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This paper documents a strong delayed response to COVID-19, which is caused by the SARS-CoV- 2 virus in countries that did not encounter the SARS disease in 2003. The SARS outbreak was caused by a similar virus, SARS-CoV-1. Individuals in countries that developed SARS infections in 2003 search more intensively for COVID-19-related information on Google during the first outbreak of COVID-19 in Wuhan, China, in late January 2020. Governments in countries that have not experienced SARS respond significantly slower in implementing containment measures to combat COVID-19 than countries that have experienced SARS. Furthermore, the timely responses of individuals and governments are more pronounced in countries that reported deaths caused by SARS, which left deeper imprints. Consequently, COVID-19 case numbers and mortalities have been substantially higher in countries that did not experience SARS deaths. Our findings suggest that the imprint of the early experience of similar viruses is a fundamental mechanism underlying timely responses to COVID-19. [ABSTRACT FROM AUTHOR]

The Coronavirus epidemic first appeared in Wuhan-China on December, 31st, 2019. This has put the world's hospitals, clinics, testing laboratories and health administrations under pressure. As of April, 04th, 2020, the World Health Organization reported more than 167515 confirmed cases in more than 100 countries worldwide. The diagnosis of the epidemic will increase the burden on overburdened testing laboratories. Several screening methods have been proposed in parallel in order to facilitate and, above all, to make rapid diagnosis easier. At this level, X-Ray images seem to be a good accompanying solution for emerging countries to help rapid screening. The solution proposed in this paper consists on a collaborative and smart platform based on the Convolutional Neural Network for Classification and Detection namely VGG16. The platform ensures the fast download of the X-Ray image, with the entry of the patient's personal information followed by the launch of a 5 seconds test. The platform generates, as a result, a PDF file containing all patient information. [ABSTRACT FROM AUTHOR]

• Almost all currently infected COVID-19 patients in Henan province were analyzed. • COVID-19 patients in Henan province show gender and age preferences, migrant workers or college students are at high risk. • The incubation period was statistically estimated. • The state transition diagram can reveal the time-phased nature of the COVID-19 epidemic. • Network analysis reveals the aggregate outbreak phenomena of COVID-19. COVID-19 is spreading quickly all over the world. Publicly released data for 1212 COVID-19 patients in Henan of China were analyzed in this paper. Various statistical and network analysis methods were employed. We found that COVID-19 patients show gender (55% vs 45%) and age (81% aged between 21 and 60) preferences; possible causes were explored. The estimated average, mode and median incubation periods are 7.4, 4 and 7 days. Incubation periods of 92% of patients were no more than 14 days. The epidemic in Henan has undergone three stages and has shown high correlations with the numbers of patients recently returned from Wuhan. Network analysis revealed that 208 cases were clustering infected, and various People's Hospitals are the main force in treating COVID-19. The incubation period was statistically estimated, and the proposed state transition diagram can explore the epidemic stages of emerging infectious disease. We suggest that although the quarantine measures are gradually working, strong measures still might be needed for a period of time, since ∼7.45% of patients may have very long incubation periods. Migrant workers or college students are at high risk. State transition diagrams can help us to recognize the time-phased nature of the epidemic. Our investigations have implications for the prevention and control of COVID-19 in other regions of the world. [ABSTRACT FROM AUTHOR]

• The latest summary of the COVID-19 outbreak in China; • There might be an oral-fecal transmission of the virus; • Aggregates and consolidates the epidemiology, clinical manifestations, diagnosis, treatments and preventions of this new type of coronavirus. There is a current worldwide outbreak of the novel coronavirus Covid-19 (coronavirus disease 2019; the pathogen called SARS-CoV-2; previously 2019-nCoV), which originated from Wuhan in China and has now spread to 6 continents including 66 countries, as of 24:00 on March 2, 2020. Governments are under increased pressure to stop the outbreak from spiraling into a global health emergency. At this stage, preparedness, transparency, and sharing of information are crucial to risk assessments and beginning outbreak control activities. This information should include reports from outbreak site and from laboratories supporting the investigation. This paper aggregates and consolidates the epidemiology, clinical manifestations, diagnosis, treatments and preventions of this new type of coronavirus. [ABSTRACT FROM AUTHOR]

The recent emergence of a new coronavirus (COVID-19) has gained a high cover in public media and worldwide news. The virus has caused a viral pneumonia in tens of thousands of people in Wuhan, a central city of China. This short paper gives a brief introduction on how the demand for information on this new epidemic is reported through Google Trends. The reported period is 31 December 2020 to 20March 2020. The authors draw conclusions on current infodemiological data on COVID-19 using three main search keywords: coronavirus, SARS and MERS. Two approaches are set. First is the worldwide perspective, second - the Chinese one, which reveals that in China this disease in the first days was more often referred to SARS then to general coronaviruses, whereas worldwide, since the beginning, it is more often referred to coronaviruses. [ABSTRACT FROM AUTHOR]

Introduction. Coronavirus disease is a pandemic disease with a rapid increase in cases and deaths since its first identification in Wuhan, in December 2019. The aim of the study is to evaluate epidemiologic and prognostic factors for the deliveries assisted in the Obstetrics and Gynecology Clinic of Sibiu since March 2020. Frequent manifestations include fever, cough, myalgia, headache and diarrhea. Abnormal testing includes abnormalities on chest radiographic imaging, lymphopenia, leukopenia and thrombocytopenia. Materials and method. We have evaluated all the deliveries from pregnancies complicated with COVID-19 during since March 2020. The data were obtained from the written information in the observation papers and in the delivery registers. There were obtained general data, delivery mode, age, gestational age, fetal weight, modality of delivery, newborn evaluation at delivery and in utero transmission of the disease. Results. A total of 85 pregnancies were assisted during the period in observation, the average age of the pregnant women was 32.4 years old, the average gestational age was 38.1 weeks, 40 deliveries being assisted in the natural way and 45 by caesarean section. The main three indications for caesarean section were scarred uterus, advanced maternal age and thrombophilia. The average weight of the newborn was 3120 grams. No case of intrauterine transmission to the fetus was found. No severe complications, neither obstetrical nor respiratory, were registered. Conclusions. Because coronavirus disease might increase the risk for pregnancy complications, the management should optimally be in a healthcare facility with close maternal and fetal monitoring. No evidence of in utero transmission was seen in severe acute respiratory syndrome. Currently, no coronavirus-specific treatments have been approved by the Drug Administration in Europe or in the United States of America. [ABSTRACT FROM AUTHOR]

The coronavirus pandemic, also known as the COVID-19 pandemic, is an ongoing virus. It was first identified on December 2019 in Wuhan, China, and later spread to 192 countries. As of now, 251,266,207 people have been affected, and 5,070,244 deaths are reported. Due to the growing number of COVID-19 patients, the demand for COVID wards is increasing. Telemedicine applications are increasing drastically because of convenient treatment options. The healthcare sector is rapidly adopting telemedicine applications for the treatment of COVID-19 patients. Most telemedicine applications are developed for heterogeneous environments and due to their diverse nature, data transmission between similar and dissimilar telemedicine applications is a difficult task. In this paper, we propose a Tele-COVID system architecture design along with its security aspects to provide the treatment for COVID-19 patients from distance. Tele-COVID secure system architecture is designed to resolve the problem of data interchange between two different telemedicine applications, interoperability, and vendor lock-in. Tele-COVID is a web-based and Android telemedicine application that provides suitable treatment to COVID-19 patients. With the help of Tele-COVID, the treatment of patients at a distance is possible without the need for them to visit hospitals; in case of emergency, necessary services can also be provided. The application is tested on COVID-19 patients in the county hospital and shows the initial results. [ABSTRACT FROM AUTHOR]

