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In this paper, we bring a comparison study between the original genome sequence of SARS-CoV 2 virus, ID: MN908947, coming from China 2019, and sequences of SARS-CoV 2 virus - P.1 variant, coming from Brazil 2021. Using tools of stochastic processes and through the identification of each sequence as being a sample of a stochastic process, after finding the best model for each sequence, we build dendrograms using a metric introduced in [1] that show that the original sequence can be considered in a separated cluster when compared with the P.1 variant. [ABSTRACT FROM AUTHOR]

Several papers on the dynamics and early identification of COVID-19 by mathematical modelling have been published in the last two years. This review will look at the quantitative analysis and dynamical behaviors of novel coronavirus, with a focus on the Caputo fractional derivative. Currently many mathematical models are used to investigate the Novel coronavirus under Caputo fractional order system of differential equations. In this paper, we have focused on reviewing the Caputo fractional model of Covid 19, local stability and optimal control. The purpose of this work is to provide the research community a complete overview of the Caputo fractional derivative approach used in these investigations. [ABSTRACT FROM AUTHOR]

The new coronavirus that is the source of the pandemic respiratory infection known as COVID-19 poses a major risk to the public's health. In this paper, we present a mathematical model of the spread of an infectious disease according to the characteristics of the COVID-19 pandemic. The proposed improvedmodel is developed on the AnyLogic system, which will be referred to as the SEAIR (Susceptible-Exposed-Asymptomatically Infected-Symptomatically infected-Recovered) model. The paper considers mathematical models that are used to analyze and predict the development of the COVID-19 pandemic, including time series analysis, equations of mathematical physics and simulation models, their various combinations and generalizations. Due to the novelty and complexity of the COVID-19 disease, the parameters of most mathematical models are usually unknown, and this leads to the need to consider and solve inverse problems. [ABSTRACT FROM AUTHOR]

Corona virus (covid-19) – an infectious illness caused by SARS-COV-2. It can be transmitted from one person to another when an infected person cough, sneeze, speak. sing or breathe. Thus, masking your face in general places is an effective method of protection. Globally, governments implemented lockdowns following the covid-19 pandemic. other transmission off the virus. However, the long period the lockdown affects peoples live hood. In order to overcome this, the government has announced partial lockdown with some rules & regulations. An efficient & economic approach of using AI and do setup a device to create a safe environment. This paper will present a hybrid face mask detection algorithm that utilizes deep learning. A device to detect masks entail of a dataset of images with mask or without mask, and with the help of open-CV in real time, a webcam stream is used to detect whether or not a face mask is worn by the person. Persons without face mask has been indicated in with an alert massage and door will not open using Arduino controller. [ABSTRACT FROM AUTHOR]

Lactoferrin can be used as an antiviral drug. In this regard, the development of methods for the isolation of lactoferrins from cow, goat and human milk, a comparative study of their physicochemical properties and an assessment of the possibility of using them as therapeutic and prophylactic agents against Sars-Cov-2 are relevant. The paper presents a comparative analysis of the effectiveness of ion-exchange, metal-affinity, immunoaffinity, DNA-affinity methods for the isolation of lactoferrin from women's, cow's and goat's milk. Using HPLC and immunochemical methods, the structural and functional features of isolated lactoferrins were studied. Based on the obtained results and analysis of literature data, the mechanism of interaction of lactoferrins with heparan sulfate proteoglycan (HSPGs) of cells was shown, as a result of which LF blocks the primary binding of viral particles to the cell surface. Bovine lactoferrin showed the highest activity against SARS-CoV-2. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had an unprecedented impact on global health and social well-being in recent years. As of September 2022, more than 600 million people worldwide have been infected with the SARS-COV-2 virus [1], which has proven difficult to control due to the lack of available treatments and medications. Quarantine measures have become necessary to manage the high infection rate of COVID-19, but these measures pose additional risks, especially for elderly patients who may be more prone to falls or fainting during the quarantine period. To address this concern, a fall detection system has been integrated into the health monitoring process. The advent of IoT technology has provided a way to remotely monitor and track highly contagious diseases, including COVID-19, through wearable devices and other IoT-based systems. In this paper, an IoT-based solution has been proposed that combines a fall detection system and a health monitoring system to remotely monitor the patient's condition. The ESP8266 microcontroller, along with sensors such as the pulse oximeter (MAX30100), temperature sensor (LM35), and accelerometer (MPU6050), is used to collect health data, which is uploaded to a developed dashboard on BLYNK for monitoring purposes. The fall detection system sends an alert message via SMS to the care taker's phone when a fall occurs. The methodology used in both systems is based on threshold-based prediction from the data collected by the wearable device. When the health condition falls below a pre-set threshold value, the alert system in Blynk is triggered, and the caretaker is notified. The system aims to provide low-income families with a portable and low-cost healthcare device that provides high accuracy in monitoring health parameters, making it a valuable tool in the fight against COVID-19 and other highly contagious diseases. [ABSTRACT FROM AUTHOR]

