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This study attempts to examine the response of stock markets amid the COVID-19 pandemic on prominent stock markets of the BRICS nation and compare it with the 2008 financial crisis by employing the GARCH and EGARCH model. First, average and variance of stock returns are tested for differences before and after the pandemic, t-test and F-test were applied. Further, OLS regression was applied to study the impact of COVID-19 on the standard deviation of returns using daily data of total cases, total deaths, and returns of the indices from the date on which the first case was reported till June 2020. Second, GARCH and EGARCH models are employed to compare the impact of COVID-19 and the 2008 financial crisis on the stock market volatility by using the data of respective stock indices for the period 2005–2020. The results suggest that the increasing number of COVID-19 cases and reported death cases hurt stock markets of the five countries except for South Africa in the latter case. The findings of the GARCH and EGARCH model indicate that for India and Russia, the financial crisis of 2008 has caused more stock volatility whereas stock markets of China, Brazil, and South Africa have been more volatile during the COVID-19 pandemic. The study has practical implications for investors, portfolio managers, institutional investors, regulatory institutions, and policymakers as it provides an understanding of stock market behavior in response to a major global crisis and helps them in taking decisions considering the risk of these events.

The Susceptible, Infected and Recover (SIR) model is a very simple model to estimate the dynamics of an epidemic. In the current pandemic due to Covid-19, the SIR model has been used to estimate the dynamics of infection for various infected countries. Numerical solutions are used to obtain the value of parameters for the SIR model. The maximum and minimum basic reproduction number (14.5 and 2.3) are predicted to be in Turkey and China, respectively.

The Susceptible, Infected and Recover (SIR) model is a very simple model to estimate the dynamics of an epidemic. In the current pandemic due to Covid-19, the SIR model has been used to estimate the dynamics of infection for Bangladesh, India, Pakistan and compared with that of China. Numerical solutions are used to obtain the value of parameters for the SIR model. It is predicted that the active case in Pakistan due to the SARS-CoV-2 will be comparable with that in China whereas it will be low for Bangladesh and India. The basic reproduction number, with fluctuations, for South Asian countries are predicted to be less than that of China. The susceptible population is also estimated to be under a million for Bangladesh and India but it becomes very large for Pakistan.

COVID-19 has been declared a pandemic by WHO on March 11, 2020. No specific treatment and vaccine with documented safety and efficacy for the disease have been established. Hence it is of utmost importance to identify more therapeutics such as Chinese medicine formulae to meet the urgent need. Qing Fei Pai Du Tang (QFPDT), a Chinese medicine formula consisting of 21 herbs from five classical formulae has been reported to be efficacious on COVID-19 in 10 provinces in mainland China. QFPDT could prevent the progression from mild cases and shorten the average duration of symptoms and hospital stay. It has been recommended in the 6th and 7th versions of Clinical Practice Guideline on COVID-19 in China. The basic scientific studies, supported by network pharmacology, on the possible therapeutic targets of QFPDT and its constituent herbs including Ephedra sinica, Bupleurum chinense, Pogostemon cablin, Cinnamomum cassia, Scutellaria baicalensis were reviewed. The anti-oxidation, immuno-modulation and antiviral mechanisms through different pathways were collated. Two clusters of actions identified were cytokine storm prevention and angiotensin converting enzyme 2 (ACE2) receptor binding regulation. The multi-target mechanisms of QFPDT for treating viral infection in general and COVID-19 in particular were validated. While large scale clinical studies on QFPDT are being conducted in China, one should use real world data for exploration of integrative treatment with inclusion of pharmacokinetic, pharmacodynamic and herb-drug interaction studies.