The COVID-19 virus first appeared in Wuhan, China, and has affected the whole world. Due to COVID-19, which spreads rapidly and causes death, countries have taken their own pandemic measures. The first case of COVID-19 was seen in Turkey on March 11, 2020, and on the same day, the COVID-19 outbreak was declared a pandemic by the World Health Organization. Turkey has implemented measures such as full closure and partial closure throughout the country in certain periods during the pandemic process. These measures have increased the time people spend at home and have led to differences in their general lifestyles. These differences have caused various effects, especially on ecological carrying capacity, as well as the changes in the world's economic and social consumption habits (electricity, heating, transportation, etc.). It is observed that the changing human habits due to the pandemic are effective in ecological developments, in cities having cleaner air and environment, and in the positive renewal of natural life. One of the most important components of the ecological footprint, which is used to make ecological differences measurable and comparable, is the carbon footprint. In this study, the individual change in the carbon footprint is discussed and the positive environmental changes in Turkey are questioned in relation to individual human activities. The study comparatively examines pre-COVID-19 (before 1 March 2020) and post-COVID-19 (after 1 March 2020) in terms of individual carbon footprint. [ABSTRACT FROM AUTHOR]

In December 2019, a SARS-CoV-2 virus, coined Coronavirus Disease 2019 (COVID-19), emanating from Wuhan, China, affected the global population, causing more than a million and a half deaths. Since then, many studies have shown that the hyperinflammatory response of the most severely affected patients was primarily related to a higher concentration of the pro-inflammatory cytokine interleukin-6, which directly correlated with disease severity and high mortality. Our study analyzes IL-6 and its soluble receptor complex (sIL-6R and sgp130) in critically ill COVID-19 patients who suffered severe respiratory failure from the perspective of the second COVID wave of 2020. A chemiluminescent immunoassay was performed for the determination of IL6 in serum together with an enzyme-linked immunosorbent assay to detect serum levels of sIL-6R and sgp130, which confirmed that the second wave's serum levels of IL-6 were significantly elevated in the more severe patients, as with the first 2019 COVID-19 wave, resulting in adverse clinical outcomes. At present, considering that no specific treatment for severe COVID-19 cases in its later stages exists, these molecules could be considered promising markers for disease progression, illness severity, and risk of mortality. [ABSTRACT FROM AUTHOR]

Coronavirus Disease 2019 (COVID-19) is an infectious respiratory illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease, first identified in the Chinese city of Wuhan in November 2019, has since spread worldwide, is the latest human pandemic and has officially infected over 800 million people and has caused nearly seven million deaths to date. Although SARS-CoV-2 belongs to the large family of coronaviruses, it has some unique biological characteristics in its interplay with the human host. Therefore, this narrative review aims to provide an up-to-date overview of the structure of the virus, incubation and shedding in the human host, infectivity and biological evolution over time, as well as the main mechanisms for invading human host cells and replicating within. We also proffer that ongoing epidemiological surveillance of newly emerged variants must always be accompanied by biological studies aimed at deciphering new advantageous traits that may contribute to increasing virulence and pathogenicity, such that the most appropriate strategies for establishing a (relatively) safe coexistence with the human host can be implemented. [ABSTRACT FROM AUTHOR]

Most countries and territories worldwide are affected by coronavirus disease 2019 (COVID-19), and some cities have become known as epicenters owing to high outbreaks. Because of the changeable and unknown nature of the virus, managers of different cities could learn from the experiences of cities that have been successful in controlling COVID-19 instead of wasting time exploring different methods. It would be even more beneficial if they analyzed the experiences of similar cities. The similarity of such cities could be examined within a geographic information system based on various criteria. This study investigated the similarities among eight cities – Wuhan, Tehran, Bergamo, Madrid, Paris, Daegu, New York, and Berlin – in terms of the COVID-19 situation (target) in these locations based on proximity factors, weather, and demographic criteria. First, the factor and target layers were prepared, and then similar cities were identified using a similarity model and different distance metrics. The results were aggregated using the Copeland method because of the different outcomes for each metric. The most similar city was identified for each selected city, and its similarity level was determined based on these criteria. The results suggested the following pairs of similar cities: Wuhan–Berlin, Tehran–Berlin, Daegu–Wuhan, Bergamo–Madrid, Paris–Madrid, and New York–Paris based on COVID-19 related data up to 15 April 2020 (target T1), and Daegu–Wuhan, Tehran–Madrid, Bergamo–Paris, Berlin–Paris, and New York–Madrid up to 8 December 2021 (target T2) with a minimum and maximum similarity rate of 82.85% and 92.36%, respectively. For similar cities, the most similar factors among the proximity criteria are the distance from bus and metro stations; among weather, the criteria are humidity and pressure; and among demographics, the criteria are male and female population ratios, literacy ratio, and death ratio from asthma and cancer, with a minimum and maximum difference of 0% and 64.94%, respectively. In addition, according to the random forests ranking results (with root mean squared error = 0.23), temperature, distance from the bank, and gender were the most important criteria for the eight studied cities. Identifying these important factors helps to determine hotspots or places of future outbreaks to choose control strategies according to the cultural and ecological conditions of each city. [ABSTRACT FROM AUTHOR]

While extensive literature exists on the COVID-19 pandemic at regional and national levels, understanding its dynamics and consequences at the city level remains limited. This study investigates the pandemic in Maringá, a medium-sized city in Brazil's South Region, using data obtained by actively monitoring the disease from March 2020 to June 2022. Despite prompt and robust interventions, COVID-19 cases increased exponentially during the early spread of COVID-19, with a reproduction number lower than that observed during the initial outbreak in Wuhan. Our research demonstrates the remarkable impact of non-pharmaceutical interventions on both mobility and pandemic indicators, particularly during the onset and the most severe phases of the emergency. However, our results suggest that the city's measures were primarily reactive rather than proactive. Maringá faced six waves of cases, with the third and fourth waves being the deadliest, responsible for over two-thirds of all deaths and overwhelming the local healthcare system. Excess mortality during this period exceeded deaths attributed to COVID-19, indicating that the burdened healthcare system may have contributed to increased mortality from other causes. By the end of the fourth wave, nearly three-quarters of the city's population had received two vaccine doses, significantly decreasing deaths despite the surge caused by the Omicron variant. Finally, we compare these findings with the national context and other similarly sized cities, highlighting substantial heterogeneities in the spread and impact of the disease. [ABSTRACT FROM AUTHOR]

