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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]

This paper addresses the reactions of domestic helpers to the Wuhan (Hubei Province) lockdown that began on January 23, 2020. We use a novel dataset containing the information of over 40,000 Chinese domestic helpers registered on a leading professional website from November 2019 to June 2020. The results indicate a declining pattern of short-term labor supply of domestic helpers across 11 major Chinese cities, which shows an increase in the expected monthly wage of domestic helpers in these cities. More importantly, using a difference-in-difference (DID) model, this paper provides some evidence on the existence of labor market discrimination against domestic helpers born in Hubei Province due to employers' fear of infection. [ABSTRACT FROM AUTHOR]

In December 2019, a group of people in Wuhan city of Hubei province of China were found to be affected by an infection called dark etiology pneumonia. The outbreak of this pneumonia infection was declared a deadly disease by the China Center for Disease Control and Prevention on January 9, 2020, named Novel Coronavirus 2019 (nCoV-2019). This nCoV- 2019 is now known as COVID-19. There is a big list of infections of this coronavirus which is present in the form of a big family. This virus can cause several diseases that usually develop with a serious problem. According to the World Health Organization (WHO), 2019-nCoV has been placed as the modern generation of Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) coronaviruses, so COVID-19 can repeatedly change its internal genome structure to extend its existence. Understanding and accurately predicting the mutational properties of the genome structure of COVID-19 can forma good leadership role in preventing and fighting against coronavirus. In this research paper, an analytical approach has been presented which is based on the k-means cluster technique of machine learning to find the clusters over the mutational properties of the COVID-19 viruses' complete genome. This method would be able to act as a promising tool to monitor and track pathogenic infections in their stable and local genetics/hereditary varieties. This paper identifies five main clusters of mutations with k = 5 as best in most cases in the coronavirus that could help scientists and researchers develop disease control vaccines for the transformation of coronaviruses. [ABSTRACT FROM AUTHOR]

An outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), epi-centred in Hubei Province of the People's Republic of China, quickly spread worldwide and caused COVID-19 pandemic. It infected hundreds of millions of people and caused millions of deaths. In this paper, we develop a compartmental ODE model of COVID-19 transmission. We consider a possibility of breakthrough infections after the vaccination and account for both symptomatic and asymptomatic infections and transmissions. We also incorporate game theory to study the optimal vaccination decisions from the individuals' perspective. We show that vaccination alone is unlikely to eliminate COVID-19. To achieve herd immunity, the individuals would have to receive a dose of a vaccine more frequently than once every 3 months. It is therefore crucial to adhere to various guidelines, such as quarantine, isolate and wear a mask if tested positive for COVID-19. [ABSTRACT FROM AUTHOR]

The genome of the novel coronavirus (COVID-19) disease was first sequenced in January 2020, approximately a month after its emergence in Wuhan, capital of Hubei province, China. COVID-19 genome sequencing is critical to understanding the virus behavior, its origin, how fast it mutates, and for the development of drugs/vaccines and effective preventive strategies. This paper investigates the use of artificial intelligence techniques to learn interesting information from COVID-19 genome sequences. Sequential pattern mining (SPM) is first applied on a computer-understandable corpus of COVID-19 genome sequences to see if interesting hidden patterns can be found, which reveal frequent patterns of nucleotide bases and their relationships with each other. Second, sequence prediction models are applied to the corpus to evaluate if nucleotide base(s) can be predicted from previous ones. Third, for mutation analysis in genome sequences, an algorithm is designed to find the locations in the genome sequences where the nucleotide bases are changed and to calculate the mutation rate. Obtained results suggest that SPM and mutation analysis techniques can reveal interesting information and patterns in COVID-19 genome sequences to examine the evolution and variations in COVID-19 strains respectively. [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]

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]

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]

After the outbreak of SARS and MERS, the world is now in the grip of another viral disease named COVID-19 caused by a beta Coronavirus – SARS COV-2 which appears to be the only one with a pandemic potential. The case of COVID-19 was reported in the Hubei province of Wuhan city in Central China at the end of December 2019 and it is suspected that the sea food market played a role in this outbreak which was closed abruptly. Subsequently, a Public Health Emergency of International Concern was declared on 30 January 2020 by the World Health Organization. Both SARS and MERS corona viruses had its reservoir in bats and were transferred to humans from palm civets and camels respectively. This virus can be transmitted through airborne droplets. Natural reservoir and intermediate host of COVID-19 is yet to be identified. This paper reviews the occurrences of viral diseases in the recent times including SARS and MERS. As an addition to this, the paper will contain a detailed examination of the COVID-19 Pandemic. [ABSTRACT FROM AUTHOR]

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"Novel Coronavirus", also known as COVID-19 has spread globally which is sparked in Wuhan, the capital city of Hubei in China. It is a big problem for the world's health organization as the number of affected cases as well as deaths increases in each country. Several Machine Learning models are designed to predict and forecast the COVID-19 outbreak. In this paper, we are designed to understand the current scenario of coronavirus, and predicting and forecasting the total number of confirmed cases of novel COVID-19 in India using linear regression, polynomial regression, and support vector machine regression models. The decision-making process of an organization depends on the data analysis and it is presenting with interactive visual representation. It is not possible to predict without having necessary information from the past and the present. Trends over time tell us where we were and where we can go with our model. The model is predicting the number of confirmed cases from 30th May 2021 to 15th June 2021on the data available from 22 January 2020 to 29 May 2021. Forecasting the future of confirmed cases can help health organizations to plan medical facilities. Moreover, the time series forecasting technique is being engaged to forecast the overall total of confirmed cases in the upcoming. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease (COVID-19) broke out worldwide in 2020. The purpose of this paper was to find out the impact of migrant population on the epidemic, aiming to provide data support and suggestions for control measures in various epidemic areas. Generalized additive model was utilized to model the relationship between migrant population and the cumulative number of confirmed cases of COVID-19. The difference of spatial distribution was analyzed through spatial autocorrelation and hot spot analysis. Generalized additive model demonstrated that the cumulative number of confirmed cases was positively correlated with migration index and population density. The predictive results showed that, if no travel restrictions are imposed on the migrant population as usual, this number of COVID-19 would have reached 27,483 (95% CI 16,074, 48,097; the actual number was 23,177). The increase in one city (Jian) would be 577.23% (95% CI 322.73%, 972.73%) compared with the real confirmed cases of COVID-19. The average increase in 73 cities was 85.53% (95% CI 19.53%, 189.81%). Among the migration destinations, the number of cases in cities of Hubei province, Chongqing, and Beijing was relatively high, and there were large-scale high-prevalence clusters in eastern Hubei province. Without restrictions on migration, the high prevalence areas in Hubei province and its surrounding areas will be further expanded. The reduced population mobility and population density can greatly slow down the spread of the epidemic. All epidemic areas should suspend the transportation between cities, strictly control the population travel, and decrease the population density, so as to reduce the spread of COVID-19. [ABSTRACT FROM AUTHOR]

