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What started as a cluster of patients with a mysterious respiratory illness in Wuhan, China, in December 2019, was later determined to be coronavirus disease 2019 (COVID-19). The pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel Betacoronavirus, was subsequently isolated as the causative agent. SARS-CoV-2 is transmitted by respiratory droplets and fomites and presents clinically with fever, fatigue, myalgias, conjunctivitis, anosmia, dysgeusia, sore throat, nasal congestion, cough, dyspnea, nausea, vomiting, and/or diarrhea. In most critical cases, symptoms can escalate into acute respiratory distress syndrome accompanied by a runaway inflammatory cytokine response and multiorgan failure. As of this article's publication date, COVID-19 has spread to approximately 200 countries and territories, with over 4.3 million infections and more than 290,000 deaths as it has escalated into a global pandemic. Public health concerns mount as the situation evolves with an increasing number of infection hotspots around the globe. New information about the virus is emerging just as rapidly. This has led to the prompt development of clinical patient risk stratification tools to aid in determining the need for testing, isolation, monitoring, ventilator support, and disposition. COVID-19 spread is rapid, including imported cases in travelers, cases among close contacts of known infected individuals, and community-acquired cases without a readily identifiable source of infection. Critical shortages of personal protective equipment and ventilators are compounding the stress on overburdened healthcare systems. The continued challenges of social distancing, containment, isolation, and surge capacity in already stressed hospitals, clinics, and emergency departments have led to a swell in technologically-assisted care delivery strategies, such as telemedicine and web-based triage. As the race to develop an effective vaccine intensifies, several clinical trials of antivirals and immune modulators are underway, though no reliable COVID-19-specific therapeutics (inclusive of some potentially effective single and multi-drug regimens) have been identified as of yet. With many nations and regions declaring a state of emergency, unprecedented quarantine, social distancing, and border closing efforts are underway. Implementation of social and physical isolation measures has caused sudden and profound economic hardship, with marked decreases in global trade and local small business activity alike, and full ramifications likely yet to be felt. Current state-of-science, mitigation strategies, possible therapies, ethical considerations for healthcare workers and policymakers, as well as lessons learned for this evolving global threat and the eventual return to a "new normal" are discussed in this article. [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]

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]

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]

The world is urgently looking for ways to flatten the coronavirus disease 2019 (COVID-19) curve, and many governments have resorted to implementing strict lockdowns, as researchers show the effectiveness of China's approaches in containing the virus. However, this paper argues that the draconian lockdowns instituted in Wuhan, Hubei, China, may have actually contributed to intensifying patient surges and incapacitating local health systems. Medical aids were rushed to Hubei and new hospitals were rapidly built, however, the healthcare system was still unable to match the staggering increase of patients in the early stages of the lockdowns. The paper proposes using patient evacuation to enhance sustainable COVID-19 mitigation during lockdowns. It demonstrates that patients in Hubei could have been transported to other Chinese provinces where hospitals were under-utilized. This could have theoretically saved thousands of lives by reducing inequities between Hubei and the rest of China in healthcare capacity for treating COVID-19 patients. [ABSTRACT FROM AUTHOR]

Coronavirus outbreak in Wuhan, China, turned into a pandemic in record time. Communication of disease presentation and mechanism of spread remain keys to getting ahead of the virus and limiting its spread beyond the capacity of management. Owing to huge academic focus and pandemic concern around the globe, this bibliometric analysis investigated research productivity related to coronavirus disease (COVID-19) pandemic using the Web of Science database. The relevant data were harvested, and search query was further refined by publication years (2020 OR 2019) and document types (article, book chapter, and proceedings paper). Finally, 6694 records were imported and downloaded in Plaintext and BibTeX formats on August 1, 2020. The data analysis was performed using MS Excel, VOS viewer, and Biblioshiny software. Of the 6694 publications that appeared in that period, the USA and Chinese research institutions topped the numbers. At the same time, the Journal of Medical Virology and CUREUS (Cureus Journal of Medical Science), remained favorite journals for publications. The pattern of multi-author publications has outstripped that of single-authors. Apart from COVID-19 and the novel coronavirus, the important keywords mentioned included pandemic, pneumonia, epidemiology, public health, outbreak, epidemic, China, infection, and treatment. The analysis shows a strong local research response from China, with large teams reporting on the disease outbreak. Subsequent studies will document a global response as the virus spreads worldwide. The initial research related to the current coronavirus outbreak was reported from within China. The data and patterns were supposed to alter as the virus spread globally. [ABSTRACT FROM AUTHOR]

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

A severe pneumonia of an unknown origin was reported in Wuhan, China in December 2019. The disease, now known as coronavirus disease 2019 (COVID-19), has evolved into a public health emergency of international concern and wreaked worldwide havoc. An unprecedented and vigorous scientific response has allowed the accelerated discovery of the virus and reliable diagnostic methods; a rapid characterization of the disease and its impacts so as to better apply precautionary and public health measures; and resulted in remarkable progress in the development of mitigation strategies, including the development of vaccines at breakneck speed. This paper provides a health perspective of the virus and the pandemic it caused, based on available best evidence. Controversies surrounding the origin of the virus, its incubation period and infectivity, presentation and course of the disease, testing, as well as treatments and vaccinations are highlighted. The pandemic response, including infection control measures, and considerations on mental and economic health, alongside physical health is discussed. Moving forward, it is important that the global community is aware and better informed. More resources are needed to strengthen public health systems and healthcare infrastructure and delivery. This virus has the potential to persist and become endemic and seasonal in communities. Thus, non-pharmaceutical interventions (e.g. wearing masks, frequent hand washing, etc.) might become the new normal in a post-pandemic world. The silver lining in the COVID-19 cloud may be the lessons it provides, so that we may be better prepared to respond to an inevitable next pandemic. [ABSTRACT FROM AUTHOR]