Background/purpose: In December 2019, Wuhan, Hubei, China firstly reported the existence of the COVID-19 virus. It is crucial to prioritize the psychological well-being of citizens in lockdown cities and make more strides in the academic field of post-traumatic stress disorder (PTSD) to prepare for the post-pandemic era. Methods: We took the cognitive-relational theory as our basis and collected Hubei province-level data (N = 3,465) to examine the impact of perceived social status decline on the prevalence of PTSD symptoms, and checked the mediating effect of perceived vulnerability to disease (PVD) during the period of psychological adjustment. Results: Using propensity score matching, we estimate the average treatment effect of perceived social status decline on PTSD level, and we robustly regress the two with weight adjustment generated in matching. We found that more decline in perceived social status is associated with a worse degree of PTSD symptoms, and confirmed PVD's buffering role although the mediating effect was not as high as hypothesized. Conclusion and implications: Our study confirmed the decisive role of subject social status in health prediction compared to traditional socioeconomic measures, which extends the cognitive-relational in examining socioeconomic status and contributes to the dialog on socioeconomic inequality. We also suggested providing more social support at the community level and enhancing individuals' positive understanding to protect mental health. [ABSTRACT FROM AUTHOR]

Artificial intelligence plays a very prominent role in many fields, and of late, this term has been gaining much more popularity due to recent advances in machine learning. Machine learning is a sphere of artificial intelligence where machines are responsible for doing daily chores and are believed to be more intelligent than humans. Furthermore, artificial intelligence is significant in behavioral, social, physical, and biological engineering, biomathematical sciences, and many more disciplines. Fractional-order modeling of a real-world problem is a powerful tool for understanding the dynamics of the problem. In this study, an investigation into a fractional-order epidemic model of the novel coronavirus (COVID-19) is presented using intelligent computing through Bayesian-regularization backpropagation networks (BRBFNs). The designed BRBFNs are exploited to predict the transmission dynamics of COVID-19 disease by taking the dataset from a fractional numerical method based on the Grünwald–Letnikov backward finite difference. The datasets for the fractional-order mathematical model of COVID-19 for Wuhan and Karachi metropolitan cities are trained with BRBFNs for biased and unbiased input and target values. The proposed technique (BRBFNs) is implemented to estimate the integer and fractional-order COVID-19 spread dynamics. Its reliability, effectiveness, and validation are verified through consistently achieved accuracy metrics that depend on error histograms, regression studies, and mean squared error. [ABSTRACT FROM AUTHOR]

Purpose: The pandemic of coronavirus disease 2019 (COVID-19) has highlighted the intricate relationship between underlying conditions and death. We designed this study to determine whether metformin therapy for type 2 diabetes mellitus (T2D) is associated with low in-hospital mortality in patients hospitalized for COVID-19.Materials and Methods: This was a retrospective study including patients with COVID-19 and T2D in Wuhan, from February 4th to April 11th, 2020. Patients were divided into two groups according to metformin exposure. The hazard ratio (HR) of COVID-19-related mortality and invasive mechanical ventilation was estimated using Cox regression.Results: There were 571 T2D patients among the 4330 confirmed COVID-19 patients. Of those patients, 241 received metformin therapy. The in-hospital mortality and invasive mechanical ventilation of metformin group was lower than non-metformin group. In the multivariate model, metformin use was linked to a decreased in-hospital mortality and invasive mechanical ventilation when compared with that of the control group (HR: 0.376 [95% CI 0.154– 0.922]; P = 0.033).Conclusion: Our study indicated that metformin therapy was associated with decreased death risk in COVID-19 patients with T2D. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic erupted from Wuhan/China, has impacted the whole world in many ways drastically. Understanding such a pandemic, taking necessary precautions, and controlling a potential epidemic is essential. Modules and communities are typically and frequently represented in networks and analyzed with specific methods. Epidemic network analysis is a powerful method that provides us different information to interpret and make accurate decisions for pandemic events. The randomness of epidemic networks is considerably high; hence, it is essential to obtain consistent information from such networks. In this study, we proposed the network-based community detection of the early-stage COVID-19 associated with the basic reproduction number and the geographical distance between country locations. Worldwide confirmed cases between 22/01/2020 and 08/06/2020 had been analyzed for tenday periods. For this purpose, the Community Detection method was used. Therefore, (i) the community regions, (ii) the change of these regions and weighted nodal degrees of confirmed cases during the COVID-19, (iii) the relations between locations, and (iv) locations or regions which played an important role during the spread of this disease were obtained. The results of this study may help to reduce the reproduction number by lowering the average rate of contact in the early stage of a new pandemic. [ABSTRACT FROM AUTHOR]

The global healthcare sector has been dealing with a situation known as a novel severe acute respiratory syndrome (SARS-CoV-2) since the end of 2019. Covid-19 is an acronym for Covid-19 (Coronavirus Disease- 2019). It causes a respiratory infection that includes cold, sneezing and coughing, and pneumonia. In the case of an animal, it causes diarrhea and upper respiratory diseases. Covid-19 transmitted human to human via airborne droplets. First Covid-19 emerged in Wuhan market China and it spread rapidly throughout the World. As we know nanoparticles are a novel drug delivery system. They have various advantageous effects like increasing the efficacy of the drug, safety, etc. In this review, we study about the nanoparticles and summarize how it is effective during drug delivery system in Covid-19. Chitosan is a much focused biopolymeric nanoparticle. It delivers drugs to the specific target site. In a recent health crisis, chitosan nanoparticles are one of the ways to release drugs of Covid-19, and specifically in the lungs of the affected patients. We studied and extracted our data from various research papers, review papers, and some other articles. The main goal is to study the nanoparticles and their future aspects which is an effective drug delivery system in Covid-19. The bibliographic search was done through a systematic search. The terms "Nanoparticles", "Covid-19 ", "Drug delivery" etc. were used to search the databases/search engines like "Google Scholar", "NCBI", "PubMed", "Science Direct" etc. These databases and search engines used here perform the limited criteria of search to conduct a systematic literature survey for the study and report writing. All the text from the articles and research papers were studied and analyzed. The various articles and research papers were used in writing this report and all of which are mentioned in the reference section of this report. Our current studies reveal that nanoparticles may prove very helpful in the delivery of drugs for Covid-19 treatment. Many cases showed that patients, where drugs are delivered with the help of nanoparticles, produced very few side effects. [Display omitted] • Almost every antiviral pharmaceutical system can be nano-scaled to improve its properties. • Nanoparticles as delivery vehicles can improve the effectiveness of drug delivery. • Nanomedicines and Nano- drug delivery system have shown promising effectiveness against the Covid 19 as treatments. • Numerous subsidies to advance the nanoparticular antiviral field are provided for public health and financially. [ABSTRACT FROM AUTHOR]

