional wellbeing. Other important factors include students' satisfaction with their university's and government's handling of the pandemic as well as students' financial security. [ABSTRACT FROM AUTHOR]

The coronavirus epidemic (COVID-19) is growing quickly around the globe. The first acute atypical respiratory illness was reported in December 2019, in Wuhan, China. This quickly spread from Wuhan city to other locations. Deep learning (DL) algorithms are one of the greatest solutions for consistently and readily recognizing COVID-19. Previously, many researchers used state-of-the-art approaches for the classification of COVID-19. In this paper, we present a deep learning approach with the EfficientnetB4 model, centered on transfer learning, for the classification of COVID-19. Transfer learning is a popular technique that uses pre-trained models that have been trained on the ImageNet database and employed on a new problem to increase generalization. We presented an in-depth training approach to extract the visual properties of COVID-19 in exchange for providing a medical assessment before infection testing. The proposed methodology is assessed on a publicly accessible X-ray imaging dataset. The proposed framework achieves an accuracy of 97%. Our model's experimental findings demonstrate that it is extremely successful at identifying COVID-19 and that it may be supplied to health organizations as a precise, quick, and successful decision support system for COVID-19 identification. [ABSTRACT FROM AUTHOR]

Older people were globally the most vulnerable during the pandemic. This paper examines the mortality and psychological crisis of older people during the pandemic in four cities, namely Wuhan, Milan, London, and Hong Kong, in order to explain their high death rates. The sample cities were purposefully selected to cover different degrees of social connectedness and outbreak periods. Quantitative data were employed to account for death and suicide data. Qualitative data analyses in government reports, public press releases, NGO announcements, and journal articles were used to study government responses and disruptions of essential healthcare services. Published scientific studies provided important information on the psychological stress and resilience of older people. Findings based on a welfare regime perspective showed that society and governments have not learned from the experiences of other countries, which resulted in high numbers of preventable deaths. Older people have successfully coped with living difficulties while serious psychological disturbances and suicidal ideations were absent at least for the period up to 2022. Older care home residents, unfortunately, were not protected with proper shielding and available vaccinations. Public inefficiency and negligence contributed to a disproportionately high rate of mortality in nursing homes. [ABSTRACT FROM AUTHOR]

This paper manages the Origin of Covid (SARS-CoV-2-novel Covid) and the Vaccination view. Within excess of 10 million tainted cases and more than 3 million setbacks at the hour of composing this piece, the original Covid actuated wellbeing disease has arisen as the most serious among every one of the worldwide pandemics. The novel (COVID-19 or 2019-nCoV) was found in Wuhan, Hubei Province, China. Bats were liable for the underlying spread, which was subsequently spread to people by the raccoon canine and palm civet. The normal COVID-19 manifestations are fever, dry hack, and sleepiness. The significant manifestations are trouble breathing, chest torment, and loss of speech. The indications and indications of SARS-CoV-2 prompting COVID-19 completely match those of the occasional or, dust sensitivities and flu as migraine, touchiness of the throat, dry hack, weariness, fever, and at times loss of sensation (Tu et al., year2020). An individual tainted with the diseases of sensitivity to pollen or, flu sensitivity is additionally liable to show temperature that can be recorded with thermo-scanners prompting the individual to turn into a suspect of the sickness. Along these lines, before immunizing, a fast and exact analytic meter or, the pack is the need of great importance to identify the SARS-CoV-2 inferable from the way that the testing in view of PCR is tedious and exorbitant (Sirkeci and Yucesahin, year2020). [ABSTRACT FROM AUTHOR]

After about 2 years since the first detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in Wuhan, China, in December 2019 that resulted in a worldwide pandemic, 6.2 million deaths have been recorded. As a result, there is an urgent need for the development of a safe and effective vaccine for coronavirus disease 2019 (COVID-19). Endeavors for the production of effective vaccines inexhaustibly are continuing. At present according to the World Health Organization (WHO) COVID-19 vaccine tracker and landscape, 153 vaccine candidates are developing in the clinical phase all over the world. Some new and exciting platforms are nucleic acid-based vaccines such as Pfizer Biontech and Moderna vaccines consisting of a messenger RNA (mRNA) encoding a viral spike protein in host cells. Another novel vaccine platform is viral vector vaccine candidates that could be replicating or nonreplicating. These types of vaccines that have a harmless viral vector like adenovirus contain a genome encoding the spike protein of SARS-CoV-2, which induces significant immune responses. This technology of vaccine manufacturing has previously been used in many human clinical trials conducted for adenoviral vector-based vaccines against different infectious agents, including Ebola virus, Zika virus, HIV, and malaria. In this paper, we have a review of nucleic acid-based vaccines that are passing their phase 3 and 4 clinical trials and discuss their efficiency and adverse effects. [ABSTRACT FROM AUTHOR]