Novel coronavirus pneumonia in 17 city (Hubei) provinces was analyzed by using the principle of thermodynamics. A thermodynamic imaging model of infectious diseases was established to calculate the cumulative superimposed density of epidemic in 17 cities (prefectures). An evaluation rule of urban risk grade is established and evaluates the COVID-19 risk of 17 cities. The results show that (1) the higher the superimposed density of urban epidemic, the more infected people. (2) In the incubation stage, the thermodynamic imaging shows a point distribution, random walk, and outward diffusion trend. In the initial stage, the color of thermodynamic imaging gradually deepened and the range gradually expanded. During the burst stage, the thermodynamic imaging color deepens rapidly and the scope expands rapidly. In the stable stage, the thermodynamic imaging color becomes darkest and the range is extended to the pole. (3) According to the situation of COVID-19 transmission in Hubei Province, the cumulative superimposed density of Wuhan epidemic is far more than 10,000, ranking as "highest-risk." Xiaogan and other 10 cities have a cumulative superimposed density within the range of [1000, 10,000], ranking as "high-risk." Shiyan and other 5 cities have accumulated superimposed density values within the range of [100, 1000], ranking as "medium-risk." Shennongjia cumulative superimposed density value is less than 100, and the level is "low-risk." [ABSTRACT FROM AUTHOR]

COVID-19 is a highly contagious disease caused by the so-called coronavirus. The causative agent of the disease, the SARS-CoV-2 virus, was firstly discovered in the late 2019 in the city of Wuhan, in the Chinese province of Hubei. From there, it spread rapidly around the world causing a worldwide pandemic of COVID-19 disease. In Croatia, the first case of infection was confirmed on February 25, 2020. This paper describes the movement of the number of infected, the number of deaths and the number of active and cured persons from COVID-19 disease in the Republic of Croatia in the period from 25 February 2020 to 25 February 2021. The paper also presents the movement of the values of variables associated with COVID-19 disease at the county level in the Republic of Croatia. This paper provides the basis for future statistical modelling related to the movement of COVID-19 variables, which should result in solutions to curb this pandemic. [ABSTRACT FROM AUTHOR]

India is in the midst of a conflict for its future economic and social relations one year after the Covid-19 pandemic began. During this time, the Indian government promoted policies that exacerbated labour exploitation and increased inequality. Both past economic crises, experience and early signs of labour market and social effects during the present epidemic, implies that the COVID19 crisis will disproportionately affect immigrants and their offspring. Prior battles to replicate conditions for accumulation and resolve the social reproduction dilemma can be traced back to these measures. This paper contends that this process will likely continue long after India and other countries have recovered economically from the pandemic, and that these contestations may provide possibilities for uniting struggles across existing boundaries. A pneumonia outbreak of unknown origin was reported in Wuhan, Hubei Province, China, in December 2019. Epidemiological evidence linked cases of pneumonia to the Huanan Seafood Wholesale Market. Inoculation of respiratory materials into human airway epithelial cells, Vero E6 and Huh7 cell lines, resulted in the isolation of a novel respiratory virus, which was later identified as a novel coronavirus linked to SARS-CoV after genome analysis (SARS-CoV-2). [ABSTRACT FROM AUTHOR]

The corona virus disease (COVID-19), which has been characterized as a pandemic by world health organization (WHO), is attacking society at its core. Pandemics in general are not merely serious public health concerns, but also trigger catastrophic socio-economic consequences in the infected countries. COVID-19 disease emerged in Wuhan city, the capital of Hubei Province in the People's Republic of China on 31-Dec-2019. The epidemiological profile of the infection has revealed that it has so far affected 216 countries, and has infected over 9 million people globally, as per the World Health Organization (WHO) COVID-19 report. India's total COVID-19 infections as on 02-Feb-2020 were 10, 767, 206 (10.7 million approximately) cases with 1, 54, 522 (1.4%) case fatalities as on 02-Feb-2021. Out of 186,514 active cases, fatalities were 14,989 (8%) as per the Ministry of Health and Family Welfare, Government of India (2021). Genomic analysis revealed that SARS-CoV-2 is phylogenetically related to severe acute respiratory syndrome (SARS) like Bat viruses. Therefore, Bats possibly could be the primary reservoir of COVID-19. The transmission profile has confirmed asymptomatic and human-to- human transmission through close contacts in familial and social settings. The intermediate source of origin and transfer to humans is not known. However, rapid human to human transfer has been confirmed widely and person-to-person transmission of SARS-CoV-2 have occurred between two persons with prolonged, unprotected exposure with the symptomatic patient. It has infected humans in all age groups, of all ethnicities, and both sexes (males and females) while spreading through communities at an alarming rate. Infected patients experience common cold-like symptoms along with raised temperature, non-productive coughing and difficulty to breathe. COVID-19 has rapidly and adversely affected the life and livelihood of mankind. This paper portrays the genetic configuration, epidemiological profile, and mode of transmission of COVID-19 and its impacts on environment in general and society in particular pertaining to health care, education, agriculture, family relation, industry and employment, mental wellbeing, domestic discord, tourism, life and livelihood, social liaison, and delineates global strategies for COVID-19 prevention and control. [ABSTRACT FROM AUTHOR]

Background: In December 2019, an outbreak of pneumonia of no identifiable cause had been widely spread - ing in Wuhan, Hubei Province, China. In late December 2019, the pathogen was identified as a new strain of coronavirus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and its associated disease, named Coronavirus disease-19 (COVID-19). As of July 3, 2020, 10,906,822 cases have been confirmed worldwide, with 522,112 deaths, as reported by the World Health Organization. Given the developing situa - tion with COVID-19, extensive studies are urgently needed that determine indicators of severity to provide evidence for health policymakers. This study aimed to review the currently available data on hematological parameters to predict disease severity in patients of COVID-19. Methods: We performed a review using three electronic databases. Fourteen papers are included. In this review, we summarized the latest research highlighting the clinical features, pathogenesis, and diagnosis, with a concentration on hematological parameters that predict severity to help identify patients with severe dis - ease. These indicators will help doctors know earlier which patients may need intensive care unit (ICU) care to manage their patients with an evidence-based protocol. Results: Most reviewed studies report hematological parameters that predict disease severity, including lymphopenia and elevated fibrin fragment D. Conclusions: We recommend using these indicators in addition to others, like respiratory failure, shock, or multiple organs dysfunction syndrome, for disease classification in situations where there are insufficient ventilators or ICU beds to prioritize advanced medical services accordingly and to ensure the maximum provision of sufficient medical care. [ABSTRACT FROM AUTHOR]

An outbreak of the so-called Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2), has been spreading rapidly nationwide in China since December 2019. Wuhan, Hubei Province, is the hardest-hit region, with a rise in confirmed cases and its hospitals overwhelmed. On 2nd February, 2020, Wuhan began to build a modular hospital to treat patients caught with mild illness. The mobile modular hospital is mainly composed of medical modules, technical support modules, ward units, living support units and transportation capacity under field conditions, and there are complete equipment and specialized personnel to treat patients. Due to the severity and particularity of SARS-CoV-2, taking granted from lessons learnt from mobile modular hospitals, we use the existing large venues to construct a new fixed modular hospital. As patients need to be treated and tested, it is important to develop a clinical laboratory in the modular hospital and ensure biosafety. The construction of a clinical laboratory in the modular hospital is faced with problems such as time pressure, limited site selection, high level of biosafety, lack of experience and so forth. This paper mainly discusses how to construct the clinical laboratory in the modular hospital quickly and safely and put it into use to provide testing service for patients under various limited conditions. [ABSTRACT FROM AUTHOR]