COVID-19 disease is caused by a contagious family of corona virus. The dynamics of infectious novel virus SARS-Cov-2 is studied in this paper. We have studied a 7 class Susceptible-Exposed-Vaccinated-Asymptomatic Infected-Symptomatic Infected-Hospitalized-Recovered (SEVI1I2HR) model. Local stability for the equilibrium pointsis established with the help of Routh Hurwitz criterion. Proof of Global stability of the endemic equilibrium is done with Dulac's function. Numerical simulation carried out for the model suggests that minimizing the contact rate, hospitalization of the infected and vaccinations for all are the only ways to control the pandemic COVID-19. Sensitivity analysis of the considered parameters in the Basic Reproduction Number (R0) was performed to understand the most affected and sensitive parameters of the model. It was observed that R0 is directly proportional to the contact rate while inversely proportional to vaccination rate. [ABSTRACT FROM AUTHOR]

COVID-19 is a contagious disease caused by a virus, the severe acute respiratory syndrome coronavirus 2 (SARS-2). COVID-19 pandemic is still the most challenging health scare phenomenon in the 20th century with social distancing was the most effective way to curb spreading of the corona virus until the discovery suitable vaccines. Essential economic activities were allowed to operate while non-essential social activities were forced to operate at reduced capacity during the pandemic. In Malaysian context religious activities were among the earliest super spreader cases of COVID-19 due to mass gatherings at Sri Petaling Mosque. Compulsory COVID-19 standard Operating Procedure (SOP) practices being introduced such as wearing face mask, social distancing and frequent hand washing have since become a norm that people are willing to adopt voluntarily. This clearly shows that activities with proven health benefits will gain people trust despites their initial reservation. This paper aim to highlight benefits of new SOPs in Mosques during and post COVID period by using COM-B model. Behaviour changes in adapting new norm during mass gatherings were used to identify the main driving factor in adoption of new norm including the positive effects in adopting the new practices. Case study was carried out at 4 selected mosques using site observation and interviews methods. The results show that the new norm practices that has been implemented for the last 2 years has since become a habit of most worshippers while attending activities in mosque. This has indirectly given a positive effect on the social, economic, and environmental impact to the community. [ABSTRACT FROM AUTHOR]

In the present times, Covid-19 is a great concern for every country as it is spread in multiple countries across the globe. In this paper, we will try to estimate potential consequences of SARS-CoV-2 transmission through a compartmental model. As many parameters may be observed to predict about number of deaths and covid-19 cases due to SARS-Cov2. We will use SEIRD model to predict basic reproduction number with the help of collected data of confirmed Covid-19 cases, deaths and the clinical parameters. In the next part of paper, we will try to make forecast about the number of deaths in India. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease 2019 (COVID-19) has shown the danger that can be caused by small, airborne and contagious virus particles such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To prevent the mass spreading of such viruses, the use of electromagnetic waves could be used to combat the spreading of the virus. Particularly, the waves could force any virus particles to oscillate in resonance with the frequency of the wave and destroy themselves before the virus would enter the respiratory system. This paper describes the use of numerical simulations to analyze the effects of exposing a SARS-CoV-2 particle to electromagnetic waves at a range of frequencies and to determine which frequencies have the potential of causing resonance within the virus which would yield to the maximum energy absorption. Such technique could be utilized in preventive/protective devices and disinfection equipment. [ABSTRACT FROM AUTHOR]

Tourism, the world's largest market was struck heavily by the COVID-19 pandemic in the 21st century. The outbreak had caused severe fatality and destructive impacts globally. Strict restrictions, curfew, lock downs, work from home and quarantines has affected almost all industries and tourism sector had the major impact. The novel coronavirus had drastically reduced the international tourist arrivals which resulted in the loss of export revenues and around 100-120 million lost their jobs and livelihood with a stronger representation of women from MSME contributing to about 54% (UNWTO). This paper focuses on the impact of COVID 19 on global tourism industry. Due to lack of essential data secondary source of data was resorted. The study highlights how the industry and its allied sectors such as aviation and hospitality Industry has suffered a negative impact due to the virus outbreak. It also covers the various strategies adopted to combat the impact on a global scale. The findings of the study will assist in overcoming the challenges in the future. [ABSTRACT FROM AUTHOR]