Objectives: COVID-19 is a transmissible illness triggered by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since its onset in late 2019 in Wuhan city of China, it continues to spread universally, leading to an ongoing pandemic that shattered all efforts to restrain it. On the other hand, in Africa, the COVID-19 infection may be influenced by malaria coinfection. Hence, in this review article, we aimed to give a comprehensive account of the similarities between COVID-19 and malaria in terms of symptoms, clinical, immunological, and molecular perspectives. Methodology: In this article, we reviewed over 50 research papers to highlight the multilayered similarities between COVID-19 and malaria infections that might influence the ontology of COVID-19. Results: Despite the poor health and fragile medical system of many sub-Saharan African countries, they persisted with a statistically significantly low number of COVID-19 cases. This was attributed to many factors such as the young population age, the warm weather, the lack of proper diagnosis, previous infection with malaria, the use of antimalarial drugs, etc. Additionally, population genetics appears to play a significant role in shaping the COVID-19 dynamics. This is evident as recent genomic screening analyses of the angiotensin-converting enzyme 2 (ACE2) and malaria-associatedvariants identified 6 candidate genes that might play a role in malaria and COVID-19 incidence and severity. Moreover, the clinical and pathological resemblances between the two diseases have made considerable confusion in the diagnosis and thereafter curb the disease in Africa. Therefore, possible similarities between the diseases in regards to the clinical, pathological, immunological, and genetical ascription were discussed. Conclusion: Understanding the dynamics of COVID-19 infection in Sub-Saharan Africa and how it is shaped by another endemic disease like malaria can provide insights into how to tailor a successful diagnostic, intervention, and control plans that lower both disease morbidity and mortality. [ABSTRACT FROM AUTHOR]

The novel coronavirus illness (COVID-19) outbreak, which began in a seafood market in Wuhan, Hubei Province, China, in mid-December 2019, has spread to almost all countries, territories, and places throughout the world. And since the fault in diagnosis of a disease causes a psychological impact, this was very much visible in the spread of COVID-19. This research aims to address this issue by providing a better solution for diagnosis of the COVID-19 disease. The paper also addresses a very important issue of having less data for disease prediction models by elaborating on data handling techniques. Thus, special focus has been given on data processing and handling, with an aim to develop an improved machine learning model for diagnosis of COVID-19. Random Forest (RF), Decision tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Support vector machine, and Deep Neural network (DNN) models are developed using the Hospital Israelita Albert Einstein (in São Paulo, Brazil) dataset to diagnose COVID-19. The dataset is pre-processed and distributed DT is applied to rank the features. Data augmentation has been applied to generate datasets for improving classification accuracy. The DNN model dominates overall techniques giving the highest accuracy of 96.99%, recall of 96.98%, and precision of 96.94%, which is better than or comparable to other research work. All the algorithms are implemented in a distributed environment on the Spark platform. [ABSTRACT FROM AUTHOR]

On 31 December 2019, WHO received inputs of pneumonia cases in Wuhan city of China, but the cause of infection could not be known initially. However, after some time, the medical researchers of China disclosed a novel coronavirus as the cause on 7 January 2020, named "2019-nCoV." The coronavirus (CoV) belongs to Severe Acute Respiratory Syndrome (SARS) species that leads to infection, causing illness, which ranges from the common cold to serious sickness. Finally, on 11 March 2020, the WHO Director-General Dr. Tedros Adhanom Ghebreyesus announced the outbreak as a pandemic. As the fear and ambiguity rose among companies and firms, the profit rate seemed to be lower due to the COVID-19 global impact. Nearly US$6 trillion in wealth from 24th to 28 February 2020 of the stock market has been wiped out. There was a great decrease in value over the S&P index, which abolished over $5 trillion in the same week. However, the largest 10 companies of S&P faced a loss of $1.4 trillion. The investors make an analytical prediction that firms' profits may drop in response to the impact of coronavirus. The prime focus is on the importance of digital business practices and how different sectors have been affected in terms of economic loss during this pandemic outbreak in this paper. [ABSTRACT FROM AUTHOR]

Travel restrictions have become an important epidemic preventive measure, but there are few relevant quantitative studies. In this paper, travel proportion is introduced into a four-compartment model to quantify the spread of COVID-19 in Wuhan. It is found that decreasing the travel proportion can reduce the peak of infections and delay the peak time. When the travel proportion is less than 35%, transmission can be prevented. This method provides reference for other places. [ABSTRACT FROM AUTHOR]