In December 2019, the first cases of a new contagious disease were diagnosed in the city of Wuhan, the capital of Hubei province in China. Within a short period of time the outbreak developed exponentially into a pandemic that infected millions of people, with a global death toll of more than 500,000 during its first 6 months. Eventually, the novel disease was named coronavirus disease 2019 (COVID-19), and the new virus was identified as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Similar to all known pandemics throughout history, COVID-19 has been accompanied by a large degree of fear, anxiety, uncertainty, and economic disaster worldwide. Despite multiple publications and increasing knowledge regarding the biological secrets of SARS-CoV-2, as of the writing of this paper, there is neither an approved vaccine nor medication to prevent infection or cure for this highly infectious disease. Past pandemics were caused by a wide range of microbes, primarily viruses, but also bacteria. Characteristically, a significant proportion of them originated in different animal species (zoonoses). Since an understanding of the microbial cause of these diseases was unveiled relatively late in human history, past pandemics were often attributed to strange causes including punishment from God, demonic activity, or volatile unspecified substances. Although a high case fatality ratio was common to all pandemic diseases, some striking clinical characteristics of each disease allowed contemporaneous people to clinically diagnose the infection despite null microbiological information. In comparison to past pandemics, SARS-CoV-2 has tricky and complex mechanisms that have facilitated its rapid and catastrophic spread worldwide. [ABSTRACT FROM AUTHOR]

In December 2019, a new virus (initially called 'Novel Coronavirus 2019-nCoV' and later renamed to SARS-CoV-2) causing severe acute respiratory syndrome (coronavirus disease COVID-19) emerged in Wuhan, Hubei Province, China, and rapidly spread to other parts of China and other countries around the world, despite China's massive efforts to contain the disease within Hubei. As with the original SARS-CoV epidemic of 2002/2003 and with seasonal influenza, geographic information systems and methods, including, among other application possibilities, online real-or near-real-time mapping of disease cases and of social media reactions to disease spread, predictive risk mapping using population travel data, and tracing and mapping super-spreader trajectories and contacts across space and time, are proving indispensable for timely and effective epidemic monitoring and response. This paper offers pointers to, and describes, a range of practical online/mobile GIS and mapping dashboards and applications for tracking the 2019/2020 coronavirus epidemic and associated events as they unfold around the world. Some of these dashboards and applications are receiving data updates in near-real-time (at the time of writing), and one of them is meant for individual users (in China) to check if the app user has had any close contact with a person confirmed or suspected to have been infected with SARS-CoV-2 in the recent past. We also discuss additional ways GIS can support the fight against infectious disease outbreaks and epidemics. [ABSTRACT FROM AUTHOR]

Oropharyngeal microbiomes play a significant role in the susceptibility and severity of COVID-19, yet the role of these microbiomes play for the development of COVID-19 Omicron variant have not been reported. A total of 791 pharyngeal swab samples were prospectively included in this study, including 297 confirmed cases of Omicron variant (CCO), 222 confirmed case of Omicron who recovered (CCOR), 73 confirmed cases of original strain (CCOS) and 199 healthy controls (HC). All samples completed MiSeq sequencing. The results showed that compared with HC, conditional pathogens increased in CCO, while acid-producing bacteria decreased. Based on six optimal oropharyngeal operational taxonomy units (OTUs), we constructed a marker microbial classifier to distinguish between patients with Omicron variant and healthy people, and achieved high diagnostic efficiency in both the discovery queue and the verification queue. At same time, we introduced a group of cross-age infection verification cohort and Omicron variant subtype XBB.1.5 branch, which can be accurately distinguished by this diagnostic model. We also analyzed the characteristics of oropharyngeal microbiomes in two subgroups of Omicron disease group—severity of infection and vaccination times, and found that the change of oropharyngeal microbiomes may affect the severity of the disease and the efficacy of the vaccine. In addition, we found that some genera with significant differences gradually increased or decreased with the recovery of Omicron variant infection. The results of Spearman analysis showed that 27 oropharyngeal OTUs were closely related to 6 clinical indexes in CCO and HC. Finally, we found that the Omicron variant had different characterization of oropharyngeal microbiomes from the original strain. Our research characterizes oropharyngeal microbiomes of Omicron variant cases and rehabilitation cases, successfully constructed and verified the non-invasive diagnostic model of Omicron variant, described the correlation between microbial OTUs and clinical indexes. It was found that the infection of Omicron variant and the infection of original strain have different characteristics of oropharyngeal microbiomes. Author summary: COVID-19, caused by the SARS-CoV-2 virus, was first discovered in Wuhan, Hubei Province, China in December 2019, causing a huge impact around the world. In the past three years, novel coronavirus has successively appeared many varieties through gene mutation, among which Omicron strain is the most popular variety. Studies have shown that the imbalance of microflora is closely related to the progress of novel coronavirus infection and the severity of clinical symptoms. In the previous study, we build database of oral-intestinal microbial metagenomics, immunome and metabolome of novel coronavirus Original Strain with mild, medium and severe clinical symptoms and healthy subjects, reveal the role and mechanism of the oral and intestinal microbiome in Original Strain infection and exacerbation, and build the model of susceptibility, severe warning and prognosis of Original Strain pneumonia which based on the microbiome-immunity-metabolism. Our research characterizes oropharyngeal microbiomes of Omicron variant cases and rehabilitation cases, successfully constructed and verified the non-invasive diagnostic model of Omicron variant. We also analyzed the characteristics of oropharyngeal microbiomes in two subgroups of Omicron disease group—severity of infection and vaccination times, and found that the change of oropharyngeal microbiomes may affect the severity of the disease and the efficacy of the vaccine. And we described the correlation between microbial OTUs and clinical indexes. At last, it was found that the infection of Omicron variant and the infection of original strain have different characteristics of oropharyngeal microbiomes. [ABSTRACT FROM AUTHOR]

SARS COV-2 belongs to the beta coronavirus genera, it is believed that bats as warm-blooded flying vertebrates are ideal hosts for coronavirus gene source. COVID-19 caused by novel coronavirus was originated from Wuhan city of Hubei Province in China in December 2019. The common symptoms comprise fever, cough, malaise, and shortness of breath. The incubation period is between 2 - 14 days. In this paper we have deliberated the structure of viruses; varying symptoms among COVID-19, SARS, MERS, and approach to tackling this problem with utmost effective Unani alternatives. Unani medicine endeavors to find the best conceivable ways by which a person can lead a hale and hearty life with minimum or zero sicknesses. Unani scholars believe by practicing important things like the use of fresh and clean water, breathing clean air and eating fresh food, upholding a balance between the mind and the body so that the metabolic processes can function effortlessly and the body wastes are evacuated, by enhancing immunity, and keeping an equilibrium between (Asbab-e-Sitta Zarooriya) six essential factors for life one can stay away from diseases. [ABSTRACT FROM AUTHOR]