This study aimed to investigate the efficacy of Traditional Chinese Medicine (TCM) decoction with different intervention timepoints in the treatment of coronavirus disease 2019 (COVID-19) patients. We retrospectively collected the medical records and evaluated the outcomes of COVID-19 patients that received TCM decoction treatment at different timepoints. A total of 234 COVID-19 patients were included in this study. Patients who received TCM decoction therapy within 3 days or 7 days after admission could achieve shorter hospitalization days and disease periods compared to those who received TCM decoction $\ge$ 7 days after admission (all $p<0.05$). Patients who received TCM decoction therapy within 3 days had significantly fewer days to negative SARS-CoV-2 from nasopharyngeal/oral swab and days to negative SARS-CoV-2 from urine/stool/blood samples compared to those received TCM decoction $\ge 7$ days after admission (all $p<0.05$). Patients who received TCM decoction therapy on the 3rd to 7th day after admission had a faster achievement of negative SARS-CoV-2 from urine/stool/blood samples compared to those who received TCM decoction $\ge 7$ days after admission ($p<0.05$). Logistic models revealed that more days from TCM decoction to admission $\ge 7$ days might be a risk factor for long hospitalization days, disease period, and slower negative-conversion of SARS-CoV-2 (all $p<0.01$). Conclusively, our results suggest that TCM decoction therapy should be considered at the early stage of COVID-19 patients.

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.

The goal of our research is to establish the direction of coronavirus chaotic motion to control corona dynamic by fractal nature analysis. These microorganisms attaching the different cells and organs in the human body getting very dangerous because we don’t have corona antivirus prevention and protection but also the unpredictable these viruses motion directions what resulting in very important distractions. Our idea is to develop the method and procedure to control the virus motion direction with the intention to prognose on which cells and organs could attach. We combined very rear coronavirus motion sub-microstructures images from worldwide experimental microstructure analysis. The problem of the recording this motion is from one point of view magnification, but the other side in resolution, because the virus size is minimum 10 times less than bacterizes. But all these images have been good data to resolve by time interval method and fractals, the points on the motion trajectory. We successfully defined the diagrams on the way to establish control over Brownian chaotic motion as a bridge between chaotic disorder to control disorder. This opens a very new perspective to future research to get complete control of coronavirus cases.

COVID-19 is known in recent times as a severe syndrome of respiratory organ (Lungs) and has gradually produced pneumonia, a lung disorder all around the world. As coronavirus is continually spreading rapidly globally, the computed tomography (CT) technique has been made important and essential for quick diagnosis of this dangerous syndrome. Hence, it is necessitated to develop a precise computer-based technique for assisting medical clinicians in identifying the COVID-19 influenced patients with the help of CT scan images. Therefore, the multilayer perceptron neural networks optimized with Garra Rufa Fish optimization using images of CT scan is proposed in this paper for the classification of COVID-19 patients (COV-19-MPNN-GRF-CTI). The input images are taken from SARS-COV-2 CT-scan dataset. Initially, the input images are pre-processed utilizing convolutional auto-encoder (CAE) to enhance the quality of the input images by eliminating noises. The pre-processed images are fed to Residual Network (ResNet-50) for extracting the global and statistical features. The extraction over the features of CT scan images is made through ResNet-50 and subsequently input to multilayer perceptron neural networks (MPNN) for CT images classification as COVID-19 and Non-COVID-19 patients. Here, the layer of Batch Normalization of the MPNN is separated and added with ResNet-50 layer. Generally, MPNN classifier does not divulge any adoption of optimization approach for calculating the optimal parameters and accurately classifying the extracted features of CT images. The Garra Rufa Fish (GRF) optimization algorithm performs to optimize the weight parameters of MPNN classifiers. The proposed approach is executed in MATLAB. The performance metrics, such as sensitivity, precision, specificity, $F$-measure, accuracy and error rate, are examined. Then the performance of the proposed COV-19-MPNN-GRF-CTI method provides 22.08%, 24.03%, 34.76% higher accuracy, 23.34%, 26.45%, 34.44% higher precision, 33.98%, 21.95%, 34.78% lower error rate compared with the existing methods, like multi-task deep learning using CT image analysis for COVID-19 pneumonia classification and segmentation (COV-19-MDP-CTI), COVID-19 classification utilizing CT scan depending on meta-classifier approach (COV-19-SEMC-CTI) and deep learning-based COVID-19 prediction utilizing CT scan images (COV-19-CNN-CTI), respectively.