The novel Coronavirus (SARS-CoV-2) has wreaked havoc throughout the world, affecting nearly every country. Several countries are currently battling the virus's second or third wave, which is wreaking havoc far worse than the first. A variety of plastic-based personal protective equipment (PPE) was instrumental in protecting people during the COVID-19 pandemic. Every day, a significant amount of single-use PPE including masks, gloves, protective aprons, face shields, safety glasses, sanitiser containers, plastic shoes, and medical gowns (made primarily of polypropylene) is discarded by health care workers and the general public. While this personal protective equipment is lowering the spread of SARS-CoV2, the lacking of sustainable management possesses a serious threat to public health and the environment. As the SARS-CoV-2 virus can survive in discarded medical waste for up to 168 hours, likely, the medical waste originating from hospitals, clinics, medical centres, home isolation, and quarantine facilities where the infected individual is getting treatment could spread and increase the infectivity of the virus. Therefore, this paper discusses the environmental and human health consequences of single-use personal protective equipment used to reduce the spread of SARS-CoV-2. [ABSTRACT FROM AUTHOR]

Covid-19 is an ongoing global pandemic coronavirus disease. It has affected the life and health of people across the world. Nurses play an important role in this pandemic situation. Nurses fight to safeguard the lives of everyone infected. We can't imagine a society without nurses in the present scenario. But the truth is even though there is an abundance of flattering words and appreciations in social media the real-life situation of nurses is filled with agony. This paper is an attempt to depict the real-life situation of nurses during the pandemics. Nurses face discrimination, exclusion, and isolation because they treat corona-infected patients. They don't get job satisfaction from the part of the management of hospitals and the government. They were sacrificing their life and family for the corona-infected patients. All nurses have to keep a strict quarantine schedule and social distancing. This makes family and social life very difficult; along with the terrible work pressure and isolated life which would adversely affect their mental stability. During the first wave of the Covid pandemic, people were confused and panic spread and they were very rude to health workers. Not only nurses but their families were also subjected to cruel exclusion and discrimination from society. Yet, they gave all their strength and will and fought hard to prevent these pandemic situations and saved many infected people. Those efforts are acknowledged, and nurses deserve better salaries and recognition for their done and are doing. [ABSTRACT FROM AUTHOR]

Covid-19 is a severe pandemic with a high mortality rate caused by the infectious respiratory virus SARS-CoV-2. Viruses associated with respiratory tract infections will use epithelial cells and airway mucosa as initial targets and cause respiratory tract infections or organ damage. This paper develops a model for the spread of Covid-19 with the effect of vaccination and quarantine. This model has seven variables, namely Susceptible, Vaccinated, Exposed, Symptomatic, Asymptomatic, Quarantined, and Recovered. From this model, two equilibrium points are obtained, non-endemic and endemic equilibrium points. Further, we analyze the global stability using the Lyapunov method. If basic reproduction number (ℛ0) less than one, then the non-endemic equilibrium point is globally asymptotically stable, while if basic reproduction number more than one, then the endemic equilibrium point is globally asymptotically stable. Numerical simulations are given to describe the behavior of the spread of Covid-19 in Banten Province on February 1, 2022 until May 1, 2022 with the result that increasing vaccination rates can reduce susceptible subpopulations. From the simulation results, it is obtained with a value of ℛ0 = 0.0000103<1, meaning that the Covid-19 spread model is asymptotically stable at a non-endemic equilibrium point. This indicates that the spread Covid-19 will decrease over time. [ABSTRACT FROM AUTHOR]

An original infection has spread over the global village in form of pandemic and the regular lifestyle of human beings got stuck with misfortunes. As it has been declared by the global organization of health sector namely WHO, this Covid got initiated from southern city of China during the last weeks of the year 2019. Based on the multiple observations, it was declared as most infectious as well contagious spread through wind or by coming in close contact with a tainted individual. Various methods have been implemented to cease off the fast spreading infectious disease. For example, keeping a social separation, that is to say, keeping a legitimate actual separation among individuals and reducing close contact with one another, and wearing a facial covering to stay away from the beads that are passed by means of wind air. Consequently, this paper put forth the observation made to explore the initiatives for maintaining physical distance in the public domain and to identify whether a Face Mask is worn or not. The architectural framework provides the exact location and position of an object along with an acknowledgment about facial coverage as per the WHO norms under the moving images of the persons who are into public domain. Our suggested model implements the existing procedures such as dual shot face detector, You look only once and finally ResNet algorithms. Irrespective of the origin location of an individual and their interaction among themselves taking into consideration the space between them and the facial expressions in a continuous form were recognized. A general outcomes dashboard has displayed the result composing the measures with regard to the abuses of an individual person and breaking the protocol of maintaining separate distance measures. Subsequent to carrying out and conveying the procedures of process, the above said exploration model accomplished an accuracy of maximum performance. Subsequently, this exploration project had come up with demonstrated realities that maintaining physical distance along with the usage of facial mask decreases the escalation of the disease, which lead to consequently constructs a procedures to assist with identifying the mentioned actions. [ABSTRACT FROM AUTHOR]