SARS-CoV-2 has caused a worldwide pandemic that began with an outbreak of pneumonia cases in the Hubei province of China. Knowledge of those most at risk is integral for treatment, guideline implementation, and resource allocation. We conducted a systematic review and meta-analysis to evaluate comorbidities associated with severe and fatal cases of COVID-19. A search was conducted on PubMed and EmBase on 20 April 2020. Pooled estimates were collected using a random-effects model. Thirty-three studies were included in the systematic review and twenty-two in the meta-analysis. Of the total cases 40.80% (95%CI: 35.49%, 46.11%) had comorbidities, while fatal cases had 74.37% (95%CI: 55.78%, 86.97%). Hypertension was more prevalent in severe [47.65% (95%CI: 35.04%, 60.26%)] and fatal [47.90% (95%CI: 40.33%, 55.48%)] cases compared to total cases [14.34% (95%CI: 6.60%, 28.42%)]. Diabetes was more prevalent among fatal cases [24.89% (95%CI: 18.80%, 32.16%)] compared to total cases [9.65% (95%CI: 6.83%, 13.48%)]. Respiratory diseases had a higher prevalence in fatal cases [10.89% (95%CI: 7.57%, 15.43%)] in comparison to total cases [3.65% (95%CI: 2.16%, 6.1%)]. Studies assessing the mechanisms accounting for the associations between severe cases and hypertension, diabetes, and respiratory diseases are crucial in understanding this new disease, managing patients at risk, and developing policies and guidelines that will reduce future risk of severe COVID-19 disease. [ABSTRACT FROM AUTHOR]

In mid-December 2019, a novel atypical pneumonia broke out in Wuhan, Hubei Province, China and was caused by a newly identified coronavirus, initially termed 2019 Novel Coronavirus and subsequently severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of 19 May 2020, a total of 4,731,458 individuals were reported as infected with SARS-CoV-2 among 213 countries, areas or territories with recorded cases, and the overall case-fatality rate was 6.6% (316,169 deaths among 4,731,458 recorded cases), according to the World Health Organization. Studies have shown that SARS-CoV-2 is notably similar to (severe acute respiratory syndrome coronavirus) SARS-CoV that emerged in 2002-2003 and Middle East respiratory syndrome coronavirus (MERS-CoV) that spread during 2012, and these viruses all contributed to global pandemics. The ability of SARS-CoV-2 to rapidly spread a pneumonia-like disease from Hubei Province, China, throughout the world has provoked widespread concern. The main symptoms of coronavirus disease 2019 (COVID-19) include fever, cough, myalgia, fatigue and lower respiratory signs. At present, nucleic acid tests are widely recommended as the optimal method for detecting SARS-CoV-2. However, obstacles remain, including the global shortage of testing kits and the presentation of false negatives. Experts suggest that almost everyone in China is susceptible to SARS-CoV-2 infection, and to date, there are no effective treatments. In light of the references published, this review demonstrates the biological features, spread, diagnosis and treatment of SARS-CoV-2 as a whole and aims to analyse the similarities and differences among SARS-CoV-2, SARS-CoV and MERS-CoV to provide new ideas and suggestions for prevention, diagnosis and clinical treatment. [ABSTRACT FROM AUTHOR]

2019 Coronavirus disease (COVID-19) had a big impact in Italy, mainly concentrated in the northern part of the Country. All this was mainly due to similarities of this area with Wuhan in Hubei Province, according to geographical, environmental and socio-economic points of view. The basic hypothesis of this research was that the presence of atmospheric pollutants can generate stress on health conditions of the population and determine pre-conditions for the development of diseases of the respiratory system and complications related to them. In most cases the attention on environmental aspects is mainly concentrated on pollution, neglecting issues such as land management which, in some way, can contribute to reducing the impact of pollution. The reduction of land take and the decrease in the loss of ecosystem services can represent an important aspect in improving environmental quality. In order to integrate policies for environmental change and human health, the main factors analyzed in this paper can be summarized in environmental, climatic and land management. The main aim of this paper was to produce three different hazard scenarios respectively related to environmental, climatic and land management-related factors. A Spatial Analytical Hierarchy Process (AHP) method has been applied over thirteen informative layers grouped in aggregation classes of environmental, climatic and land management. The results of the health hazard maps show a disparity in the distribution of territorial responses to the pandemic in Italy. The environmental components play an extremely relevant role in the definition of the red zones of hazard, with a consequent urgent need to renew sustainable development strategies. The comparison of hazard maps related to different scenarios provides decision makers with tools to orient policy choices with a different degree of priority according to a place-based approach. In particular, the geospatial representation of risks could be a tool for legitimizing the measures chosen by decision-makers, proposing a renewed approach that highlights and takes account of the differences between the spatial contexts to be considered - Regions, Provinces, Municipalities - also in terms of climatic and environmental variables. • COVID-19 had a big impact in Italy, mainly in the northern part of the Country. • Reduction of land take and ecosystem services' loss improve environmental quality. • Environmental, climatic and land management can be leveraged for pandemic policies. • Geospatial representation of risks is a useful approach to support decision-makers. [ABSTRACT FROM AUTHOR]

Background: The novel coronavirus diseases 2019 (COVID-19) caused over 1.7 million confirmed cases and cumulative mortality up to over 110,000 deaths worldwide as of 14 April 2020. A total of 57 Macao citizens were obligated to stay in Hubei province, China, where the highest COVID-19 prevalence was noted in the country and a "lockdown" policy was implemented for outbreak control for more than one month. They were escorted from Wuhan City to Macao via a chartered airplane organized by Macao SAR government and received quarantine for 14 days with none of the individual being diagnosed with COVID-19 by serial RNA tests from the nasopharyngeal specimens and sera antibodies. It was crucial to identify common characteristics among these 57 uninfected individuals. Methods: A questionnaire survey was conducted to extract information such as behavior, change of habits and preventive measures. Results: A total of 42 effective questionnaires were analyzed after exclusion of 14 infants and children with age under fifteen as ineligible for the survey and missing of one questionnaire, with a response rate of 97.7% (42 out of 43). The proportion of female composed more than 70% of this group of returners. The main reason for visiting Hubei in 88.1% of respondents was to visit relatives. Over 88% of respondents did not participate in high-risk activities due to mobility restriction. All (100%) denied contact with suspected or confirmed COVID-19 cases. Comparison of personal hygiene habits before and during disease outbreak showed a significant increase in practice including wearing a mask when outdoor (16.7% and 95.2%, P < 0.001) and often wash hands with soap or liquid soap (85.7% and 100%, P = 0.031). [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic, which originated in Hubei, China, in December 2019, has had a profound impact on global public health. With the elucidation of the SARS-CoV-2 virus structure, genome type, and routes of infection, a variety of diagnostic methods have been developed for COVID-19 detection and surveillance. Although the pandemic has been declared over, we are still significantly affected by it in our daily lives in the post-pandemic era. Among the various diagnostic methods, nanomaterials, especially metallic nanomaterials, have shown great potential in the field of bioanalysis due to their unique physical and chemical properties. This review highlights the important role of metallic nanosensors in achieving accurate and efficient detection of COVID-19 during the pandemic outbreak and spread. The sensing mechanisms of each diagnostic device capable of analyzing a range of targets, including viral nucleic acids and various proteins, are described. Since SARS-CoV-2 is constantly mutating, strategies for dealing with new variants are also suggested. In addition, we discuss the analytical tools needed to detect SARS-CoV-2 variants in the current post-pandemic era, with a focus on achieving rapid and accurate detection. Finally, we address the challenges and future directions of metallic nanomaterial-based COVID-19 detection, which may inspire researchers to develop advanced biosensors for COVID-19 monitoring and rapid response to other virus-induced pandemics based on our current achievements. [ABSTRACT FROM AUTHOR]