The COVID-19 is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARSCOV-2). It has been identified as the causative agent of the viral pneumonia outbreak in Wuhan, China, at the end of 2019. It has a high human to human transmission capability and primarily targets the human respiratory system. Healthcare providers to common people are in high risks of the contamination of this fatal disease. It spreads person to person through respiratory droplets that produced during talking, coughing and sneezing. It is associated with severe and fatal respiratory disease in humans. At present it becomes great global public health concern. On 11 March 2020, the WHO declared the global COVID-19 outbreak as a pandemic. The COVID-19 pandemic has entered in a very dangerous phase in April and May 2020, and the severity is increasing day by day. On 26 July 2020, about 216 countries and territories are infected worldwide; total global infections become more than 16.3 million and total recovered about 9.9 million with total deaths 648,477. The disease have not abolished even in the 2021. On 27 March 2021, the disease spread up to 219 countries and territories globally; total confirmed deaths become 2,783,591, total confirmed cases 127,025,229, with total recovery 102,370,710. The paper discusses the social, economic, and health impacts in the world's poorest countries due to COVID-19 pandemic outbreak. The study also presents the current economic situation of the world and analyses the potential consequences on global economy. An attempt has been taken here to create consciousness among the common people to reduce the fatality of this killer disease. From the beginning of 2021, COVID-19 vaccines are distributed worldwide and all nations are united to abolish the virus completely. [ABSTRACT FROM AUTHOR]

The outbreak of the novel coronavirus disease (COVID-19), caused by the SARS-CoV-2 virus, occurred in Wuhan, China in December 2019 and quickly spread to both the developed and developing countries. The virus will continue to infect humans and animals symptomatically or asymptomatically, and humans will be expected to coexist with SARS-CoV-2 by preventing infections and its spread. In this paper, we report on the occurrence of the COVID-19 epidemic in Japan, including the city of Kobe. [ABSTRACT FROM AUTHOR]

The eruption of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (corona virus disease, COVID-19) in Wuhan, China, and its global spread has led to an exponentially growing number of infected patients, currently exceeding over 6.6 million and over 390,000 deaths as of the 5th of June 2020. In this pandemic situation, health systems have been put under stress, and the demand for personal protective equipment (PPE) exceeded the delivery capabilities of suppliers. To address this issue, 3D printing was identified as a possible solution to quickly produce PPE items such as face shields, mask straps, masks, valves, and ear savers. Around the world, companies, universities, research institutions, and private individuals/hobbyists stepped into the void, using their 3D printers to support hospitals, doctors, nursing homes, and even refugee camps by providing them with PPE. In Germany, the makervsvirus movement took up the challenge and connected thousands of end users, makers, companies, and logistic providers for the production and supply of face shields, protective masks, and ear savers. The Karlsruhe Institute of Technology (KIT) also joined the makervsvirus movement and used its facilities to print headbands for face shield assemblies and ear savers. Within this paper, the challenges and lessons learned from the quick ramp up of a research laboratory to a production site for medium-sized batches of PPE, the limitations in material supply, selection criteria for suitable models, quality measures, and future prospects are reported and conclusions drawn. [ABSTRACT FROM AUTHOR]

Coronavirus Disease (COVID-19) is a new disease that emerged in Wuhan, China which spreads through close contact of people, often by small droplets produced during coughing or sneezing. Detail mechanism by which it spreads between people are under investigation. The World Health Organization (WHO) declared this disease as a pandemic after the severity of the disease increased. Many scientific reports gathered have suggested many drugs that could be potential candidates for the treatment. Although, clinical effectiveness has not been fully evaluated. In this review, we have aggregated the data from few research articles, official news websites and few review papers regarding its phylogenetic relation, genomic constitution, transmission, replication and in-silico analysis done by researchers for few potent drugs that are currently used to cure COVID-19. SARS-CoV-2 belongs to Betacoronavirus genus with Genome structure consists 14 Open Reading Frames (ORFs) that encode 27 proteins. Coronavirus replicates into the host cells having unique mechanisms like ribosome frame-shifting and synthesis of genomic and sub genomic RNAs. In-silico methods have the advantage that they can make fast predictions for a large set of compounds in a high-throughput mode and also make their prediction based on the structure of a compound even before it has been synthesized. In-silico softwares have been used to find or to improve a novel bioactive compound, which may exhibit a strong affinity to a particular target in the drug development process. [ABSTRACT FROM AUTHOR]

Coronavirus 2019 (COVID‐19) captured worldwide attention as the virus spread from initial detection as a pneumonia of unknown cause in Wuhan, China in December 2019 to the declaration of a pandemic by the WHO only 3 months later. This paper describes the impact of COVID‐19 on cardiac surgery services in the greater Los Angeles area. Discussion includes public health response to the pandemic, how local hospitals reacted to the expected surge in patients with COVID, the establishment of telemedicine services, and the projected future impact on California healthcare as a result of the COVID‐19 pandemic. [ABSTRACT FROM AUTHOR]