Simple Summary: The coronavirus disease 2019 (COVID-19) pandemic, caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first encountered in December of 2019 in Wuhan, China. As of now, there have been over 200 million infections and 4 million deaths attributed to the virus. Due to this, it has been a priority to find an effective preventative measure, and numerous vaccines have been developed. Although the developed vaccines share the target of blocking viral entry by the spike protein, their pharmacology and efficacy differs. As such, the mechanism of action and the elicited immune response of the most common COVID-19 vaccines have been compared to help determine which vaccine is most efficacious and is best suited to prevent reinfection and address viral mutations. It has been over a year since SARS-CoV-2 was first reported in December of 2019 in Wuhan, China. To curb the spread of the virus, many therapies and cures have been tested and developed, most notably mRNA and DNA vaccines. Federal health agencies (CDC, FDA) have approved emergency usage of these S gene-based vaccines with the intention of minimizing any further loss of lives and infections. It is crucial to assess which vaccines are the most efficacious by examining their effects on the immune system, and by providing considerations for new technological vaccine strategies in the future. This paper provides an overview of the current SARS-CoV-2 vaccines with their mechanisms of action, current technologies utilized in manufacturing of the vaccines, and limitations in this new field with emerging data. Although the most popular COVID-19 vaccines have been proven effective, time will be the main factor in dictating which vaccine will be able to best address mutations and future infection. [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 recent years, as China's peaking carbon dioxide emissions and air pollution control projects have converged, scholars have begun to focus on the synergistic mechanisms of greenhouse gas and pollution gas reduction. In 2020, the unprecedented coronavirus pandemic, which led to severe nationwide blockade measures, unexpectedly provided a valuable opportunity to study the synergistic reduction in greenhouse gases and polluting gases. This paper uses a combination of NO2, O3, and CO2 column concentration products from different satellites and surface concentrations from ground-based stations to investigate potential correlations between these monitoring indicators in four Chinese representative cities. We found that XCO2 decreased in March to varying degrees in different cities. It was witnessed that the largest decrease in CO2, −1.12 ppm, occurred in Wuhan, i.e., the first epicenter of COVID-19. We also analyzed the effects of NO2 and O3 concentrations on changes in XCO2. First, in 2020, we used a top-down approach to obtain the conclusion that the change amplitude of NO2 concentration in Beijing, Shanghai, Guangzhou, and Wuhan were −24%, −18%, −4%, and −39%, respectively. Furthermore, the O3 concentration increments were 5%, 14%, 12%, and 14%. Second, we used a bottom-up approach to obtain the conclusion that the monthly averaged NO2 concentrations in Beijing, Shanghai, and Wuhan in March had the largest changes, changing to −39%, −40%, and −61%, respectively. The corresponding amounts of changes in monthly averaged O3 concentrations were −14%, −2%, and 9%. However, the largest amount of change in monthly averaged NO2 concentration in Guangzhou was found in December 2020, with a value of −40%. The change in O3 concentration was −12% in December. Finally, we analyzed the relationship of NO2 and O3 concentrations with XCO2. Moreover, the results show that the effect of NO2 concentration on XCO2 is positively correlated from the point of the satellite (R = 0.4912) and the point of the ground monitoring stations (R = 0.3928). Surprisingly, we found a positive (in satellite observations and R = 0.2391) and negative correlation (in ground monitoring stations and R = 0.3333) between XCO2 and the O3 concentrations. During the epidemic period, some scholars based on model analysis found that Wuhan's carbon emissions decreased by 16.2% on average. Combined with satellite data, we estimate that Wuhan's XCO2 fell by about 1.12 ppm in February. At last, the government should consider reducing XCO2 and NO2 concentration at the same time to make a synergistic reduction. [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]

• Use Machine Learning models to predict the mortality of COVID19 patients. • Analyse patient case studies which may help in better personalised treatment. • Apply Shapley Values to interpret the Machine Learning models predictions. • Perform what-if analysis to see how patient mortality outcomes change. In this paper we apply a series of Machine Learning models to a recently published unique dataset on the mortality of COVID19 patients. We use a dataset consisting of blood samples of 375 patients admitted to a hospital in the region of Wuhan, China. There are 201 patients who survived hospitalisation and 174 patients who died whilst in hospital. The focus of the paper is not only on seeing which Machine Learning model is able to obtain the absolute highest accuracy but more on the interpretation of what the Machine Learning models provides. We find that age , days in hospital , Lymphocyte and Neutrophils are important and robust predictors when predicting a patients mortality. Furthermore, the algorithms we use allows us to observe the marginal impact of each variable on a case-by-case patient level, which might help practicioneers to easily detect anomalous patterns. This paper analyses the global and local interpretation of the Machine Learning models on patients with COVID19. [ABSTRACT FROM AUTHOR]

The COVID-19 prevention and control measures are taken by China's government, especially traffic restrictions and production suspension, had spillover effects on air quality improvement. These effects differed among cities, but these differences have not been adequately studied. To provide more knowledge, we studied the air quality index (AQI) and five air pollutants (PM2.5, PM10, SO2, NO2, and O3) before and after the COVID-19 outbreak in Shanghai, Wuhan, and Tangshan. The pollution data from two types of monitoring stations (traffic and non-traffic stations) were separately compared and evaluated. We used monitoring data from the traffic stations to study the emission reduction caused by traffic restrictions. Based on monitoring data from the non-traffic stations, we established a difference-in-difference model to study the emission reduction caused by production suspension. The COVID-19 control measures reduced AQI and the concentrations of all pollutants except O3 (which increased greatly), but the magnitude of the changes differed among the three cities. The control measures improved air quality most in Wuhan, followed by Shanghai and then Tangshan. We investigated the reasons for these differences and found that differences in the characteristics of these three types of cities could explain these differences in spillover effects. Understanding these differences could provide some guidance and support for formulating differentiated air pollution control measures in different cities. For example, whole-process emission reduction technology should be adopted in cities with the concentrated distribution of continuous process enterprises, whereas vehicles that use cleaner energy and public transport should be vigorously promoted in cities with high traffic development level. [ABSTRACT FROM AUTHOR]

The SARS-CoV-2 pandemic demonstrated the need for potent and broad-spectrum vaccines. This study reports the development and testing of a lumpy skin disease virus (LSDV)-vectored vaccine against SARS-CoV-2, utilizing stabilized spike and conserved nucleocapsid proteins as antigens to develop robust immunogenicity. Construction of the vaccine (LSDV-SARS2-S,N) was confirmed by polymerase chain reaction (PCR) amplification and sequencing. In vitro characterization confirmed that cells infected with LSDV-SARS2-S,N expressed SARS-CoV-2 spike and nucleocapsid protein. In BALB/c mice, the vaccine elicited high magnitude IFN-γ ELISpot responses (spike: 2808 SFU/106 splenocytes) and neutralizing antibodies (ID50 = 6552). Testing in hamsters, which emulate human COVID-19 disease progression, showed the development of high titers of neutralizing antibodies against the Wuhan and Delta SARS-CoV-2 variants (Wuhan ID50 = 2905; Delta ID50 = 4648). Additionally, hamsters vaccinated with LSDV-SARS2-S,N displayed significantly less weight loss, lung damage, and reduced viral RNA copies following SARS-CoV-2 infection with the Delta variant as compared to controls, demonstrating protection against disease. These data demonstrate that LSDV-vectored vaccines display promise as an effective SARS-CoV-2 vaccine and as a potential vaccine platform for communicable diseases in humans and animals. Further efficacy testing and immune response analysis, particularly in non-human primates, are warranted. [ABSTRACT FROM AUTHOR]