The outbreak of the novel Coronavirus has taken the lives of many and has increased the demand for new and innovative medical and testing equipment. In addition to that, the longing to retain normalcy, during these difficult times, is also required. Industry 4.0 is the contemporary phase of the Industrial revolution whose backbone is 'cyber-physical' systems. It is automation of the conventional industrial and manufacturing processes using the latest technology. This paper aims to show an in-depth evaluation done on the technologies of industry 4.0 and their implementation during the Covid-19 pandemic. It also gives an overview of the major technologies used and their range of operation, towards making societies better and saving lives. [ABSTRACT FROM AUTHOR]

Online shopping has become an alternative way for people in buying things, especially in the middle of the novel coronavirus (COVID-19) outbreak crisis. Besides, it is also making life easier. The problem with online shopping is when sometimes customer could not able to receive the goods when nobody at home. And it also involved safety considerations as in normal case, the good will be left outside the premises by the courier when the recipient not at home the good might be stolen. This paper aimed to design a Smart Home Package Mailbox to secure the delivery packages receive safely with good condition by reducing the problems occurring during the delivery. The Smart Home Package Mailbox is a hardware that operates by using the Quick Response (QR) code to communicate with its system. It has two compartments level which levels two for receiving package and level one for storing the received packages. Two main android applications have been developed to support the project system. The Smart Postman application is for the postman to open the mailbox door and update data in the database. While Smart Home Mailbox application is for users to track the delivery and receive notification. Once the sending package is put inside the mailbox, the sender and the recipient will be notified regarding the delivered package. This project is able to increase courier management efficiency as well as packages being delivered within expected time with good condition. [ABSTRACT FROM AUTHOR]

Since 2019, people around the world have been suffering from severe acute respiratory syndrome coronavirus 2 (SARS-COV-2). Most countries, even developed ones, have struggled to deal with the pandemic. The test is the first way to identify patients affected by the coronavirus. Post-test confirmation in particular takes longer and isn't as accurate because we've seen a lot of tests test positive and then confirm negative with a percentage error of +10% to-10%. There are many respiratory diseases including COPD (chronic obstructive pulmonary disease), lung cancer, pneumonia, bronchitis and Covid-19. According to research, pneumonia and Covid-19 share many of the same symptoms. So it's very difficult to find out in real time which diseases are affecting patients based on our final test results. Various methods have been used to detect Covid-19 infection. This paper proposes Long Short-Term Memory (LSTM) to improve the effectiveness and efficiency of Covid-19 detection. Several other datasets, notably COVID-19, viral and bacterial pneumonia cases, were used to test the retrained model. [ABSTRACT FROM AUTHOR]

In the last month of 2019, a new coronavirus has been discovered. The coronavirus (COVID-19) pandemic already has caused major human suffering as well as significant economic loss. The rapid spread of this disease all around the world has made every country to implement lockdown in order to break the chain of Covid-19. The imposed of lockdown in every country all around the world have given a major impact towards political, social, human suffering as well as significant economic disruption. The COVID-19 pandemic has flipped the world of work all over the sides. It having a significant impact on workers' and their families' jobs, lives, and well-being, as well as on businesses around the world, particularly small and medium-sized businesses. This paper aims in exposing the effect of COVID-19 on the economic as well as in the employment globally. As a result, there are a lot of effect from the movement restriction order that have been imposed by the government in each nation. The imposed on movement control order with the implementation of stay at home policy and social distancing have impacted all sectors of economic which has led impact the employment. [ABSTRACT FROM AUTHOR]

As of April 30, Vietnam has a total of 270 confirmed cases of the SARS-CoV-2 virus and no death, numbers that are significantly low compared to other neighboring countries. Besides, with no new cases reported in the past few days, and 222 cases have recovered. So far, Vietnam, which borders with China, where the COVID-19 originated, has done an excellent job in fighting the outbreak with limited resources compared to wealthy Western countries. In order to prevent further spread, Vietnamese authorities have enforced social distancing, many restaurants and businesses have shuttered, tourists have left, and many residents are out of work. As a result, the Vietnam economy and residents have been primarily affected. In this paper, the author will review how the Vietnamese Government combat the COVID-19 pandemic successfully, manage socio-economic impact as well as support poor people and workers. Lastly, the author will provide recommendations to Vietnam's Government to reduce the impact of coronavirus in tourist field. [ABSTRACT FROM AUTHOR]