Simple Summary: The COVID-19 pandemic showcases the impact of mitigation and elimination strategies across the globe, including the development of effective vaccines, antiviral drugs and diagnostic tools. However, the virus changes rapidly over time. Consequently, control strategies have been limited by time-consuming experimental acquisition of three-dimensional atomic protein structures of the fast-developing mutant 'variants' of the virus, which remains an unviable strategy for fast and effective disease control. Here, we use AlphaFold2 to model the atomic structure of the ever-changing SARS-CoV-2 spike protein in silico. AlphaFold2 is an artificial intelligence (AI) deep learning computational tool capable of producing models at experimental resolution in only a few hours. Structural models for major Variants of Concern (Alpha, Delta, and Omicron) and latitude-delimited haplotypes, sets of genetically linked and highly prevalent mutations that impact the epidemic calendar of the virus, were compared to the structure of the reference Wuhan strain. We find that patterns of structural change triggered by seasonal haplotype diversification could help predict the changing face of the virus, understand seasonal behavior, and develop more resilient vaccines and drugs. The slow experimental acquisition of high-quality atomic structures of the rapidly changing proteins of the COVID-19 virus challenges vaccine and therapeutic drug development efforts. Fortunately, deep learning tools such as AlphaFold2 can quickly generate reliable models of atomic structure at experimental resolution. Current modeling studies have focused solely on definitions of mutant constellations of Variants of Concern (VOCs), leaving out the impact of haplotypes on protein structure. Here, we conduct a thorough comparative structural analysis of S-proteins belonging to major VOCs and corresponding latitude-delimited haplotypes that affect viral seasonal behavior. Our approach identified molecular regions of importance as well as patterns of structural recruitment. The S1 subunit hosted the majority of structural changes, especially those involving the N-terminal domain (NTD) and the receptor-binding domain (RBD). In particular, structural changes in the NTD were much greater than just translations in three-dimensional space, altering the sub-structures to greater extents. We also revealed a notable pattern of structural recruitment with the early VOCs Alpha and Delta behaving antagonistically by suppressing regions of structural change introduced by their corresponding haplotypes, and the current VOC Omicron behaving synergistically by amplifying or collecting structural change. Remarkably, haplotypes altering the galectin-like structure of the NTD were major contributors to seasonal behavior, supporting its putative environmental-sensing role. Our results provide an extensive view of the evolutionary landscape of the S-protein across the COVID-19 pandemic. This view will help predict important regions of structural change in future variants and haplotypes for more efficient vaccine and drug development. [ABSTRACT FROM AUTHOR]

The rapid spread of the virus named Covid-19, which emerged in Wuhan (China) at the end of 2019, caused a global epidemic all over the world. On March 16, 2020, a state of emergency was declared in Kazakhstan and measures were taken in Kazakhstan like the rest of the world. In the meantime, intangible cultural heritage such as aitys, which is a contest on improvised oral poetry, got a great attention in Kazakh society. This article presents the results of an analysis on organizing aitys contests and the poems on COVID-19. The main cause of pandemic is that it changed conventional improvised poetry contest into virtual environment. As a result of the study, it was revealed that four types of aitys contests were held since the state of emergency was declared in Kazakhstan, where three of them were oral and one in written form. The first type of oral aitys contest took place online through video chatting service between two contestants from different places. The second type of the contest was offline at the same place and their contest was streamed via social media. These two contests are streamed on Skype, Instagram, Facebook and the records of the contest are posted on Youtube platform. The third is a radio-talk contest. In an aitys, a man can only listen to his voice to see what he sees in person. Finally, Instagram can be mentioned on paper, as well as on visual and print media such as Facebook. The article states that in the period from March to September 2020, more than one hundred aitys was conducted with the participation of more than one hundred minstrels. Covid-19-themed calls and responses of minstrels were evaluated around Bascom's "Four Functions of Folklore" and Performance Theory. The poems during the aitys proves that the Kazakh people during the outbreak try to safeguard the world's vision and traditions, as well as important advice for oral history. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic, which outbroke in Wuhan (China) in December 2019, severely hit almost all sectors of activity in the world as a consequence of the restrictive measures imposed. Two years later, Africa still emerges as the least affected continent by the pandemic. This study analyzed COVID-19 prevalence across African countries through country-level variables prior to clustering. Using Spearman-rank correlation, multicollinearity analysis and univariate filtering, 9 country-level variables were identified from an initial set of 34 variables. These variables relate to socioeconomic status, population structure, healthcare system and environment and the climatic setting. A clustering of the 54 African countries is further carried out through the use of agglomerative hierarchical clustering (AHC) method, which generated 3 distinctive clusters. Cluster 1 (11 countries) is the most affected by COVID-19 (median of 63,508.6 confirmed cases and 946.5 deaths per million) and is composed of countries with the highest socioeconomic status. Cluster 2 (27 countries) is the least affected (median of 4473.7 confirmed cases and 81.2 deaths per million), and mainly features countries with the least socioeconomic features and international exposure. Cluster 3 (16 countries) is intermediate in terms of COVID-19 prevalence (median of 2569.3 confirmed cases and 35.7 deaths per million) and features countries the least urbanized and geographically close to the equator, with intermediate international exposure and socioeconomic features. These findings shed light on the main features of COVID-19 prevalence in Africa and might help refine effectively coping management strategies of the ongoing pandemic. [ABSTRACT FROM AUTHOR]

The sudden outbreak of the COVID-19 pandemic has brought drastic changes to people's daily lives, work, and the surrounding environment. Investigations into these changes are very important for decision makers to implement policies on economic loss assessments and stimulation packages, city reopening, resilience of the environment, and arrangement of medical resources. In order to analyze the impact of COVID-19 on people's lives, activities, and the natural environment, this paper investigates the spatial and temporal characteristics of Nighttime Light (NTL) radiance and Air Quality Index (AQI) before and during the pandemic in mainland China. The monthly mean NTL radiance, and daily and monthly mean AQI are calculated over mainland China and compared before and during the pandemic. Our results show that the monthly average NTL brightness is much lower during the quarantine period than before. This study categorizes NTL into three classes: residential area, transportation, and public facilities and commercial centers, with NTL radiance ranges of 5–20, 20–40 and greater than 40 (nW· cm − 2 · sr − 1 ), respectively. We found that the Number of Pixels (NOP) with NTL detection increased in the residential area and decreased in the commercial centers for most of the provinces after the shutdown, while transportation and public facilities generally stayed the same. More specifically, we examined these factors in Wuhan, where the first confirmed cases were reported, and where the earliest quarantine measures were taken. Observations and analysis of pixels associated with commercial centers were observed to have lower NTL radiance values, indicating a dimming behavior, while residential area pixels recorded increased levels of brightness after the beginning of the lockdown. The study also discovered a significant decreasing trend in the daily average AQI for mainland China from January to March 2020, with cleaner air in most provinces during February and March, compared to January 2020. In conclusion, the outbreak and spread of COVID-19 has had a crucial impact on people's daily lives and activity ranges through the increased implementation of lockdown and quarantine policies. On the other hand, the air quality of mainland China has improved with the reduction in non-essential industries and motor vehicle usage. This evidence demonstrates that the Chinese government has executed very stringent quarantine policies to deal with the pandemic. The decisive response to control the spread of COVID-19 provides a reference for other parts of the world. [ABSTRACT FROM AUTHOR]