The Coronavirus Disease 2019 (COVID-19) pandemic was declared in early 2020 after several unexplained pneumonia cases were first reported in Wuhan, China, and subsequently in other parts of the world. Commonly, the disease comprises several clinical features, including high temperature, dry cough, shortness of breath, and hypoxia, associated with findings of interstitial pneumonia on chest X-ray and computer tomography. Nevertheless, severe forms of acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) are not limited to the respiratory tract but also may be extended to other systems, including the cardiovascular system. The bi-directional relationship between atherosclerosis and COVID-19 is accompanied by poor prognosis. The immune response hyperactivation due to SARS-CoV-2 infection causes an increased secretion of cytokines, endothelial dysfunction, and arterial stiffness, which promotes the development of atherosclerosis. Also, due to the COVID-19 pandemic, access to healthcare amenities was reduced, resulting in increased morbidity and mortality in patients at risk. Furthermore, as lockdown measures were largely adopted worldwide, the sedentary lifestyle and the increased consumption of processed nutrients or unhealthy food increased, and in the consequence, we might observe even 70% of overweight and obese population. Altogether, with the relatively low ratio of vaccinated people in many countries, and important health debt appeared, which is now and will be for next decade a large healthcare challenge. However, the experience gained in the COVID-19 pandemic and the new methods of patients' approaching have helped the medical system to overcome this crisis and will hopefully help in the case of new possible epidemics. [ABSTRACT FROM AUTHOR]

Background and objective: Corona virus causes respiratory tract infections in mammals. The latest type of Severe Acute Respiratory Syndrome Corona-viruses 2 (SARS-CoV-2), Corona virus spread in humans in December 2019 in Wuhan, China. The purpose of this study was to investigate the relationship between type 2 diabetes mellitus (T2DM), and their biochemical and hematological factors with the level of infection with COVID-19 to improve the treatment and management of the disease. Material and method: This study was conducted on a population of 13,170 including 5780 subjects with SARS-COV-2 and 7390 subjects without SARS-COV-2, in the age range of 35–65 years. Also, the associations between biochemical factors, hematological factors, physical activity level (PAL), age, sex, and smoking status were investigated with the COVID-19 infection. Result: Data mining techniques such as logistic regression (LR) and decision tree (DT) algorithms were used to analyze the data. The results using the LR model showed that in biochemical factors (Model I) creatine phosphokinase (CPK) (OR: 1.006 CI 95% (1.006,1.007)), blood urea nitrogen (BUN) (OR: 1.039 CI 95% (1.033, 1.047)) and in hematological factors (Model II) mean platelet volume (MVP) (OR: 1.546 CI 95% (1.470, 1.628)) were significant factors associated with COVID-19 infection. Using the DT model, CPK, BUN, and MPV were the most important variables. Also, after adjustment for confounding factors, subjects with T2DM had higher risk for COVID-19 infection. Conclusion: There was a significant association between CPK, BUN, MPV and T2DM with COVID-19 infection and T2DM appears to be important in the development of COVID-19 infection. [ABSTRACT FROM AUTHOR]

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SARS-CoV-2 is the causal agent of COVID-19; the first report of SARS-CoV-2 infection was in December 2019 in Wuhan, China. This virus has since caused the largest pandemic in history, and the number of deaths and infections has been significant. Nevertheless, the development of vaccines has helped to reduce both deaths and infections. Comorbidities such as diabetes, hypertension, heart and lung diseases, and obesity have been identified as additional risk factors for infection and the progression of COVID-19. Additionally, latent toxoplasmosis has been reported to be a risk factor for acquiring COVID-19 in some studies, but other studies have suggested a negative association between these two infections. Furthermore, in patients after vaccination or with COVID-19 and coinfection, an increase in the lethality and mortality of toxoplasmosis has been observed. Therefore, the objective of the current study is to determine the association of toxoplasmosis with COVID-19 in patients diagnosed with COVID-19. Serum samples from 384 patients previously diagnosed with COVID-19 using IgG antibodies against the S1/S2 antigens of SARS-CoV-2 were collected. Subsequently, anti-Toxoplasma IgG and IgM antibodies were analyzed with ELISA. Statistical analysis was performed using SPSS Version 20.0 frequencies, percentages, 2 × 2 tables, and the Pearson correlation coefficient. IgG and IgM anti-Toxoplasma antibodies were positive in 105/384 (27.34%) and (26/191) 13.6% of patients, respectively. The positivity for both infections was higher in patients aged >40 years old. Subjects who were overweight or obese were mainly positive for both IgG antibodies against S1/S2 SARS-CoV-2 and Toxoplasma antibodies. In conclusion, the coinfection rate was 21.7%. The prevalence of S1/S2 SARS-CoV-2 was 308/384 (80.2%), and the percentage of Toxoplasma antibodies was 27.34%. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak slowed down global economic activities substantially, resulting in unrest in the financial markets, especially in the beginning of the pandemic outbreak. This study aims to investigate if COVID-19 caused abnormal returns in the US and the Chinese stock markets in the beginning of the pandemic outbreak. Event study methodology is adopted for this purpose. This study finds that a significant negative impact appeared immediately after the Wuhan lockdown in the Chinese markets, while the US markets were slow to pick up. The former was more severely hurt after lockdown, while the latter was more brutally affected after COVID-19 was labeled a global threat. COVID-19 also played a significant role in connecting these two stock markets. The US and China should collaborate further in combating this novel and notorious global pandemic. [ABSTRACT FROM AUTHOR]

Objective: Telerehabilitation and telemedicine have gradually gained popularity. In 2019, the outbreak of COVID-19 started in Wuhan and then spread across the world. To date, most countries have opted to coexist with the virus. However, patients, especially those who have su ered a stroke, should take measures to avoid being infected with any disease as much as possible since any infectious disease can lead to adverse events for them. Telerehabilitation can be beneficial to stroke patients as they are less likely to be infected by the virus. In recent years, several studies on telerehabilitation have been conducted globally. This meta-analysis aimed to investigate the effects of telerehabilitation on the balance ability of stroke patients, compare the efficacy of conventional rehabilitation with telerehabilitation, explore the characteristics of telerehabilitation and conventional rehabilitation, and provide recommendations for rehabilitation programs in the context of the global pandemic. Methods: We searched Pubmed, Embase, theWeb of Science, and The Cochrane Library databases from 1 January 2020 to 31 December 2022 for randomized controlled trials published in English that evaluated the improvement of balance function in stroke patients after telerehabilitation and compared the di erences between telerehabilitation (TR) and conventional rehabilitation (CR). The random- e ects model was utilized to calculate mean di erences (MDs) with 95% confidence intervals (CIs) to estimate intervention e ects. Statistical heterogeneity was assessed according to the I² values. The risk of bias was measured using the Cochrane risk-of-bias assessment tool. Results: We included nine studies in the system evaluation, all of which were included in the pooled analysis. All outcomes in the experimental and control groups improved over time. The comparison between groups concluded that people who received the telerehabilitation intervention had a significant improvement in the Berg Balance Scale (MD = 2.80; 95% CI 0.61, 4.98, P < 0.05, I2 = 51.90%) and the Fugl-Meyer Assessment (MD = 8.12; 95% CI 6.35, 9.88, P < 0.05, I2 = 0) compared to controls. The Timed Up and Go test (MD = -4.59; 95% CI -5.93, -.25, P < 0.05, I² = 0) and Tinetti Performance-Oriented Mobility Assessment--Balance (MD = 2.50; 95% CI 0.39, 4.61, P < 0.05) scored better in the control group than in the experimental group. There were no significant differences in other outcomes between the two groups. Conclusion: Studies on changes in medical conditions during the COVID-19 pandemic also demonstrated that, for stroke patients, telerehabilitation achieves similar effects as the conventional rehabilitation model and can act as a continuation of the conventional rehabilitation model. Owing to the different equipment and intervention programs of telerehabilitation, its curative effect on the static balance and reactive balance of stroke patients may be different. Currently, telerehabilitation may be more conducive to the rehabilitation of patients' static balance abilities, while conventional rehabilitation is more effective for the rehabilitation of patients' reactive balance. Therefore, further studies are needed for investigating the di erence in efficacy between varied devices and telerehabilitation programs. Further research is needed on static and reactive balance. In addition, such research should have a large body of literature and a large sample size to support more definitive findings based on the context of the COVID-19 pandemic. Systematic review registration: CRD42023389456. [ABSTRACT FROM AUTHOR]