COVID-19 is a type of disease that transmits a new variant of virus known as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) in the same novel coronavirus family as SARS-CoV and Middle East Respiratory Syndrome Coronovirus (MERS-COV). A fast method to detect the disease is essential to prevent larger transmission and to look after the infected patients. The Chest X-ray, one of the detection methods of COVID-19 can be used in the examination process of suspected cases. In this paper, a COVID-19 detection model through chest x-ray images is proposed by using Grey Level Co-occurrence Matrix (GLCM) with Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Backpropagation Artificial Neural Network (BP-ANN) classifiers. In this case, Principal Component Analysis (PCA) will be added as a mean to optimize features extraction process. The aim of this work is to find the best classifier for predicting chest x-ray images as normal, pneumonia, or COVID-19 suspect. The BP-ANN emerged as the best classifier with 85,5% accuracy, 85,8% precision, and 86,1% recall. [ABSTRACT FROM AUTHOR]

The outbreak that began at the beginning of this year as a result of a new virus in the coronavirus family is killing countless people and driving the world economy to its knees. Furthermore, such circumstances are historically cyclical. For better or worse, even for novel viruses, the symptoms and treatment of infected individuals are always the same: more or less severe flu symptoms, isolation, and thorough hygiene.In a pandemic environment, collecting the samples of COVID-19 (Corona Virus) and diagnosing is very challenging. The medical staffs need to be in close formal contact with patients while collecting those samples from the patients. This leads to cross-infection within the medical staff. It spreads the disease to others. This paper proposes the robotic model for collecting the Nasopharyngeal (NP) sample from the patients using Bluetooth-enabled robots to avoid this cross-infection. The low-cost robot designed in this paper is enabled remotely for swab collection. The wireless technology used for handling robot operations is straightforward and compatible with android technology. The Bluetooth-enabled robot design is developed with active end effectors that help the robot increase precision with the DC motor combination. The passive form of the positioning arm of the robot defines the ankle, shoulder rotation, movement. The detachable form of the swab gripper is used to grip while movements with integrated force sensing capability. [ABSTRACT FROM AUTHOR]

(COVID-19) is an epidemic disease with symptoms similar to the flu. Since December 2019, the new Coronavirus has appeared in China and spread all around the world. As a result, the rapid and severe spread. For example, computed tomography (CT) and x-rays are utilized to find an available and accurate diagnostic tool. Artificial intelligence technologies make a significant contribution to the diagnosis and analysis of medical images. As a consequence, it was used to detect and diagnose Covid-19. Thus, the entire major medical scanning will be covered in this review paper, especially Focus mostly on last year's combination of Artificial Intelligence with CT and X-ray. Both are commonly used on front-line medical clinics to identify the recent results of fighting COVID-19. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019, also known as Covid-19, is an infectious disease that has infected more than nineteen million people all around the world. This virus is a member of coronavirus family but it is the most mortal one. It has named as 2019-nCoV by World Health Organization(WHO) after Chinese Center for Disease Control and Prevention(CDC) discovered a new coronavirus from a swab sample of a patient. As we know this pandemic started December 2019 in China, and it is still spreading and causing deaths all around the world. In this paper, we aimed to estimate the right size of epidemic. For that purpose, we chose 10 countries, which are affected by, and still fighting with this disease, to forecast the upcoming death rates by using the previous week deaths. These 10 countries are Argentina, Austria, Brazil, France, Iran, Italy, Sweden, Turkey, United Kingdom, and United States of America. We used the death data of WHO with assumption that data is accurate. For this estimation, firstly, we used the assumption that the reported death delay distributed according to a gamma distribution. Then, we used a binomial distribution for assumption of deaths. This binomial formula led us to find a posterior distribution which is an extension of Bayes' theorem for death ratio. Lastly, we compared our estimations with real data. [ABSTRACT FROM AUTHOR]

COVID-19 is an infectious disease which is spreading fast and some variants of the coronavirus, such as Delta and Omicron, are spreading more easily between people and this spreading can be alleviated to some extend by vaccination. In this context, the government is demanding for producing the vaccination certificate for the person to be in certain public places. But there can be chances where people do not know their Aadhar number and they do not carry their vaccination certificates especially in rural areas. And also it takes a lot of time in waiting in a long queue for verification purpose. So to tackle all these problems we propose an automated system for identifying vaccinated person without much human interventions. The system uniquely identifies the person through iris recognition using various deep learning models. In this paper we made a comparative analysis of different deep learning and CNN architectures (MobileNet, EfficientNetB3,InceptionV3, DenseNet169) for building a model for person recognition and select the best model with maximum accuracy. Here, we used kaggle dataset (1000 iris images) for testing and training. Also using an iris scanner, the live iris images can be captured and used for model building. Using some deep learning algorithms, the features of iris image of the person is obtained and it is compared with those in Aadhaar database to get the Aadhar number. Using this Aadhar number, the details of the vaccinated person can be obtained from vaccination database and system will identify whether a person is vaccinated or not. By installing our proposed system, vaccinated person can be easily identified without much human interventions, avoiding touch and reducing the long queue for verification, thereby maintaining social distancing. [ABSTRACT FROM AUTHOR]