In the new paradigm of health-centric governance, policymakers are in constant need of appropriate metrics to determine suitable policies in a non-arbitrary fashion. To this end, in this paper, a compartmentalized model for the transmission of COVID-19 is developed, with a socially distanced compartment added to the model. The modification allows for administrators to quantify the extent to which voluntary social distancing norms are followed, and address restrictions accordingly. Modifications are also made to incorporate inter-region migration, and suitable metrics are proposed to quantify the impact of migration on the rise of cases. The healthcare capacity is modeled and a method is developed to study the consequences of the saturation of the healthcare system. The model and related measures are used to study the nature of the transmission and spread of COVID-19 in India, and appropriate insights are drawn.

The aim of the present paper is to state a simplified nonlinear mathematical model to describe the dynamics of the novel coronavirus (COVID-19). The design of the mathematical model is described in terms of four categories susceptible ($S)$, infected ($I)$, treatment ($T)$ and recovered ($R)$, i.e. SITR model with fractals parameters. These days there are big controversy on if is needed to apply confinement measure to the population of the word or if the infection must develop a natural stabilization sharing with it our normal life (like USA or Brazil administrations claim). The aim of our study is to present different scenarios where we draw the evolution of the model in four different cases depending on the contact rate between people. We show that if no confinement rules are applied the stabilization of the infection arrives around 300 days affecting a huge number of population. On the contrary with a contact rate small, due to confinement and social distancing rules, the stabilization of the infection is reached earlier.

Coronavirus disease (COVID-19) is a pandemic disease that has affected almost all around the world. The most crucial step in the treatment of patients with COVID-19 is to investigate about the coronavirus itself. In this research, for the first time, we analyze the complex structure of the coronavirus genome and compare it with the other two dangerous viruses, namely, dengue and HIV. For this purpose, we employ fractal theory, sample entropy, and approximate entropy to analyze the genome walk of coronavirus, dengue virus, and HIV. Based on the obtained results, the genome walk of coronavirus has greater complexity than the other two deadly viruses. The result of statistical analysis also showed the significant difference between the complexity of genome walks in case of all complexity measures. The result of this analysis opens new doors to scientists to consider the complexity of a virus genome as an index to investigate its danger for human life.

Coronavirus disease (COVID-19) is a pandemic disease that has had a deadly effect on all countries around the world. Since an essential step in developing a vaccine is to consider genomic variations of a virus, in this research, we analyzed the variations of the coronavirus genome between different countries. For this purpose, we benefit from complexity and information theories. We analyzed the variations of the fractal dimension and Shannon entropy of genome walks for two-hundred samples of coronavirus genomes from 10 countries, including the Czech Republic, France, Thailand, USA, Japan, Taiwan, China, Australia, Greece, and India. The result of the analysis showed the significant variations (P-value=0.0001) in the complexity and information content of genome walks between different countries, and therefore, we conclude that the structure of the coronavirus genome is significantly different among different countries. This is a novel and very significant investigation that should be considered for developing a vaccine for this deadly virus.

The coronavirus has influenced the lives of many people since its identification in 1960. In general, there are seven types of coronavirus. Although some types of this virus, including 229E, NL63, OC43, and HKU1, cause mild to moderate illness, SARS-CoV, MERS-CoV, and SARS-CoV-2 have shown to have severer effects on the human body. Specifically, the recent known type of coronavirus, SARS-CoV-2, has affected the lives of many people around the world since late 2019 with the disease named COVID-19. In this paper, for the first time, we investigated the variations among the complex structures of coronaviruses. We employed the fractal dimension, approximate entropy, and sample entropy as the measures of complexity. Based on the obtained results, SARS-CoV-2 has a significantly different complex structure than SARS-CoV and MERS-CoV. To study the high mutation rate of SARS-CoV-2, we also analyzed the long-term memory of genome walks for different coronaviruses using the Hurst exponent. The results demonstrated that the SARS-CoV-2 shows the lowest memory in its genome walk, explaining the errors in copying the sequences along the genome that results in the virus mutation.