The novel coronavirus (nCoV-2019) is responsible for the acute respiratory disease in humans known as COVID-19. This infection was found in the Wuhan and Hubei provinces of China in the month of December 2019, after which it spread all over the world. By March, 2020, this epidemic had spread to about 117 countries and its different variants continue to disturb human life all over the world, causing great damage to the economy. Through this paper, we have attempted to identify and predict the novel coronavirus from influenza-A viral cases and healthy patients without infection through applying deep learning technology over patient pulmonary computed tomography (CT) images, as well as by the model that has been evaluated. The CT image data used under this method has been collected from various radiopedia data from online sources with a total of 548 CT images, of which 232 are from 12 patients infected with COVID-19, 186 from 17 patients with influenza A virus, and 130 are from 15 healthy candidates without infection. From the results of examination of the reference data determined from the point of view of CT imaging cases in general, the accuracy of the proposed model is 79.39%. Thus, this deep learning model will help in establishing early screening of COVID-19 patients and thus prove to be an analytically robust method for clinical experts. [ABSTRACT FROM AUTHOR]

In 2020, when the novel coronavirus disease 2019 (COVID-19) broke out as a global pandemic, cities in Hubei Province first went into lockdown on 23 January and resumed work and production on 20 March. From February to March 2020, human activities in Hubei decreased significantly, with the average particulate matter smaller than 2.5 μm (PM2.5) concentration standing at 40 μg/m3, which is 21% lower than the expected based on a linear fitting trend in thePM2.5 concentration in Hubei. By using the empirical orthogonal function (EOF) method, this paper comparatively analyzes the spatial-temporal variations of Hubei's PM2.5 concentration anomaly in February and March 2020 and the same periods of 2016–2019. The results show that the daytime peak of the PM2.5 daily variation in Hubei in a low-emission scenario during COVID-19 declined significantly, to which human activities contributed the most. However, during nighttime, the PM2.5 peak became more prominent, and the meteorological conditions had a more noticeable effect on the PM2.5 concentration. In addition, during COVID-19, there was a great drop in PM2.5 pollution accumulated from local sources within the urban circle of Wuhan City, while an increase was seen in central-western Hubei due to the inter-regional pollutant transport. Thus, the high PM2.5 concentration center in the urban circle of Wuhan disappeared, but the pollution transport channel cities in central-western Hubei remained as high-PM2.5-concentration centers. [ABSTRACT FROM AUTHOR]

In late 2019, a new Coronavirus disease (COVID-19) appeared in Wuhan, Hubei Province, China. The virus began to spread throughout many countries, affecting a large population. Polymerase chain reaction is currently being utilized to diagnose COVID-19 in suspected patients; however, its sensitivity is quite low. The researchers also developed automated approaches for reliably and timely identifying COVID-19 from X-ray images. However, traditional machine learning-based image classification algorithms necessitate manual image segmentation and feature extraction, which is a time-consuming task. Due to promising results and robust performance, Convolutional Neural Network (CNN)-based techniques are being used widely to classify COVID-19 from Chest X-rays (CXR). This study explores CNN-based COVID-19 classification methods. A series of experiments aimed at COVID-19 detection and classification validates the viability of our proposed framework. Initially, the dataset is preprocessed and then fed into two Residual Network (ResNet) architectures for deep feature extraction, such as ResNet18 and ResNet50, whereas support vector machines with its multiple kernels, including Quadratic, Linear, Gaussian and Cubic, are used to classify these features. The experimental results suggest that the proposed framework efficiently detects COVID-19 from CXR images. The proposed framework obtained the best accuracy of 97.3% using ResNet50. [ABSTRACT FROM AUTHOR]

SARS‐CoV‐2 first raised from Wuhan City, Hubei Province in November 2019. The respiratory disorder, cough, weakness, fever are the main clinical symptoms of coronavirus disease 2019 (COVID‐19) patients. Natural Killer (NK) cells as a first defense barrier of innate immune system have an essential role in early defense against pulmonary virus. They kill the infected cells by inducing apoptosis or the degranulation of perforin and granzymes. Collectively, NK cells function are coordinated by the transmitted signals from activating and inhibitory receptors. It is clear that the cytotoxic function of NK cells is disrupted in COVID‐19 patients due to the dysregulation of activating and inhibitory receptors. Therefore, better understanding of the activating and inhibitory receptors mechanism could facilitate the treatment strategy in clinic. To improve the efficacy of immunotherapy in COVID‐19 patients, the functional detail of NK cell and manipulation of their key checkpoints are gathered in current review. Significance statement: Natural Killer (NK) cells as a major innate immunity compartment have a substantial role in the control of infection in coronavirus disease 2019 (COVID‐19) patients. Despite the important role of NK cells in viral diseases, the function of these cells is disrupted in COVID‐19 patients. Dysregulation of the activating and inhibitory receptors and cytokine storm in respiratory air‐way followed by accumulation of disarming NK cells, are major factors in disease severity in COVID‐19 patients. Therefore, it seems that the manipulating of immune checkpoints, the control of excessive secretion of cytokines (anticytokine therapy) and inhibitory receptors targeting by the monoclonal antibodies would be helpful to restore NK cell function [ABSTRACT FROM AUTHOR]

Corona virus disease 2019 started in December 2019 as an outbreak of unexplained pneumonias in Wuhan, a city in Hubei province of China. This illness emerged as an epidemic in China and later spread to almost all countries over the globe except Antarctica. This is caused by a beta Corona virus, which is genetically similar to SARS virus. The predominant mode of transmission is via droplet spread, when the infected person coughs, sneezes or talks the virus is released in the respiratory secretions. As there are only a few cases of COVID 19 in neonates, there is no convincing evidence to support the possibility of vertical transmission. Clinical presentation in neonates is nonspecific, commonly observed are temperature instability, respiratory distress, poor feeding, lethargy, vomiting and diarrhea. Laboratory examinations may be nonspecific. Definitive test for 2019-nCoV is the detection of viral nucleic acid by real-time fluorescence polymerase chain reaction (RT-PCR). Suspected and confirmed COVID positive mothers should be delivered in separate delivery rooms and operation theaters. Since there is no approved treatment or drug for this disease, prevention of infection and breaking the chain of transmission plays a crucial role. [ABSTRACT FROM AUTHOR]

The SARS-CoV-2 virus, commonly known as COVID-19, first occurred in December 2019 in Wuhan, Hubei Province, China. Since then, it has become a tremendous threat to human health. With a pandemic threat, it is in the significant interest of the scientific world to establish its method of infection. In this manuscript, we combine knowledge of the infection mechanism with theoretical methods to answer the question of the virus's selectivity. We proposed a two-stage infection mechanism. In the first step, the virus interacts with the ACE2 receptor, with the "proper strength". When the interaction is too strong, the virus will remain in an "improper position"; if the interaction is too weak, the virus will "run away" from the cell. We also indicated three residues (positions 30, 31, and 353) located on the ACE2 protein-binding interface, which seems to be crucial for successful infection. Our results indicate that these residues are necessary for the initiation of the infection process. [ABSTRACT FROM AUTHOR]