Due to the fast spread of the new Coronavirus called SARS-CoV-2 (otherwise known as COVID-19 virus) worldwide and its continuous mutations, such large viral outbreaks require early elucidation to determine the genetic sequence of the virus to design an effective system for identifying variants Different known and unknown virus. Identifying specific variables facilitates the comprehension and modelling of viral propagation patterns to treat and manage outbreaks, create efficient reduction techniques, and avoid future outbreaks. In addition, it serves a vital role in assessing the efficiency of known vaccines against each variation and modelling the probability of superinfection. It is known that the genomic sequence carries the vast majority of genetic information pertaining to variations and variants of the Coronavirus. This paper proposes a system that depends on deep learning to predict the mutations of complete COVID-19 virus genomes based on the Convolutional Neural Network (CNN) algorithm as an alignment-free method. The k-mer technology is applied to fragment the DNA of coronavirus mutants to create a unique vocabulary. The proposed approach can correctly predict and characterize other coronavirus strains, such as MERS-CoV, Alpha-CoV, SARS-CoV-2, SARS-CoV, Gamma, Beta, and other strains, regardless of missing information and sequencing errors with an accuracy rate of 99% on the data set. [ABSTRACT FROM AUTHOR]

The SARS-COV-2 virus had infected over 600 million people, and this global pandemic seems endless. The individual interaction affects the virus transmission but is ignored in most reported prediction models. In this paper, we build a Susceptible-Exposed-Infected-Recovered (SEIR) model to include the individual interactions with the direct simulation Monte Carlo (DSMC) method. The classical pandemic model is a series of ordinary differential equations with constant parameters. In the real world, as viruses proliferate in the human body, the critical parameter of the pandemic is time-varying. In this state, the result from the present DSMC method shows the time to reach the peak number of patients is linearly related to the infectious period, which is beyond the capability of the traditional deterministic model. This study provides several valuable results which are potentially useful to the centers for disease control and prevention (CDC) in the future. [ABSTRACT FROM AUTHOR]

The importance of SARS-CoV-2 molecule and especially its spike (S) protein defines the need of studying its structure and functions in deep. Main structural studies of viral fusion proteins, largely limited to X-ray crystallographic analysis for a long time, rely on cryo-electron microscopy nowadays, so the amount of high resolution data of spike protein is yet to growing up. The opportunity of applying computational methods for similarity detection between protein molecules for further analysis of SARS-CoV-2 spike protein, based on these precise-enough data is considered in this paper. Biological background and the research problem definition are presented first, followed by a survey of protein structure comparison methods, which are reportedly used for studying SARS-CoV-2 spike protein. Conclusions are made about future investigations in the area. [ABSTRACT FROM AUTHOR]

In early 2020, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was found in Wuhan, China. The virus causes the disease named COVID 19 and this disease was declared as pandemic. Vaccines helped prevent the spread of the disease, while the intermediary media that transmits the virus expands the spread of COVID 19. This paper constructed the model and analyzed the effects of vaccination on the spread of the virus COVID 19 with asymptomatic cases through intermediary media. It is assumed that the vaccine is given only to susceptible individuals, and individuals who receive the vaccine will be resistant and cannot be infected with COVID 19. The Runge Kutta method is used to solve the differential equations, and dynamical system is used to analyze the model. The numerical simulation shows that the vaccine effectively prevents the spread of COVID 19 with asymptomatic cases through intermediary media. However, there is a need for further review regarding the effect of vaccination that does not make recipients resistant to COVID 19. [ABSTRACT FROM AUTHOR]

Cancer is one of the diseases that causes the highest mortality rate in the world, where most used treatment is chemotherapy that destroys both cancer and healthy cells. The side effect of chemotherapy is fatigue caused by a decrease in the body's immune. This situation has become quite risky due to the presence of COVID-19 outbreak in the world, where the virus attacks humans with poor immunity. The aim of this paper is to develop the mathematical model about the viral dynamics of SARS-CoV-2 on tumor-immune system of cancer patient with chemotherapy. In the model, we study the interactions between cancer cells, SARS-CoV-2 virus, epithelial cells, and immune system. From the model, we will analyze the equilibrium point stability. In the end, we simulated the effect of chemotherapy towards SARS-CoV-2 infection on cancer patient and the sensitivity analysis of the chemotherapy drugs doses toward the density of Cytotoxic T Lymphocyte cells. [ABSTRACT FROM AUTHOR]