When an epidemic started in the Chinese city of Wuhan in December 2019, coronavirus was identified as the cause. Infection by the virus occurs through the interaction of viral S protein with the hosts' angiotensin-converting enzyme 2. By leveraging resources such as the DrugBank database and bioinformatics techniques, ligands with potential activity against the SARS-CoV-2 spike protein were designed and identified in this investigation. The FTMap server and the Molegro software were used to determine the active site of the Spike-ACE2 protein's crystal structure. Virtual screening was performed using a pharmacophore model obtained from antiparasitic drugs, obtaining 2000 molecules from molport®. The ADME/Tox profiles were used to identify the most promising compounds with desirable drug characteristics. The binding affinity investigation was then conducted with selected candidates. A molecular docking study showed five structures with better binding affinity than hydroxychloroquine. Ligand_003 showed a binding affinity of −8.645 kcal·mol−1, which was considered an optimal value for the study. The values presented by ligand_033, ligand_013, ligand_044, and ligand_080 meet the profile of novel drugs. To choose compounds with favorable potential for synthesis, synthetic accessibility studies and similarity analyses were carried out. Molecular dynamics and theoretical IC50 values (ranging from 0.459 to 2.371 µM) demonstrate that these candidates are promising for further tests. Chemical descriptors showed that the candidates had strong molecule stability. Theoretical analyses here show that these molecules have potential as SARS-CoV-2 antivirals and therefore warrant further investigation. [ABSTRACT FROM AUTHOR]

The outbreak of the severe acute respiratory syndrome coronavirus 2 started in Wuhan, China, towards the end of 2019 and spread worldwide. The rapid spread of the disease can be attributed to many factors including its high infectiousness and the high rate of human mobility around the world. Although travel/movement restrictions and other non-pharmaceutical interventions aimed at controlling the disease spread were put in place during the early stages of the pandemic, these interventions did not stop COVID-19 spread. To better understand the impact of human mobility on the spread of COVID-19 between regions, we propose a hybrid gravity-metapopulation model of COVID-19. Our modeling framework has the flexibility of determining mobility between regions based on the distances between the regions or using data from mobile devices. In addition, our model explicitly incorporates time-dependent human mobility into the disease transmission rate, and has the potential to incorporate other factors that affect disease transmission such as facemasks, physical distancing, contact rates, etc. An important feature of this modeling framework is its ability to independently assess the contribution of each factor to disease transmission. Using a Bayesian hierarchical modeling framework, we calibrate our model to the weekly reported cases of COVID-19 in thirteen local health areas in Metro Vancouver, British Columbia (BC), Canada, from July 2020 to January 2021. We consider two main scenarios in our model calibration: using a fixed distance matrix and time-dependent weekly mobility matrices. We found that the distance matrix provides a better fit to the data, whilst the mobility matrices have the ability to explain the variance in transmission between regions. This result shows that the mobility data provides more information in terms of disease transmission than the distances between the regions. Author summary: Mathematical modelling was at the forefront of the fight against COVID-19. Many models were used to study the disease dynamics and the effectiveness of intervention strategies to control the disease spread. Due to the high infectiousness of COVID-19 and the high rate of human mobility, understanding the disease spread between regions became important. Many of the previously developed models for studying disease dynamics between regions described human mobility based on either the distances between the regions or using other forms of mobility data. In addition, some of these models can only be used to study the spread of COVID-19 from an epicentre to neighbouring regions/cities. These models are suitable for only the early stages of the disease outbreak. We developed a hybrid gravity-metapopulation modelling framework for studying the spread of diseases between regions. Our model provides the flexibility of using the distances between the regions and mobile device data as a proxy for human mobility between regions. Furthermore, our framework is suitable for studying disease dynamics at any epidemic stage. It accounts for disease spread from each region to the remaining regions, irrespective of the number of reported cases in each region and their population sizes. [ABSTRACT FROM AUTHOR]

The outbreak of multiple disaster sites during the coronavirus disease 2019 (COVID-19) pandemic has presented challenges due to varying access time intensity, population density, and medical resources at each site. To address these issues, this study focuses on 13 districts and counties in Wuhan, China. The importance of each research area is analyzed using the improved PageRank and TOPSIS algorithms to determine the optimal site selection plan. Additionally, a particle swarm algorithm is used to construct an emergency material dispatching model that targets both distribution and site selection costs to solve the multi-distribution center dispatching problem. The results suggest that constructing 10 distribution centers can satisfy the demand for epidemic prevention and control in Wuhan city while saving costs associated with site selection and material distribution. Compared to the previous optimal solution, the distribution and site selection costs under the optimal solution decreased by 27.9% and 17.82%, respectively. This approach can serve as a basis for dispatching emergency materials during public health emergencies. [ABSTRACT FROM AUTHOR]

Background: During the ongoing COVID-19 pandemic, the resident's WeChat group has created a new material foundation for dialogue to occur and become a powerful platform for resident communication. This study explores the mechanism behind and the effects of residents' WeChat group use on residents' community trust, community attachment, and pro-community behavior. Methods: An online survey questionnaire was used for data collection. The authors collected data from 500 commercial housing community residents in Wuhan, China, and analyzed the data using SPSS 26.0 and Mplus 8.3 software. Results: This study's findings uncover that (1) residents' usage of WeChat groups has a statistically significant and positive impact on their community trust, community attachment, and pro-community behavior; (2) community trust and community attachment both play a mediating role in the mechanism behind residents' usage of WeChat groups in improving pro-community behavior; and (3) the transmission and united effects between community trust and community attachment form a distal mediating role. Conclusion: The model systematically and comprehensively reveals the internal mechanism behind residents' adoption of pro-community behavior. Community managers can actively participate in the resident's WeChat group to ensure the dissemination of positive information in the community; enhance residents' awareness of risk, community trust, and belonging; and cultivate community resilience. At the same time, community managers should also fully recognize the important transformative roles that community trust and community belonging play between the use of WeChat groups by residents and the formation of pro-community behavior. Community managers should actively establish a warm and trusting community culture, strive to create a community atmosphere with a sense of belonging, make residents develop emotional attachment to the community, and then form behavior that is beneficial to the community, greatly enhancing the resilience and self-management of the community in disaster situations. [ABSTRACT FROM AUTHOR]

An outbreak of coronavirus disease (COVID-19) in Wuhan, China caused by SARS-CoV-2 has led to a serious epidemic in China and other countries, resulting in worldwide concern. With active efforts of prevention and control, more and more patients are being discharged. However, how to manage these patients normatively is still challenging. This paper reports an asymptomatic discharged patient with COVID-19 who retested positive for SARS-CoV-2, which arouses concern regarding the present discharge standards of COVID-19. [ABSTRACT FROM AUTHOR]