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

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The entire world has changed a great deal starting with the fall of 2019 when, in Wuhan (China), a new and dangerous pandemic occurred. Nothing has been the same ever since. People started wearing masks, use lots of disinfectants and have their freedom of movement restricted. Of course, changes also occurred when it comes to communication in general and to didactic communication in particular. The shift towards online teaching became compulsory, although many students and teachers were not ready for this change and, we dare say that, even today, in 2021, some of them are still not able to correctly cope with the requirements of online classes. The purpose of this paper is to analyse the way in which the COVID-19 pandemic influenced present-day didactic communication and to highlight the pros and cons of shifting the classical teaching process, as we used to know it, into the online environment. [ABSTRACT FROM AUTHOR]

This study presents internet of things (IOT) and artificial intelligence technologies that are critical in reducing the harmful effects of this illness and assisting its recovery. It explores COVID-19's economic impacts before learning about new technologies and potential solutions. The research objective was to propose a solution for self-diagnosis, self-monitoring, and self-management of COVID-19 with personal mobiles and personal data using cloud solutions and mobile applications with the help of an intelligent IoT system, artificial intelligence, machine learning, and 5G technologies. The proposed solution based on self-diagnosis without any security risk for users' data with low cost of cloud-based data analytics by using handsets only is an innovative approach. Since the COVID-19 outbreak, the global social, economic, religious, and cultural frameworks and schedules have been affected adversely. The fear and panic associated with the new disease, which the world barely knew anything about, amplified the situation. Scientists and epidemiologists have traced the first outbreak of COVID-19 at Wuhan, China. A close examination of the genetic makeup of the virus showed that the virus is zoonotic, meaning that the virus changed hosts from animals to humans. The uncertainty associated with the above features and characteristics of the virus, as well as the high mortality rates witnessed in many parts of the globe, significantly contributed to the widespread global panic that brought the world to a standstill. Different authorities and agencies associated with securing the public have implemented different means and methods to try and mitigate the transmission of the infection as scientists and medical practitioners work on remedies to curb the spread of COVID-19. Owing to different demographics, different parts of the globe have attempted to effectively implement locally available resources to efficiently fight and mitigate the adverse effects of the COVID-19 pandemic. The general framework provided by the World Health Organization (WHO) has been implemented or enhanced in different parts of the globe by locally available resources and expertise to effectively mitigate the impact of COVID-19. There is currently no effective vaccine for COVID-19, but new technology can be available within weeks to reduce the spread of the disease; current approaches such as contact tracing and testing are not secure, and the cost of testing is high for end users. The proposed solution based on self-diagnosis without any security risk for users' data with low cost of cloud-based data analytics functions by using an intelligent internet of things (IOT) system for collecting sensors data and processing them with artificial intelligence to improve efficiency and reduce the spread of COVID-19. [ABSTRACT FROM AUTHOR]

The formulation of mathematical models using differential equations has become crucial in predicting the evolution of viral diseases in a population in order to take preventive and curative measures. In December 2019, a novel variety of Coronavirus (SARS-CoV-2) was identified in Wuhan, Hubei Province, China, which causes a severe and potentially fatal respiratory syndrome. Since then, it has been declared a pandemic by the World Health Organization and has spread around the globe. A reaction–diffusion system is a mathematical model that describes the evolution of a phenomenon subjected to two processes: a reaction process, in which different substances are transformed, and a diffusion process, which causes their distribution in space. This article provides a mathematical study of the Susceptible, Exposed, Infected, Recovered, and Vaccinated population model of the COVID-19 pandemic using the bias of reaction–diffusion equations. Both local and global asymptotic stability conditions for the equilibria were determined using a Lyapunov function, and the nature of the stability was determined using the Routh–Hurwitz criterion. Furthermore, we consider the conditions for the existence and uniqueness of the model solution and show the spatial distribution of the model compartments when the basic reproduction rate R 0 < 1 and R 0 > 1 . Thereafter, we conducted a sensitivity analysis to determine the most sensitive parameters in the proposed model. We demonstrate the model's effectiveness by performing numerical simulations and investigating the impact of vaccination, together with the significance of spatial distribution parameters in the spread of COVID-19. The findings indicate that reducing contact with an infected person and increasing the proportion of susceptible people who receive high-efficacy vaccination will lessen the burden of COVID-19 in the population. Therefore, we offer to the public health policymakers a better understanding of COVID-19 management. [ABSTRACT FROM AUTHOR]

COVID-19 (C19) was first recognized and described in November 2019 in Wuhan. In January 2020, WHO declared a public health emergency of international concern as a result of the spreading epidemic. By March of the same year, C19 was declared a pandemic. In connection with the pandemic, individual countries around the world enacted a variety of policies designed to slow the spread of C19; policies which dramatically impacted visits to museums of all kinds. Lockdowns selected institutional closures and restrictions and declines in tourism all contributed to precipitous declines in the use of museums. In response, museum significantly increased their digital presence, attempting to replace in-person experiences with virtual experiences of all kinds. However, the inherent limitations of the digital realm, including and particularly financial limitations, suggest an uncertain future for museums. We use data from visitations to the worlds' ten largest art museums over the period 2007-2020 as a vehicle for tangibly describing and discussing these issues. [ABSTRACT FROM AUTHOR]