Segmentation of chest and lung images plays a crucial role in supporting disease diagnosis, treatment planning, and surgical navigation. For accurate segmentation of lung images, this paper proposes a CT image segmentation algorithm based on classical methods and neural networks using machine learning methods. The neural network and machine learning approach has been thoroughly analyzed in comparison with standard segmentation methods (threshold detection, K-means clustering, histogram-based approach, and boundary detection). The experimental results show that the proposed algorithm is highly competitive. [ABSTRACT FROM AUTHOR]

The major problem, nowadays is the novel coronavirus or covid-19 pandemic which has and still infecting people and causing the death of many around the world. Although strict and severe actions have been taken by the government of all over the world to control and minimize the spread of this virus by lockdown, suspending, all travel and sport activity, economic and social activity, quarantines, travel restriction at stations and airport, etc, many of the people around the world have lost their lives and many are suffering. Recently, a conducted study has reported that in China the number of cases that were not documented is 79% as they show no symptom of the virus. In many other countries, the real representation of the patient infected by the virus was different from the real number which was higher in number. Hence, patients with asymptomatic conditions are the major reason for the fast and large covid-19 spread and are also the major reason that caused the people too afraid of the situation. To contribute this global pandemic, in this paper, we propose an investigation which is IoT based system designed to detect both symptomatic-asymptomatic infected people, undocumented, and the place of the pandemic. The aim is to make the authority and people aware of the situation by providing the total numbers of the cases registered, totally recovered, and death of the people caused by the pandemic. In the proposed system, we have used the IoT (internet of things) technology with the NODEMCU-ESP8266, LCD, and Wi- Fi connection to provide information of the important numbers of the patients of covid: cases, recovery, and death of the people in India. This system may keep people more informative, protected, and aware of the danger of the virus so they can take care of themselves and their families in every possible way. [ABSTRACT FROM AUTHOR]

In this paper, we use the SEIR model to predict the outbreak of the novel coronavirus 2019-nCoV, and when the outbreak will begin. We predicted and compared the development of an outbreak where certain measures are taken and measures are not taken, and then we compared with the actual data of the city of Wuhan and study further approaches to responding to such outbreaks. [ABSTRACT FROM AUTHOR]

In this paper, we proposed the design of a smart inhaler for traveler's safety equipment. This smart inhaler utilizes multimode virus destruction using thermal and molecular inhibition. Based on our previous research, a cylindrical shape coated with graphene oxide has the potential to adsorb the SARS-CoV-2 with a strong binding affinity. Having this coronavirus adsorbed on the GO surface then we can apply physical treatment such as thermal heating to destroy the virus. By adding a small compartment inside the inhaler for Eucalyptus oil (Eucalyptol, 1.8 cineole), we add the option of the destructing virus through a virus inhibition (molecular mechanism). This double action of inhaler (thermal heating on the GO surface, and virus inhibition by eucalyptol) is expected to give the traveler some security during COVID-19 pandemic time. Detail of interaction between GO and the spike of the glycoprotein of SARS-CoV-2 and the docking inhibition of SARS-CoV-2 by Eucalyptol discussed in this paper along with some initial design of the inhaler system. [ABSTRACT FROM AUTHOR]

The infectious disease caused by novel coronavirus (2019-nCoV) has been widely spreading since last year and has shaken the entire world. It has caused an unprecedented effect on daily life, global economy and public health. Hence this disease detection has life-saving importance for both patients as well as doctors. Due to limited test kits, it is also a daunting task to test every patient with severe respiratory problems using conventional techniques (RT-PCR). Thus implementing an automatic diagnosis system is urgently required to overcome the scarcity problem of Covid-19 test kits at hospital, health care systems. The diagnostic approach is mainly classified into two categories-laboratory based and Chest radiography approach. In this paper, a novel approach for computerized coronavirus (2019-nCoV) detection from lung x-ray images is presented. Here, we propose models using deep learning to show the effectiveness of diagnostic systems. In the experimental result, we evaluate proposed models on publicly available data-set which exhibit satisfactory performance and promising results compared with other previous existing methods. [ABSTRACT FROM AUTHOR]

The entire human race is currently facing a huge disruption of everyday life due to the rapid spread of the novel Corona Virus disease 2019 (COVID-19). It is essential to develop a tool or model for fast diagnosis of the disease which is pandemic and also the model should be able to justify the result for trustworthy in the field of medicine. Machine learning (ML) and Deep Learning (DL) models play a vital role in identifying COVID-19 patients by visually analyzing their Computed Tomography (CT) scan images. In this paper, few publicly available convolutional neural network models (CNN) were analyzed to classify the CT scan images of lungs into two classes, COVID-19 positive and negative cases. In addition to that, Local Interpretable Model-agnostic Explanation (LIME) framework is used as an explanation technique for interpretability. The pixel of relevancy responsible for the outcome of classification is visually explained through LIME technique which gives trustworthiness in the field of healthcare. [ABSTRACT FROM AUTHOR]