The COVID-19 epidemic has had a huge impact on the mental state of human beings due to its high infection and fatality rates in early 2020. In this paper, a cross-sectional online survey was designed to understand the mental state of college students in a university located in Wuhan city during the lockdown. Out of 1168 respondents, above 50% participants had obvious fear and anxiety symptoms; anxiety and fear were 61.64% and 58.39%, respectively. Conformity (49.49%), invulnerability (26.11%), insensitivity (21.49%) and rebelliousness (12.41%) symptoms also appeared. Meanwhile, it was revealed that the senior students experienced more anxiety than the freshmen. Moreover, it was found that the psychological symptoms (except for the insensitivity symptom) had no significant difference in gender, residence and annual household income after the one-way analysis of variance. [ABSTRACT FROM AUTHOR]

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]

COVID-19 was first identified in December 2019 in Wuhan, China. It mainly affects the respiratory system and can lead to the death of the patient. The motivation for this study was the current pandemic situation and general deficiency of emergency mechanical ventilators. The paper presents the development of a mechanical ventilator and its control algorithm. The main feature of the developed mechanical ventilator is AmbuBag compressed by a pneumatic actuator. The control algorithm is based on an adaptive neuro-fuzzy inference system (ANFIS), which integrates both neural networks and fuzzy logic principles. Mechanical design and hardware design are presented in the paper. Subsequently, there is a description of the process of data collecting and training of the fuzzy controller. The paper also presents a simulation model for verification of the designed control approach. The experimental results provide the verification of the designed control system. The novelty of the paper is, on the one hand, an implementation of the ANFIS controller for AmbuBag pressure control, with a description of training process. On other hand, the paper presents a novel design of a mechanical ventilator, with a detailed description of the hardware and control system. The last contribution of the paper lies in the mathematical and experimental description of AmbuBag for ventilation purposes. [ABSTRACT FROM AUTHOR]

In this paper we evaluate the operational efficiency of the Brazilian airlines considering the novel coronavirus Covid-19 outbreak. This novel coronavirus was first reported end of 2019 in Wuhan, China, however the powerful contamination spread among people forced the World Health Organization to characterize the Covid-19 as a pandemic in March of 2020. Here we analyze the main Brazilian airlines operations response due to lower demand because Covid-19 outbreak in first quarter of 2020 comparing with first quarter of 2019. The analysis here aims to verify the efficiency of airlines in domestic air transport market in Brazil through Multicriteria Data Envelopment Analysis (MCDEA) model. We used MCDEA to avoid limitations of classical DEA models for the case, especially the numbers of decision units and variable. In this paper we used an improvement of the MCDEA model to seek benchmarks considering a dual model all objective functions of MCDEA. The results highlight the challenges for the airlines, due to flight restriction and demand dropping. And also, the evaluation exposes the different company configuration of aircrafts age and network reconfiguration which was reflected by the efficiency difference on the period. The assessment shows the company with a better mix of aircraft models has a leverage on efficiency response due to unpredictable period as the pandemic Covid-19 outbreak. • Air transport largely facilitates diseases spread worldwide. • Analysis of Brazilian airlines during the Covid-19 outbreak. • Assessment considers the Multicriteria Data Envelopment Analysis (MCDEA) model. • Improvement of the MCDEA model to seek benchmarks based in a duality formulation. • Efficiency challenges for the airlines to outbrave the Covid-19. [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]

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]

Background: Wuhan was the epicenter of the Coronavirus Disease 2019 (COVID-19), outbreak in China. We aimed at surveying the general public in China to better understand their levels of psychological state and its influencing factors after the Wuhan shutdown on January 23. Methods: The cross-sectional survey was conducted online and 4,701 respondents participated in this survey. Of them, 3,803 respondents were considered for final analysis. Data on subjective indicators of daily life changes were collected, and individual scores on changes in anxiety, depression, and stress were tested by 8-item, 11-item, and 6-item questionnaires, respectively. Results: Multivariable regression analyses showed that living in the rural areas, living in the other regions except Hubei, and higher education were independent correlates of less negative emotions. Besides, the level of attention, self-assessed infection risk, impact on the daily life and mental health help-seeking tended to be positively associated with the scores of anxiety, depression, and stress. Conclusions: City of residence, education, marital status, monthly income, level of attention, self-assessed infection risk, impact on daily life and mental health help-seeking were important correlates of anxiety, depression, and stress scores. [ABSTRACT FROM AUTHOR]

A consensus species tree is reconstructed from 11 gene trees for human, bat, and pangolin beta coronaviruses from samples taken early in the pandemic (prior to April 1, 2020). Using coalescent theory, the shallow (short branches relative to the hosts) consensus species tree provides evidence of recent gene flow events between bat and pangolin beta coronaviruses predating the zoonotic transfer to humans. The consensus species tree was also used to reconstruct the ancestral sequence of human SARS-CoV-2, which was 2 nucleotides different from the Wuhan sequence. The time to most recent common ancestor was estimated to be Dec 8, 2019 with a bat origin. Some human, bat, and pangolin coronavirus lineages found in China are phylogenetically distinct, a rare example of a class II phylogeography pattern (Avise et al. in Ann Rev Eco Syst 18:489–422, 1987). The consensus species tree is a product of evolutionary factors, providing evidence of repeated zoonotic transfers between bat and pangolin as a reservoir for future zoonotic transfers to humans. [ABSTRACT FROM AUTHOR]

In December 2019, the COVID-19 virus was first detected in Wuhan, Hebei Province, China. On 11 March 2020, the World Health Organisation declared the COVID-19 virus a world pandemic. Even though the physical closure of tertiary institutions had proven to be an effective strategy in breaking the transmission chain in the pandemic, the closure still had various negative effects on students' academic endeavours. In an attempt to keep the disruption of studies to a minimum, higher education institutions across the world implemented online learning programmes by converting face-to-face programmes, thereby creating various challenges for students and lecturers on different levels. The purpose of this article was to determine how challenges faced by multi-campus universities in South Africa during this time would compare to challenges faced by other countries. This study focused on two specific multi-campus universities in South Africa with similar student bodies that offered various accounting courses on different campuses. These courses ranged from diplomas to CTA degrees. [ABSTRACT FROM AUTHOR]

A highly contagious virus, severe acute respiratory syndrome coronavirus 2, caused the coronavirus disease 19 (COVID-19) pandemic (SARS-CoV-2). SARS-CoV-2 genetic variants have been reported to circulate throughout the COVID-19 pandemic. COVID-19 symptoms include respiratory symptoms, fever, muscle pain, and breathing difficulty. In addition, up to 30% of COVID-19 patients experience neurological complications such as headaches, nausea, stroke, and anosmia. However, the neurotropism of SARS-CoV-2 infection remains largely unknown. This study investigated the neurotropic patterns between the B1.617.2 (Delta) and Hu-1 variants (Wuhan, early strain) in K18-hACE2 mice. Despite both the variants inducing similar pathogenic patterns in various organs, B1.617.2-infected K18-hACE2 mice demonstrated a higher range of disease phenotypes such as weight loss, lethality, and conjunctivitis when compared to those in Hu-1-infected mice. In addition, histopathological analysis revealed that B1.617.2 infects the brain of K18-hACE2 mice more rapidly and effectively than Hu-1. Finally, we discovered that, in B1.617.2-infected mice, the early activation of various signature genes involved innate cytokines and that the necrosis-related response was most pronounced than that in Hu-1-infected mice. The present findings indicate the neuroinvasive properties of SARS-CoV-2 variants in K18-hACE2 mice and link them to fatal neuro-dissemination during the disease onset. [ABSTRACT FROM AUTHOR]