COVID-19, the disease caused by the novel coronavirus 2019, has caused grave woes across the globe since it was first reported in the epicentre of Wuhan, Hubei, China, in December 2019. The spread of COVID-19 in China has been successfully curtailed by massive travel restrictions that rendered more than 900 million people housebound for more than two months since the lockdown of Wuhan, and elsewhere, on 23 January 2020. Here, we assess the impact of China's massive lockdowns and travel restrictions reflected by the changes in mobility patterns across and within provinces, before and during the lockdown period. We calibrate movement flow between provinces with an epidemiological compartment model to quantify the effectiveness of lockdowns and reductions in disease transmission. Our analysis demonstrates that the onset and phase of local community transmission in other provinces depends on the cumulative population outflow received from the epicentre Hubei. Moreover, we show that synchronous lockdowns and consequent reduced mobility lag a certain time to elicit an actual impact on suppressing the spread. Such highly coordinated nationwide lockdowns, applied via a top-down approach along with high levels of compliance from the bottom up, are central to mitigating and controlling early-stage outbreaks and averting a massive health crisis. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new beta coronavirus that emerged at the end of 2019 in the Hubei province of China. SARS-CoV-2 causes coronavirus disease 2019 (COVID-19) and was declared a pandemic by the World Health Organization (WHO) on 11 March 2020. Herd or community immunity has been proposed as a strategy to protect the vulnerable, and can be established through immunity from past infection or vaccination. Whether SARSCoV-2 infection results in the development of a reservoir of resilient memory cells is under investigation. Vaccines have been developed at an unprecedented rate and 7 408 870 760 vaccine doses have been administered worldwide. Recently emerged SARS-CoV-2 variants are more transmissible with a reduced sensitivity to immune mechanisms. This is due to the presence of amino acid substitutions in the spike protein, which confer a selective advantage. The emergence of variants therefore poses a risk for vaccine effectiveness and long-term immunity, and it is crucial therefore to determine the effectiveness of vaccines against currently circulating variants. Here we review both SARS-CoV-2-induced host immune activation and vaccine-induced immune responses, highlighting the responses of immune memory cells that are key indicators of host immunity. We further discuss how variants emerge and the currently circulating variants of concern (VOC), with particular focus on implications for vaccine effectiveness. Finally, we describe new antibody treatments and future vaccine approaches that will be important as we navigate through the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

A novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in the sprawling capital of Central China's Hubei province Wuhan in 2019. Since late 2019, SARS-CoV-2 has been responsible for a global pandemic that has resulted in 405,961,201 confirmed cases of infection and 5,789,567 deaths as of February 11, 2022, according to the World Health Organization (WHO). Here we report the development of an inactivated SARS-CoV-2 vaccine candidate and show that its efficacy and safety in preclinical studies warrant further clinical evaluation. Efficiency and possible side effects of vaccine candidate on different doses were evaluated using histopathology, K18 hACE2 transgenic mice challenge, T-cell response, and serum neutralization test in Balb/c mice. All pilot production and propagation experiments of SARS-CoV-2 were performed in the Level 3 Negative Pressure Biosafety Facility (BSL3/ABSL-3) according to COVID-19 interim guidance of WHO. All analytical studies have shown that the quality control parameters of the vaccine candidate meet the requirements for inactivated viral vaccines according to European Pharmacopeia. The vaccine candidate (KOCAK-19) has been authorized by the Turkish Medicines and Medical Devices Agency and has now entered phase 2 clinical development (NCT04838080). [ABSTRACT FROM AUTHOR]

SARS-CoV-2, a novel strain of coronavirus' also called coronavirus disease 19 (COVID-19), a highly contagious pathogenic respiratory viral infection emerged in December 2019 in Wuhan, a city in China's Hubei province without an obvious cause. Very rapidly it spread across the globe (over 200 countries and territories) and finally on 11 March 2020 World Health Organisation characterized it as a "pandemic". Although it has low mortality of around 3% as of 18 May 2021 it has already infected 164,316,270 humans with 3,406,027 unfortunate deaths. Undoubtedly the world was rocked by the COVID-19 pandemic, but researchers rose to all manner of challenges to tackle this pandemic by adopting the shreds of evidence of ML and AI in previous epidemics to develop novel models, methods, and strategies. We aim to provide a deeper insight into the convolutional neural network which is the most notable and extensively adopted technique on radiographic visual imagery to help expert medical practitioners and researchers to design and finetune their state-of-the-art models for their applicability in the arena of COVID-19. In this study, a deep convolutional neural network, its layers, activation and loss functions, regularization techniques, tools, methods, variants, and recent developments were explored to find its applications for COVID-19 prognosis. The pipeline of a general architecture for COVID-19 prognosis has also been proposed. This paper highlights recent studies of deep CNN and its applications for better prognosis, detection, classification, and screening of COVID-19 to help researchers and expert medical community in multiple directions. It also addresses a few challenges, limitations, and outlooks while using such methods for COVID-19 prognosis. The recent and ongoing developments in AI, MI, and deep learning (Deep CNN) has shown promising results and significantly improved performance metrics for screening, prediction, detection, classification, forecasting, medication, treatment, contact tracing, etc. to curtail the manual intervention in medical practice. However, the research community of medical experts is yet to recognize and label the benchmark of the deep learning framework for effective detection of COVID-19 positive cases from radiology imagery. [ABSTRACT FROM AUTHOR]

Background: The novel coronavirus (COVID-19) epidemic initially appeared in Wuhan, Hubei Province, China, on 31 December 2019 and was spread rapidly worldwide. Most underlying diseases reported with COVID-19 patients are diabetes, hypertension, coronary heart diseases, and cerebrovascular disease. We do not know whether individuals with thyroid disease are at increased risk of COVID-19 infection. Methods: Two experienced researchers will conduct an electronic search of the databases including PubMed/MEDLINE, the Cochrane Reviews, and the Cochrane Central Register of Controlled Trials (CENTRAL), Web of Science, Scopus, and ProQuest, for articles published since October 2019. Clinical trials and observational studies will be included. Studies will be screened after de-duplication. A standardized data extraction form will be developed through discussions with the review team and will be revised after piloting. An appropriate risk of bias assessment tool will be used to assess the quality of studies. Two independent reviewers will assess the eligibility, extraction of detailed information, and quality assessment of studies. The results will be pooled for meta-analysis, subgroup analysis and/or descriptive analysis based on the included data conditions. Conclusion: Results of this study will provide current evidence on the association of COVID-19 diseases with any thyroid disorders such as hypothyroidism, thyrotoxicosis, and thyroid cancer with or without radioiodine therapy. Findings will be disseminated in peer-reviewed publications and conference presentations. Trial registration: PROSPERO registration number: CRD42020184289. https://www.crd.york.ac.uk/PROSPERO/#recordDetails [ABSTRACT FROM AUTHOR]