The availability of accurate information has proved fundamental to managing health crises. This research examined pandemic data provided by 198 countries worldwide two years after the outbreak of the deadly Coronavirus in Wuhan, China. We compiled and reevaluated the consistency of daily COVID-19 infections with Benford's Law. It is commonly accepted that the distribution of the leading digits of pandemic data should conform to Newcomb-Benford's expected frequencies. Consistency with the law of leading digits might be an indicator of data reliability. Our analysis shows that most countries have disseminated partially reliable data over 24 months. The United States, Israel, and Spain spread the most consistent COVID-19 data with the law. In line with previous findings, Belarus, Iraq, Iran, Russia, Pakistan, and Chile published questionable epidemic data. Against this trend, 45 percent of countries worldwide appeared to demonstrate significant BL conformity. Our measures of Benfordness were moderately correlated with the Johns Hopkins Global Health Security Index, suggesting that the conformity to Benford's law may also depend on national health care policies and practices. Our findings might be of particular importance to policymakers and researchers around the world. [ABSTRACT FROM AUTHOR]

The world is currently struggling with a new type of coronavirus (2019-nCoV) pandemic that first appeared in Wuhan, China, and then spread to almost all countries. As in other countries of the world, public authorities in Turkey are implementing many preventive and mitigating partial lockdown (PL) actions against the virus's effects. Some decisions and policies implemented before and after March 11, 2020, when the first virus case has been identified, have reduced people and traffic circulation, which has also turned into some improvements in air quality. At this point, this study aims to investigate how this pandemic affects the air quality of a metropolis. A case study of the city of Istanbul, the most affected city with more than half of Turkey's cases, is performed. In our analysis, we observe, compare, and discuss the impact of the COVID-19 pandemic and PL decisions on Istanbul city's air quality. We consider the particulate matter (PM10), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), nitrogen oxide (NO), nitrogen oxides (NOx), and ozone (O3) concentrations. We used data from 19 air monitoring stations (AMSs) and obtained improvements in the air quality for the pandemic period. In summary, the concentration levels in PM10, NO2, NO, and NOx result in a clear decline in pandemic times compared to the normal times in Istanbul. On the other hand, a non-homogenous trend for SO2 and CO concentrations is observed for different AMSs. A partial increase in O2 concentration is obtained in the comparison of before and during the PL period. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this research is to compare over the developing trend in Covid-19 research publications between 2020 and 2021 in India, overall in respect of age groups, health conditions, funding support, institutions, and research design. Background: Covid-19 is a contagious disease caused by severe acute respiratory syndrome coronavirus (SARS-CoV), first cited in Wuhan, China in December 2019. And has affected the entire world rapidly and still ongoing. The symptoms are fever, cough, weakness, and breathlessness; the infected individual develops pneumonia that sometimes leads to respiratory failure. The older population with co-morbidities is at higher risk. Methodology: This is a cross-sectional study done by Scopus, Web of Science, and Pubmed Indexed journals, with the Covid-19, SARS-CoV, Pandemic, Coronavirus, India, and Outburst as keywords. Yearly publication data were extracted through 'Bibliometrix R studio' and relative percentage was computed and linear or exponential regressions examined the yearly growth in the proportion to research publications on Covid-19. [ABSTRACT FROM AUTHOR]

A sequence of unexplained pneumonia cases was reported in December 2019 in Wuhan, China. After several analyses, in January 2020, the World Health Organization temporarily named this new virus as the 2019 novel coronavirus (2019-nCoV). It is found that 2019-nCoV is abundantly present in nasopharyngeal and salivary secretions of affected patients and it is found to be transmitted through the saliva and the feco–oral routes since its spread is through potential person-to-person transmission route which is predominantly thought to be respiratory droplet/contact in nature. Dental surgeons are at tremendous risk of 2019-nCoV infection due to the face-to-face communication and the exposure to aerosols, saliva, blood, and other body fluids and the handling of sharp instruments and also may encounter patients with suspected or confirmed severe acute respiratory syndrome coronavirus 2 infection and will have to act diligently not only to provide care but also at the same time prevent nosocomial spread of infection. The significance of this review is that oral health providers must be aware and be prepared to tackle any imminent infectious diseases like the present coronavirus disease 2019 outbreak, which can be life threatening to susceptible patients. This review provides a brief outline of the epidemiology, symptoms, mode of transmission, specific recommendations for dental practice that are suggested for patient screening, infection control strategies to block the person-to-person transmission routes in dental clinics and hospitals, and patient management protocol since there are certain chances of encountering oral emergencies such as acute pulpitis, periapical periodontitis, abscess, dental trauma, children's oral emergencies, and oro-maxillofacial infections during this period. [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]

The SARS-CoV2 pandemic that began in December 2019 in Wuhan, China caused a major change in all areas of interest in our contemporary world, the medical system being particularly affected by the large number of infections, hospitalizations and deaths daily recorded. The impact of COVID-19 disease has affected the majority of medical specialities, dermatology and psychiatry being directly involved in the multitudeconsequences of this viral infection. Skin lesions related to the systemic disease are multiple, nonspecific and varied, reflecting the range of host immune responses to SARS-CoV2 infection. It appears that maculopapular, vesicular and urticarial eruptions are the most common lesions associated to COVID-19 disease. From a psychological point of view, both medical workers and patients were equally affected. On one hand, symptoms such as depression, anxiety, stress and posttraumatic stress disorder (PTSD) have been frequently reported among COVID-19 positive individuals and on the other hand, physical and mental exhaustion as well as burnout syndrome have affected the entire medical community. [ABSTRACT FROM AUTHOR]