This study focuses on the prevalence of COVID-19 disease along with vaccination in the United States. We have considered the daily total infected cases of COVID-19 with total vaccinated cases as exogenous input and modeled them using light/heavy tailed auto-regressive with exogenous input model based on the innovations that belong to the flexible class of the two-piece scale mixtures of normal (TP–SMN) family. We have shown that the prediction of COVID-19 spread is affected by the rate of vaccine injection. In fact, the presence of exogenous input variables in time series models not only increases the accuracy of modeling, but also causes better and closer approximations in some issues including predictions. An Expectation-Maximization (EM) type algorithm has been considered for finding the maximum likelihood (ML) estimations of the model parameters, and modeling as well as predicting the infected numbers of COVID-19 in the presence of the vaccinated cases in the US.

In this paper, we investigated the impact of the COVID-19 pandemic on the cryptocurrency market. The direction of information transfer between the 67 digital cryptocurrency markets was evaluated, in particular Bitcoin, Ethereum, Litecoin, and Ripple, and we determined which of them were the most influential in the markets. The comparison of the first half of 2019 (outside the pandemic of COVID-19) against the first semester of 2020 (during the COVID-19 pandemic) was used to analyze the pandemic influence. We found two distinct behaviors: (i) in 2019, Bitcoin, as the primary capitalization bond, presented a more substantial transfer of information than the other cryptocurrencies toward Bitcoin $\to$ Ripple (0.0541), followed by Litecoin $\to$ Ripple (0.0522); (ii) in 2020, the most substantial transfers of information occurred from Ethereum to other cryptocurrencies (Litecoin, Bitcoin, and Ripple, in that order). In this period, the weakest transfers happened from Litecoin $\to$ Ripple and in the opposite direction, with equal value (0.0104). Our results indicate that there was a change in the direction of the information flow between the investigated cryptocurrencies, where ETH became the dominant cryptocurrency during the period of turbulence caused by the COVID-19 pandemic.

COVID-19 outbreaks are the critical challenge to the administrative units of all worldwide nations. India is also more concerned about monitoring the virus’s spread to control its growth rate by stringent behaviour. The present COVID-19 situation has huge impact in India, and the results of various preventive measures are discussed in this paper. This research presents different trends and patterns of data sources of States that suffered from the second wave of COVID-19 in India until 3rd July 2021. The data sources were collected from the Indian Ministry of Health and Family Welfare. This work reacts particularly to many research activities to discover the lockdown effects to control the virus through traditional methods to recover and safeguard the pandemic. The second wave caused more losses in the economy than the first wave and increased the death rate. To avoid this, various methods were developed to find infected cases during the regulated national lockdown, but the infected cases still harmed unregulated incidents. The COVID-19 forecasts were made on 3rd July 2021, using exponential simulation. This paper deals with the methods to control the second wave giving various analyses reports showing the impact of lockdown effects. This highly helps to safeguard from the spread of the future pandemic.

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SARS Virus may not be Coronavirus.

SARS Diagnostic Methods.

Australian Scientists to Infect Animals with Virus.

Thoughts on SARS Drug Development Strategies — And A Better Way to Manage SARS Patients?

Biota’s Antiviral Drugs and Diagnostic Kits Might Be an Answer to SARS and Avian Flu Infections.

Genetix Equipment Plays a Key Role in Determining SARS Genetic Code.

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Starpharma Collaborates with US National Institute of Allergy and Infectious Diseases.

Sanyo's New Water Technology to Suppress Avian Influenza Viruses.

Southeast Asian Countries Agree on Birdflu Plan.

US Researchers Identify How SARS Virus Infects.

Taiwan Researcher's Drug for Pompe Disease Approved in the US.

Singapore Researchers to Utilize Pharmaceuticals as Anti-foulants.

GSK Pulls out of Vaccine Plan in Taiwan.

Australian Listed biotech firms to Benefit from New Alliance.

CyGenics Signs Collaboration Agreement with the Blood Center of Zhejiang Province.

Shanghai Genomics.

CMIC and Accium Collaborate to Provide Exploratory-IND and Low-Radiation PK/ADME Service.

Stelic Discovers New Treatment Method for Acute Liver Failure.

Japan Bioventures Today — Regimmune Corporation.

Inventory Response by Baxter on its Preliminary Findings for Bird Flu Vaccine.

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Invitrogen Opens First of its Kind Supply Center in Singapore.