Since the novel coronavirus (COVID-19) outbreak in China, and due to the open accessibility of COVID-19 data, several researchers and modellers revisited the classical epidemiological models to evaluate their practical applicability. While mathematical compartmental models can predict various contagious viruses' dynamics, their efficiency depends on the model parameters. Recently, several parameter estimation methods have been proposed for different models. In this study, we evaluated the Ensemble Kalman filter's performance (EnKF) in the estimation of time-varying model parameters with synthetic data and the real COVID-19 data of Hubei province, China. Contrary to the previous works, in the current study, the effect of damping factors on an augmented EnKF is studied. An augmented EnKF algorithm is provided, and we present how the filter performs in estimating models using uncertain observational (reported) data. Results obtained confirm that the augumented-EnKF approach can provide reliable model parameter estimates. Additionally, there was a good fit of profiles between model simulation and the reported COVID-19 data confirming the possibility of using the augmented-EnKF approach for reliable model parameter estimation. [ABSTRACT FROM AUTHOR]

The World Health Organization (WHO) recognized a novel coronavirus as the causative agent of a new form of pneumonia. It was subsequently named COVID-19 and reported as the source of a respiratory disease occurrence starting in December 2019 in Wuhan, Hubei Province, China. It has been affirmed a public health emergency of international significance by the World Health Organization. It is regarded as a subset of the severe acute respiratory syndrome (SARS) and the Middle East respiratory syndrome (MERS); COVID-19 is triggered by a betacoronavirus called SARS-CoV-2, which affects the lower respiratory tract and occurs in humans as pneumonia. A variety of drugs, such as remdesivir and favipiravir, are currently undergoing clinical trials to evaluate for the management of COVID-19. The effect of the pandemic as well as the epidemic that follows through the life cycles of various recycled plastic is evaluated, particularly those required for personal safety and health care. In response to the growth in COVID-19 cases worldwide, the energy and environmental impacts of these lifecycle management have risen rapidly. However, significant hazardous waste management concerns arise due to the need to assure the elimination of residual pathogens in household and medical wastes. This review article summarizes the preventive and environmental management of COVID-19. [ABSTRACT FROM AUTHOR]

The World Health Organization (WHO) on December 31, 2019, was informed of several cases of respiratory diseases of unknown origin in the city of Wuhan in the Chinese Province of Hubei, the clinical manifestations of which were similar to those of viral pneumonia and manifested as fever, cough, and shortness of breath. And, the disease caused by the virus is named the new coronavirus disease 2019 and it will be abbreviated as 2019-nCoV and COVID-19. As of January 30, 2020, the WHO classified this epidemic as a global health emergency (Chung et al. in Radiology 295(1):202–207, 2020). It is an international real-life problem. Due to deaths, globally everyone is under fear. Now, it is the responsibility of researchers to give hope to the people. In this article, we aim to better protect people and general pandemic preparedness by predicting the lifetime of the disease-causing virus using three mathematical models. This article deals with a complex real-life problem people face all over the world, an international real-life problem. The main focus is on the USA due to large infection and death due to coronavirus and thereby the life of every individual is uncertain. The death counts of the USA from February 29 to April 22, 2020, are used in this article as a data set. The death counts of the USA are fitted by the solutions of three mathematical models and a solution to an international problem is achieved. Based on the death rate, the lifetime of the coronavirus COVID-19 is predicted as 1464.76 days from February 29, 2020. That is, after March 2024 there will be no death in the USA due to COVID-19 if everyone follows the guidelines of WHO and the advice of healthcare workers. People and government can get prepared for this situation and many lives can be saved. It is the contribution of soft computing. Finally, this article suggests several steps to control the spread and severity of the disease. The research work, the lifetime prediction presented in this article is entirely new and differs from all other articles in the literature. [ABSTRACT FROM AUTHOR]

• Four phases can be distinguished in the spread of the virus. • The relative daily change indicator best visualizes the changes in the epidemic. • We present the changes in the epidemic phases for a number of selected countries. • The re-outbreak phase is probably the expected second wave of the epidemic. • An updated, well-commented Matlab file is available in the open GitHub repository. This paper presents models of the spread of SARS-CoV-2 coronavirus in individual countries and globally in 2020 based on the statistical characteristics of the spread in the given countries or regions (in particular, in Hubei province). Through modeling, we attempt to achieve a goal which is of vital interest to societies in a pandemic catastrophe, and to answer the question of what stage of spread the epidemic has reached in a given country. The country classifier we developed is based on the relative variability indicator of the confirmed cases variable. This classification indicator is compared with a set of data-driven thresholds, the crossing of which determines the degree of spread of the epidemic in a given country. The article was written between April 2020, when the pandemic had been suppressed in China and was raging in Europe and the USA, and August 2020, as a new wave of local resumed outbreaks appeared in many countries. We contend that the spread phases are predictable based on statistical similarity. There are four phases of epidemic spread: growth, duration, suppression and re-outbreak. The authors' Matlab software, which allows simulations of the spread of coronavirus in any country based on data published by CSSE, is available in the public GitHub repository. [ABSTRACT FROM AUTHOR]

At the end of 2019, a new coronavirus (COVID-19) epidemic has triggered global public health concern. Here, a model integrating the daily intercity migration network, which constructed from real-world migration records and the Susceptible–Exposed–Infected–Removed model, is utilized to predict the epidemic spreading of the COVID-19 in more than 300 cities in China. However, the model has more than 1800 unknown parameters, which is a challenging task to estimate all unknown parameters from historical data within a reasonable computation time. In this article, we proposed a pseudocoevolutionary simulated annealing (SA) algorithm for identifying these unknown parameters. The large volume of unknown parameters of this model is optimized through three procedures co-adapted SA-based optimization processes, respectively. Our results confirm that the proposed method is both efficient and robust. Then, we use the identified model to predict the trends of the epidemic spreading of the COVID-19 in these cities. We find that the number of infections in most cities in China has reached their peak from February 29, 2020, to March 15, 2020. For most cities outside Hubei province, the total number of infected individuals would be less than 100, while for most cities in Hubei province (exclude Wuhan), the total number of infected individuals would be less than 3000. [ABSTRACT FROM AUTHOR]

A sequence of pneumonia-causing diseases from the Novel coronavirus (COVID-19) appeared in Wuhan, Hubei, China in December 2019. The outbreak of COVID-19 spread quite rapidly. Just as we write this report, almost two and a half million verified cases were reported globally, and almost 180,000 people died.1 Experience from China reveals that COVID-19 outbreaks can be brought under control within 3 months, with highly efficient touch tracking and case isolation.2 Healthcare staff are at the forefront of treatment for COVID-19 cases and have a very large chance of exposure to the infection.3 Cuts in disposable gear and COVID-19 awareness are triggering infections in healthcare workers. 4 As of February 11, 2020, China has contaminated more than 1,700 health-care workers. In Italy, 2026 (9 percent) of the COVID-19 incidents happened in healthcare professionals (as of March 15). 5 But, 31 medical teams containing more than 42,000 nurses and doctors sent by the Hubei from other provinces did not have a reported infection. It indicates that sufficient information about the transmission of disease and the use of protective equipment and procedures to manage infections is necessary to avoid the spread of infection among health care staff. [ABSTRACT FROM AUTHOR]