Since it was first identified, the epidemic scale of the recently emerged novel coronavirus (2019-nCoV) in Wuhan, China, has increased rapidly, with cases arising across China and other countries and regions. Using a transmission model, we estimate a basic reproductive number of 3.11 (95% CI, 2.39-4.13), indicating that 58-76% of transmissions must be prevented to stop increasing. We also estimate a case ascertainment rate in Wuhan of 5.0% (95% CI, 3.6-7.4). The true size of the epidemic may be significantly greater than the published case counts suggest, with our model estimating 21 022 (prediction interval, 11 090-33 490) total infections in Wuhan between 1 and 22 January. We discuss our findings in the light of more recent information. [ABSTRACT FROM AUTHOR]

Purpose: With the rapid spread of COVID-19 across the world, the consideration of effective communication strategies from Wuhan can provide valuable insight to other countries in how to manage their risk response. This study analyzes the building of a risk communication system in Wuhan, China, to aid cross-country comparison from a policy and academic perspective. Methods: We use complex adaptive systems theory (CAS) to theorize the communication strategy adopted by the government – led by the Hubei Province COVID-19 Epidemic Prevention and Control Headquarters. Using ethnographic fieldwork and discourse analysis, we observed and analyzed the online and offline communication process to formulate an overview of the communications platforms used in Wuhan. Results: Wuhan's adaptive crisis communication system was backed by digital and offline infrastructure, human resources support, policy development, as well as access to scientific and technological expertize. The Wuhan municipal government adapted its communication strategy in response to public feedback, and created mechanisms to ensure that two-way communication was used to drive policy and integrate feedback from the government, enterprises, social organizations and the public. Wuhan's risk and crisis communication strategy aimed to meet emergency commitments, recover trust, regulate the public's emotions and build consensus, operating within a complex adaptive system (CAS). Conclusion: By using complex adaptive system (CAS) theory, we argue that Wuhan quickly built an adaptive communication system consisting of five elements: 1) non-linear information output, 2) online and offline continuous support systems, 3) a public emotional support system, 4) multi-subject information interaction platforms and 5) a context-based approach. [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]

A new type of coronavirus (COVID-19), detected at the end of December 2019 in Wuhan, China, can pass from person to person, spreading very quickly. The COVID-19 outbreak has created stress among societies. This study aims to evaluate the usability of Google Trends data in predicting and modeling the COVID-19 outbreak and the attitudes of different societies to it by using an infodemiological method. The authors collected the search words related to coronavirus and their relative search volume (RSV) from 11 different countries affected by the COVID-19 outbreak from Google Trends. A positive correlation was found between the trend rate of the words searched on the internet and the number of COVID-19 cases in countries related to the COVID-19 outbreak (p<0.05). There was a significant difference between 11 country societies in the daily RSV for the COVID-19 outbreak (p<0.05). The Turkish, South Korean, Iranian, and Swiss society have searched more intensely on the internet for COVID-19 than others. The research shows that Google Trends data can be used to build the forecast model for case numbers in the COVID-19 outbreak. Besides, Google Trends data provides information about different societies' attitudes in the COVID-19 outbreak. [ABSTRACT FROM AUTHOR]

In December 2019, a novel virus named COVID-19 emerged in the city of Wuhan, China. In early 2020, the COVID-19 virus spread in all continents of the world except Antarctica, causing widespread infections and deaths due to its contagious characteristics and no medically proven treatment. The COVID-19 pandemic has been termed as the most consequential global crisis since the World Wars. The first line of defense against the COVID-19 spread are the non-pharmaceutical measures like social distancing and personal hygiene. The great pandemic affecting billions of lives economically and socially has motivated the scientific community to come up with solutions based on computer-aided digital technologies for diagnosis, prevention, and estimation of COVID-19. Some of these efforts focus on statistical and Artificial Intelligence-based analysis of the available data concerning COVID-19. All of these scientific efforts necessitate that the data brought to service for the analysis should be open source to promote the extension, validation, and collaboration of the work in the fight against the global pandemic. Our survey is motivated by the open source efforts that can be mainly categorized as (a) COVID-19 diagnosis from CT scans, X-ray images, and cough sounds, (b) COVID-19 case reporting, transmission estimation, and prognosis from epidemiological, demographic, and mobility data, (c) COVID-19 emotional and sentiment analysis from social media, and (d) knowledge-based discovery and semantic analysis from the collection of scholarly articles covering COVID-19. We survey and compare research works in these directions that are accompanied by open source data and code. Future research directions for data-driven COVID-19 research are also debated. We hope that the article will provide the scientific community with an initiative to start open source extensible and transparent research in the collective fight against the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

It has been more than a year since the new virus called severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) was identified in Wuhan, China. The disease it causes was named Coronavirus disease 2019 (COVID-19), and on 11 March 2020 it was declared a pandemic. As the virus continues to spread, the number of patients worldwide has already crossed the 100 million mark with more than 2 million deaths. We sought to provide an update on the progress made in identifying the virus, its pathophysiology, risk factors such as hypertension, diabetes, and smoking, as well as various methods of treatment. Our review also provided an overview of the different vaccines. [ABSTRACT FROM AUTHOR]

Background/aim: The COVID-19 pandemic originated in Wuhan, China, in December 2019 and became one of the worst global health crises ever. While struggling with the unknown nature of this novel coronavirus, many researchers and groups attempted to project the progress of the pandemic using empirical or mechanistic models, each one having its drawbacks. The first confirmed cases were announced early in March, and since then, serious containment measures have taken place in Turkey. Materials and methods: Here, we present a different approach, a Bayesian negative binomial multilevel model with mixed effects, for the projection of the COVID-19 pandemic and we apply this model to the Turkish case. The model source code is available at https:// github.com/kansil/covid-19. We predicted the confirmed daily cases and cumulative numbers from June 6th to June 26th with 80%, 95%, and 99% prediction intervals (PI). Results: Our projections showed that if we continued to comply with the measures and no drastic changes were seen in diagnosis or management protocols, the epidemic curve would tend to decrease in this time interval. Also, the predictive validity analysis suggests that the proposed model projections should have a PI around 95% for the first 12 days of the projections. Conclusion: We expect that drastic changes in the course of COVID-19 in Turkey will cause the model to suffer in predictive validity, and this can be used to monitor the epidemic. We hope that the discussion on these projections and the limitations of the epidemiological forecasting will be beneficial to the medical community, and policy makers. [ABSTRACT FROM AUTHOR]