Almost all published rooting and dating studies on SARS-CoV-2 assumed that (1) evolutionary rate does not change over time although different lineages can have different evolutionary rates (uncorrelated relaxed clock), and (2) a zoonotic transmission occurred in Wuhan and the culprit was immediately captured, so that only the SARS-CoV-2 genomes obtained in 2019 and the first few months of 2020 (resulting from the first wave of the global expansion from Wuhan) are sufficient for dating the common ancestor. Empirical data contradict the first assumption. The second assumption is not warranted because mounting evidence suggests the presence of early SARS-CoV-2 lineages cocirculating with the Wuhan strains. Large trees with SARS-CoV-2 genomes beyond the first few months are needed to increase the likelihood of finding SARS-CoV-2 lineages that might have originated at the same time as (or even before) those early Wuhan strains. I extended a previously published rapid rooting method to model evolutionary rate as a linear function instead of a constant. This substantially improves the dating of the common ancestor of sampled SARS-CoV-2 genomes. Based on two large trees with 83,688 and 970,777 high-quality and full-length SARS-CoV-2 genomes that contain complete sample collection dates, the common ancestor was dated to 12 June 2019 and 7 July 2019 with the two trees, respectively. The two data sets would give dramatically different or even absurd estimates if the rate was treated as a constant. The large trees were also crucial for overcoming the high rate-heterogeneity among different viral lineages. The improved method was implemented in the software TRAD. [ABSTRACT FROM AUTHOR]

Pekar et al. (2022) propose that SARS-CoV-2 was a zoonotic spillover that first infected humans in the Huanan Seafood Market in Wuhan, China. They propose that there were two separate spillovers of the closely related lineages A and lineage B in a short period of time. The two lineages are differentiated by two SNVs; hence, a single-SNV A-B intermediate must have occurred in an unsampled animal host if the two-spillover hypothesis is correct. Consequently, confirmation of the existence of an intermediate A-B genome from humans would falsify their hypothesis of two spillovers. Pekar et al. identified and excluded 20 A-B intermediate genomes from their analysis. A variety of exclusion criteria were applied, including low read depth and the assertion of repeated erroneous base calls at lineage-defining positions 8782 and 28144. However, data from GISAID show that most of the genomes were sequenced to high average sequencing depth, appearing inconsistent with these criteria. The decision to exclude the majority of genomes was based on personal communications, with raw data unavailable for inspection. Multiple errors, biases, and inconsistencies were observed in the exclusion process. For example, 12 intermediate genomes from one study were excluded; however, 54 other genomes from the same study were included, indicating selection bias. Puzzlingly, two intermediate genomes from Beijing were discarded despite an average sequencing depth of 2175X; however, four genomes from the same sequencing study were included in the analysis. Lastly, we discuss 14 additional possible intermediate genomes not discussed by Pekar et al. and note that genome sequence filtration is inappropriate when considering the presence or absence of a specific SNV pair in an outbreak. Consequently, we find that the exclusion of many of the intermediate genomes is unfounded, leaving the conclusion of two natural zoonoses unsupported. [ABSTRACT FROM AUTHOR]

SARS-COV-2 is responsible for the current worldwide pandemic, which started on December 2019 in Wuhan, China. On March 2020 World Health Organization announced COVID-19 as the new pandemic. Some SARS-COV-2 variants have increased transmissibility, cause more severe disease (e.g., increased hospitalizations or deaths), are resistant to antibodies produced by the previous infection or vaccination, and there is more difficulty in treatment and diagnosis of them. World Health Organization considered them as SARS-CoV-2 variants of concern. The introductory reproduction rate (R0) is an epidemiologic index of the transmissibility of the virus, defined as the average number of persons infected by the virus after known contact with an infectious person in a susceptible population. An R0 > 1 means that the virus is spreading exponentially, and R0 < 1, means that the outbreak is subsiding. In various studies, the estimated R and VOC growth rates were reported to be greater than the ancestral strains. However, it was also a low level of concordance between the estimated Rt of the same variant in different studies. It is because the R of a variant not only dependent on the biological and intrinsic factors of the virus but also several parameters can affect the R0, including the duration of contagiousness and the likelihood of infection per contact. Evaluation of changes in SARS-CoV-2 has shown that the rate of human-to-human transmission of this virus has increased. Like other viruses with non-human sources which succeeded in surviving in the human population, SARS-CoV-2 has gradually adapted to the human population, and its ability to transmit from human to human has increased. Of course, due to the continuous changes in this virus, it is crucial to survey the rate of transmission of the virus over time. [ABSTRACT FROM AUTHOR]

The seventh human coronavirus was discovered and reported primarily in Wuhan, China. After intense seasons with repercussions in all areas of humanity, the pandemic demonstrates a new perspective. In Brazil, the pandemic concept had impacts in vast areas, including healthcare hospitals. This present study aims to describe and synthesize data from a determined period from the year 2021 that correlate the symptoms of passive and/or active patients for COVID-19 and their respective results of IgG/IgM serological tests in hospitals in the city of Cruzeiro, São Paulo, Brazil. The form had been applied to 333 people and obtained conclusive results and several symptoms were presented; in addition, asymptomatic cases were also analyzed and directed in the genomic study of variants of concern, as well as vaccination data in the study region. [ABSTRACT FROM AUTHOR]

Background and Objectives: Several vaccines have been approved for the prevention of the coronavirus disease, discovered on 31 December in Wuhan, China. Pakistan procured vaccines from various countries. However, the lack of knowledge and reluctance of the general population to embrace the use of the vaccines are considered to be the major determinant of the slow vaccination rate. Hence, it is necessary to evaluate the willingness of the general population about their perception of the COVID-19 vaccination. Materials and Methods: A cross sectional survey based on a self-structured questionnaire comprising 18 questions was conducted (from 21 April–21 June) on 400 Pakistani participants to evaluate their knowledge, attitude, and perception towards the COVID-19 vaccination. Chi-square independent t-test and one-way Anova including a multiple step wise linear regression were used to draw conclusions about the results. p < 0.05 was considered significant. Results: A total of 400 participants responded in the knowledge, attitude, and perception (KAP) survey of which 46.5% were female and 53.5% were male. The mean age of participants was 36.08 years. This survey showed a poor knowledge (50.5%), a fair attitude (75.1%) and a poor perception (58.1%) towards the COVID-19 vaccination. Higher mean knowledge and attitude scores were reported in the age group 21–40, females, and unmarried urban citizens. Regression analysis showed that age, education, residence, and employment status influenced the knowledge and perception score to a considerable extent. Conclusions: The findings reflect an inadequate knowledge and perception on the one hand, but a better attitude towards the COVID-19 vaccination. This knowledge attitude and perception (KAP) survey will help in better understanding the opinion of the general population towards vaccination, and will be useful for policy makers and health care authorities aiming to increase the vaccination rate. [ABSTRACT FROM AUTHOR]