It is essential to understand where and how severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is transmitted. Case reports were extracted from the local Municipal Health Commissions of 320 prefectural municipalities in China (not including Hubei Province). We identified all outbreaks involving three or more cases and reviewed the major characteristics of the enclosed spaces in which the outbreaks were reported and their associated indoor environmental aspects. Three hundred and eighteen outbreaks with three or more cases were identified, comprising a total of 1245 confirmed cases in 120 prefectural cities. Among the identified outbreaks, 53.8% involved three cases, 26.4% involved four cases, and only 1.6% involved ten or more cases. Home‐based outbreaks were the dominant category (254 of 318 outbreaks; 79.9%), followed by transport‐based outbreaks (108; 34.0%), and many outbreaks occurred in more than one category of venue. All identified outbreaks of three or more cases occurred in indoor environments, which confirm that sharing indoor spaces with one or more infected persons is a major SARS‐CoV‐2 infection risk. [ABSTRACT FROM AUTHOR]

A novel coronavirus member was reported in Wuhan City, Hubei Province, China, at the end of the year 2019. Initially, the infection spread locally, affecting the Wuhan people, and then expanded rapidly throughout the world. On 11 March 2020, the World Health Organization (WHO) proclaimed it a global pandemic. The virus is a new strain most closely related to a bat coronavirus (RaTG13) which was not previously discovered in humans and is now formally known as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Coronavirus disease 2019 (COVID-19) is the disease syndrome that the SARS-CoV-2 virus triggers. It is suggested that SARS-CoV-2 can be transmitted through aerosols, direct/indirect contact, and also during medical procedures and specimen handling. The infection is characterized by isolated flu-like symptoms, but there may be specific signs of fever, fatigue, cough, and shortness of breath, as well as the loss of smell and breathing difficulty. Within this report, we tried to review the most current scientific literature published by January 2021 on various aspects of the outbreak, including virus structure, pathogenesis, clinical presentation, epidemiology, diagnostic approaches, potential therapeutics and vaccines, and prospects. We hope this article makes a beneficial impact on public education to better deal with the SARS-CoV-2 crisis and push a step forward in the near term towards its prevention and control. [ABSTRACT FROM AUTHOR]

An unusual episode of pneumonia happened in Hubei Province, Wuhan City(China) in December 2019.A novel coronavirus was recognized as the causative agent. The situation was declared a pandemic by WHO (World Health Organisation) and the disease was named as COVID-19. The outbreak progresses faster than SARS and MERS coronaviruses but has less mortality than the two precursors. COVID-19 cases continue to intensify, despite meticulous global containment and quarantine efforts. Gathered from the evidences currently published, this review deliberately summarizes the findings of COVID-19 disease transmission, pathogenesis, manifestations, analysis, treatment, and counteraction. [ABSTRACT FROM AUTHOR]

The novel coronavirus responsible for COVID-19 was first identified in Hubei Province, China in December, 2019. Within a matter of months the virus had spread and become a global pandemic. In addition to international air travel, local travel (e.g. by passenger car) contributes to the geographic spread of COVID-19. We modify the common susceptible-exposed-infectious-removed (SEIR) virus spread model and investigate the extent to which short-term travel associated with driving influences the spread of the virus. We consider the case study of the US state of Minnesota, and calibrated the proposed model with travel and viral spread data. Using our modified SEIR model that considers local short-term travel, we are able to better explain the virus spread than using the long-term travel SEIR model. Short-term travel associated with driving is predicted to be a significant contributor to the historical and future spread of COVID-19. The calibrated model also predicts the proportion of infections that were detected. We find that if driving trips remain at current levels, a substantial increase in COVID-19 cases may be observed in Minnesota, while decreasing intrastate travel could help contain the virus spread. [ABSTRACT FROM AUTHOR]

Background: Few specific medications have been proven effective for the treatment of patients with severe coronavirus disease 2019 (COVID-19). Here, we tested whether high-dose vitamin C infusion was effective for severe COVID-19. Methods: This randomized, controlled, clinical trial was performed at 3 hospitals in Hubei, China. Patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the ICU were randomly assigned in as 1:1 ratio to either the high-dose intravenous vitamin C (HDIVC) or the placebo. HDIVC group received 12 g of vitamin C/50 ml every 12 h for 7 days at a rate of 12 ml/hour, and the placebo group received bacteriostatic water for injection in the same way within 48 h of arrival to ICU. The primary outcome was invasive mechanical ventilation-free days in 28 days (IMVFD28). Secondary outcomes were 28-day mortality, organ failure (Sequential Organ Failure Assessment (SOFA) score), and inflammation progression (interleukin-6). Results: Only 56 critical COVID-19 patients were ultimately recruited due to the early control of the outbreak. There was no difference in IMVFD28 between two groups (26.0 [9.0–28.0] in HDIVC vs 22.0 [8.50–28.0] in control, p = 0.57). HDIVC failed to reduce 28-day mortality (P = 0.27). During the 7-day treatment period, patients in the HDIVC group had a steady rise in the PaO2/FiO2 (day 7: 229 vs. 151 mmHg, 95% CI 33 to 122, P = 0.01), which was not observed in the control group. IL-6 in the HDIVC group was lower than that in the control group (19.42 vs. 158.00; 95% CI -301.72 to -29.79; P = 0.04) on day 7. Conclusion: This pilot trial showed that HDIVC failed to improve IMVFD28, but might show a potential signal of benefit in oxygenation for critically ill patients with COVID-19 improving PaO2/FiO2 even though. [ABSTRACT FROM AUTHOR]

Purpose: Coronavirus disease 2019 (COVID-19) is an emerging disease that was first reported in Wuhan city, the capital of Hubei province in China, and has subsequently spread worldwide. Risk factors for mortality have not been well summarized. Current meta-analysis of retrospective cohort studies was done to summarize available findings on the association between age, gender, comorbidities and risk of death from COVID-19 infection.Methods: Online databases including Web of Science, PubMed, Scopus, Cochrane Library and Google scholar were searched to detect relevant publications up to 1 May 2020, using relevant keywords. To pool data, random-effects model was used. Furthermore, sensitivity analysis and publication bias test were also done.Results: In total, 14 studies with 29,909 COVID-19 infected patients and 1445 cases of death were included in the current meta-analysis. Significant associations were found between older age (≥65 vs <65 years old) (pooled ORs = 4.59, 95%CIs = 2.61-8.04, p < .001), gender (male vs female) (pooled ORs = 1.50, 95%CIs = 1.06-2.12, p = .021) and risk of death from COVID-19 infection. In addition, hypertension (pooled ORs = 2.70, 95%CIs = 1.40-5.24, p = .003), cardiovascular diseases (CVDs) (pooled ORs = 3.72, 95%CIs = 1.77-7.83, p = .001), diabetes (pooled ORs = 2.41, 95%CIs = 1.05-5.51, p = .037), chronic obstructive pulmonary disease (COPD) (pooled ORs = 3.53, 95%CIs = 1.79-6.96, p < .001) and cancer (pooled ORs = 3.04, 95%CIs = 1.80-5.14, p < .001), were associated with higher risk of mortality.Conclusions: Older age (≥65 years old), male gender, hypertension, CVDs, diabetes, COPD and malignancies were associated with greater risk of death from COVID-19 infection. These findings could help clinicians to identify patients with poor prognosis at an early stage. [ABSTRACT FROM AUTHOR]