Coronavirus disease (COVID-19) has showed high transmission across the continents due to Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) with total infected cases of around ~ 44 million people. This communicable virus that initiated from the Wuhan city of China in the month of December 2020 has now spread to 189 different countries with 1.1 million fatalities worldwide (till 28 Oct, 2020). The World Health Organization (WHO) declared this outbreak as Public Health Emergency of International Concern in January, 2020. The infection spreads mainly due to contact with infected droplets or fomites, highlighting flu like symptoms initially, which may further progress into severe pneumonia and respiratory failure, often observed in elderly patients with prehistory of other diseases. The diagnosis is based on detection of viral antigen, human antibody and viral gene (RT-PCR). Further, various other diagnostic tools including X-ray, CT-scan are used for imaging purpose, recently artificial intelligence based imaging (contactless scanning) gained popularity. Generally testing of existing drugs (repurposing) and development of new molecules are the main strategies adopted by researchers. However, as per initial findings, various drugs, monoclonal antibody and plasma therapy were found to show effectiveness against COVID-19. Further, many vaccine candidates have entered or will soon enter phase III clinical testing. This disease has further challenged the global economy. Thus, this review uniquely compares the strategies adopted by developed and developing countries worldwide including protective measures like lockdown, continuous testing, utilizing latest tools (artificial intelligence) in curbing this infection spread. [ABSTRACT FROM AUTHOR]

Copyright of Eurasian Journal of Veterinary Sciences is the property of Eurasian Journal of Veterinary Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Coronaviruses are enveloped viruses with a positive-sense single-stranded RNA genome infecting animals and humans. Coronaviruses have been described more than 70 years ago and contain many species. Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) are lethal species caused by human coronaviruses (HCoVs). Currently, a novel strain of HCoVs, named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (Covid-19). SARS-CoV-2 was first identified in December 2019 in Wuhan, the capital city of the Hubei province of China, and has since spread worldwide causing an outbreak in more than 200 countries. The SARS-CoV-2 outbreak was declared a pandemic on March 11th, 2020 and a public health emergency of international concern (PHEIC) in late January 2020 by the World Health Organization (WHO). SARS-CoV-2 infects the respiratory tract causing flu-like symptoms and, in some, may cause severe illness like pneumonia and multi-organ failure leading to death. Today, Covid-19 cases almost reaching 9 million, with more than 450 thousand deaths. There is an urgent demand for developing a vaccine since no effective therapies or vaccines have been approved to this day to prevent or minimize the spread of the infection. In this review, we summarized the furthest vaccines in the clinical pipeline. [ABSTRACT FROM AUTHOR]

Copyright of Electronic Turkish Studies is the property of Electronic Turkish Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electronic Turkish Studies is the property of Electronic Turkish Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

On December 31, 2019, the Chinese authorities announced that in the city of Wuhan, Hubei Province, central-eastern China, a cluster of pneumonia cases of unknown etiology had developed. A new coronavirus (SARS-CoV-2) that causes serious problems like pneumonia and even death, has been discovered. This new disease (COVID-19) has spread also in Italy starting from the first recognized case on February 20. Beyond its biological implications, this coronavirus allows us many psychological reflections. A new virus is indeed a potentially serious problem for mankind, but it can also be an opportunity to bring the focus back to us, to observe what is happening, who we are and how we are reacting both as individuals and as a population. Even positive implication of this pandemic was discussed. [ABSTRACT FROM AUTHOR]

The world entered the year 2020 with reports of the emergence of a new viral illness in Wuhan city, Hubei province, China. In January 2020, severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) was identified to be the causative novel coronavirus for the cluster of patients suffering from pneumonia in China. The disease was later named as coronavirus disease (COVID‐19) and was declared a pandemic by the World Health Organization on March 11, 2020. Several studies, since then, have tried to study and explain the origin of SARS‐CoV‐2, its structure and pathogenicity, epidemiology, modes of transmission, spectrum of illness and causes of mortality and morbidity. The current management strategies focus on supportive care and prevention of complications. With no definite treatment, as of now, encouraging reports of some anti‐viral and anti‐malarial drugs in the management of COVID‐19 generate some hope. This review intends to cover the current known aspects of COVID‐19 and SARS‐CoV‐19, based on the available literature. [ABSTRACT FROM AUTHOR]

The pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become the reason of the global health crisis. Since the first case of diagnosed COVID-19 pneumonia was reported in Wuhan, Hubei Province, China, in December 2019, the infection has spread rapidly to all over the world. The knowledge gained from previous human coronavirus infection outbreaks suggests that pregnant women and their foetuses represent a high-risk population during infectious disease epidemics. Moreover, a pregnancy, due to the physiological changes involving immune and cardiopulmonary systems, is a state predisposing women to respiratory complications of viral infection. The constantly increasing number of publications regarding the course of COVID-19 infection in pregnant women has been published, however, the available data remains limited and many questions remain unanswered. The aim of this review was to summarize the literature data and adjusted to current recommendations regarding pregnancy care, delivery and postpartum period. An extremely important issue is the need to register all the cases of COVID-19 affected women and the course of these pregnancies to local, regional, or international registries, which will be helpful to answer many clinical and scientific questions and to create guidelines ensuring an adequate level of care for women affected by COVID-19 infection during pregnancy, delivery and during postpartum period, as well as their newborns. [ABSTRACT FROM AUTHOR]

Various mitigation measures have been implemented to fight the coronavirus disease 2019 (COVID-19) pandemic, including widely adopted social distancing and mandated face covering. However, assessing the effectiveness of those intervention practices hinges on the understanding of virus transmission, which remains uncertain. Here we show that airborne transmission is highly virulent and represents the dominant route to spread the disease. By analyzing the trend and mitigation measures in Wuhan, China, Italy, and New York City, from January 23 to May 9, 2020, we illustrate that the impacts of mitigation measures are discernable from the trends of the pandemic. Our analysis reveals that the difference with and without mandated face covering represents the determinant in shaping the pandemic trends in the three epicenters. This protective measure alone significantly reduced the number of infections, that is, by over 78,000 in Italy from April 6 to May 9 and over 66,000 in New York City from April 17 to May 9. Other mitigation measures, such as social distancing implemented in the United States, are insufficient by themselves in protecting the public. We conclude that wearing of face masks in public corresponds to the most effective means to prevent interhuman transmission, and this inexpensive practice, in conjunction with simultaneous social distancing, quarantine, and contact tracing, represents the most likely fighting opportunity to stop the COVID-19 pandemic. Our work also highlights the fact that sound science is essential in decision-making for the current and future public health pandemics. [ABSTRACT FROM AUTHOR]

A novel infectious disease named coronavirus disease-2019 (COVID-19) first emerged in Wuhan, China, in December 2019 and rapidly spread worldwide. In March 2020 the World Health Organization declared a global pandemic which is the worst global public health crisis in over 100 years. On 14 June 2020, almost eight million confirmed cases of COVID-19 and more than 430,000 deaths globally were reported. Over 200 countries have been affected so far. COVID-19 is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In the absence of specific therapy and vaccine, effective infection control intervention, particularly self-hygiene and social distancing, is the only way to prevent the spread of SARS-CoV-2. The lessons learned from this pandemic will be useful for successful management of the second wave of COVID-19 which is expected in autumn or winter. [ABSTRACT FROM AUTHOR]

Since the onset of the COVID-19 outbreak in Wuhan, China, numerous forecasting models have been proposed to project the trajectory of coronavirus infection cases. Most of these forecasts are based on epidemiology models that utilize deterministic differential equations and have resulted in widely varying predictions. We propose a new discrete-time Markov chain model that directly incorporates stochastic behavior and for which parameter estimation is straightforward from available data. Using such data from China's Hubei province (for which Wuhan is the provincial capital city and which accounted for approximately 82% of the total reported COVID-19 cases in the entire country), the model is shown to be flexible, robust, and accurate. As a result, it has been adopted by the first Shanghai assistance medical team in Wuhan's Jinyintan Hospital, which was the first designated hospital to take COVID-19 patients in the world. The forecast has been used for preparing medical staff, intensive care unit (ICU) beds, ventilators, and other critical care medical resources and for supporting real-time medical management decisions. [ABSTRACT FROM AUTHOR]

The current pandemic of COVID-19 has caused havoc all over world since its emergence and rapid spread. Within three months the virus SARS-CoV-2 which was isolated from pneumonia cases in Wuhan City, Hubei Province, China in late December 2019, has affected almost all countries. India reported its first case of COVID-19 from state of Kerala on January 30, 2020, a student returned from city of Wuhan. Till date in India the disease had affected 12759 patients with 420 deaths. With every passing day the mysterious virus is been uncovered with its unique characteristics enabling the researcher to unfold the various methods including hand washing and social distancing to curtail the pandemic. Measures like 21 days lockdown to certain extent are effective but considering asymptomatic spreaders, extended measured lockdowns will be useful in the long term war against COVID-19. Till the vaccine and therapeutic solutions are derived, answer to pandemic and SARS-CoV-2 lies in lockdown, social distancing, contact tracing and containment. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the coronavirus that causes an infectious disease, called COVID-19, first detected in patients with pneumonia in Wuhan (People's Republic of China) on December 2019. Italy was the first European country to state the outbreak of the infection and its Council of Ministers declared the state of health emergency on 31.01.2020, then the World Health Organization ruled a global pandemic on 11.03.2020. The nasopharyngeal swab is based on the detection of virus RNA and is the only reliable one for declaring COVID-19 infection. The most common symptoms observed in COVID-19 patients before hospitalization may be fever, chills, cough, dyspnea, asthenia, myalgia and/or arthralgia. This symptomatology can be often complicated in a dramatically increasing manner such as to require hospitalization starting from the third-fourth week. COVID-19 outbreak has dramatically affected the quality of life by changing inter-personal relationships, community life and obviously sexual health. The purpose of this work, based on available evidence, is to provide recommendations to help the population to face their sexual life in this critical period. [ABSTRACT FROM AUTHOR]

In December 2019, severe acute respiratory syndrome-coronavirus-2, a novel coronavirus, initiated an outbreak of pneumonia from Wuhan in China, which rapidly spread worldwide. The clinical characteristics of the disease range from asymptomatic cases or mild symptoms, which include nonspecific symptoms such as fever, cough, sore throat, headache, and nasal congestion to severe cases such as pneumonia, respiratory failure demanding mechanical ventilation to multi-organ failure, sepsis, and death. As the transmission rate is quite alarming, we require an effective therapeutic strategy to treat symptomatic patients and adopt the preventive measures in order to contain the infection and prevent community transmission. Coronavirus disease 2019 (COVID-19) pandemic is a public health emergency of international concern, hence repurposing of the drugs is an attractive and a feasible option because PK/PD profile, toxicity profile, and drug interactions are already known. This review emphasizes on the different aspects of COVID-19 such as the epidemiology, etiopathogenesis, diagnosis, and preventive measures to be adopted in order to fight this pandemic. It also highlights upon the ethics preparedness and challenges faced by a developing country like India during such an outbreak. The review focuses on the various approaches adopted till date for developing effective therapeutic strategies including combination of drugs, vaccine therapy, and convalescent plasma therapy to combat this viral outbreak. [ABSTRACT FROM AUTHOR]