Face masks are used to prevent the spread of disease. Covid19 is a major example, where individuals are required to wear masks in public places to stop the sickness from spreading. The SARS-CoV-2 virus outbreak, which killed people, began in 2019. Tiny droplets from the lips or nose of a person who has COVID-19 can pass the illness to another person when they cough or breathe. There were limited medical resources and no vaccines to cure the disease. This led to hundreds of thousands of deaths worldwide. The only non-pharmaceutical way suggested by WHO to prevent the spread of this virus was to wear masks. Nowadays it has been asked by Many providers of public assistance have to wear masks in order to provide their services. Face mask identification has therefore become a meticulous effort to aid the worldwide civilization. This paper provides a solution to solve this problem of face mask detection using machine learning algorithms such as CNN (Convolutional Neural Network), TensorFlow, Keras, and OpenCV. This model detects the face from the image and identifies whether a person is wearing a face mask properly or not. Accuracy of the model is 96%. Using the Sequential Convolutional Neural Network model values of parameters is optimized in order to detect whether a person is wearing a face mask properly or not these parameters are also used to prevent our model from over-fitting. [ABSTRACT FROM AUTHOR]

The paper describes our program implementation of Global Sequence alignments of DNA sequences using the Needleman-Wunsch algorithm. This modified Neeedlenan-Wunsch algorithm is designed to align multiple sequences in parallel with OpenMP. The algorithm supports different sequence sizes by scanning and equalizing them simultaneously. The results of our experiment are obtained by comparing Sars-CoV-2 fasta files in a versatile, speedy and convenient manner. The program is adapted for optimal performance when aligning Sars-CoV-2 sequences. We analyze program hotspots, multithreading execution and potential bottlenecks using the Intel vTune Profiler. [ABSTRACT FROM AUTHOR]

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused Coronavirus Disease 2019 (COVID-19) is rapidly spreading over the world, resulting in a high mortality toll. Lung infection or pneumonia is a common complication of COVID-19, and imaging techniques, particularly computed tomography (CT), have been useful in determining the disease's diagnosis and treatment. The process of segmenting and recognizing anatomical structure boundaries in various forms of 2D and 3D medical images is known as medical image segmentation. Diagnostics, planning, monitoring, and counseling are just a few of the medical applications that use it. As a result, throughout the last few decades, a great number of segmentation algorithms have been developed. The imaging properties and computation models that have been used to manage COVID-19 are discussed in this paper. CT, positron emission tomography CT (PET/CT), lung ultrasonography, and magnetic resonance imaging (MRI) are all examples of imaging techniques. Artificial intelligence (AI) or other quantitative image analysis methods are urgently needed to maximize the efficacy of imaging in the management of COVID-19. [ABSTRACT FROM AUTHOR]

For a significant reduction of time spent on research to obtain meaningful results in scientific fields, the computing resources of the Joint Institute for Nuclear Research (JINR) and some organizations of its Member States were integrated into a distributed information and computing environment (DICE). The technical possibility for running tasks in the environment was implemented for users of the BM@N, MPD and Baikal-GVD collaborations. The resources not occupied by computational tasks within the main scientific fields of JINR are used to conduct studies on the SARS-CoV-2 virus that causes the COVID-19 disease. In addition, the paper gives a description of the DICE technical implementation, lists the participating organizations and provides some statistics on the use of their resources. [ABSTRACT FROM AUTHOR]

In December 2019, an outbreak of a disease known as severe acute respiratory syndrome coronavirus 2, SARS-CoV-2 (COVID-19) was discovered in Wuhan, China that has caused the deaths of many people around the world. Early diagnosis is needed to control the geological spread of the COVID-19 outbreak. The standard protocol of real time reverse transcription polymerase chain reaction (RT-PCR) and enzyme immunosorbent assay (ELISA) were performed for COVID-19 detection. However, they are limited by tedious sample collection and preparation procedures, longer waiting times for test results and the lack of competent personnel to operate sophisticated lab-based instruments. In this context, the point-of-care (POC) diagnostic platform has proven to be a prospective approach in addressing the above challenges. Therefore, there is a growing urgency to develop diagnostic tools for rapid detection of coronavirus to prevent subsequent spread. This device is necessary and should be sensitive, selective, and demonstrates rapid diagnostic capabilities. Electrochemical DNA biosensors have exhibited these properties and, thus, serve as a promising candidate for virus detection. This review focuses on COVID-19 viral infection and physiopathology in depth. The electrochemical mechanism of the biosensor is then discussed, as well as its capacity to detect COVID-19 in this investigation. Finally, a focus on combining nanotechnology with smartphones to produce smart nano-biosensors was highlighted, which can promote COVID-19 diagnosis that is accessible to both people and the public in patients. [ABSTRACT FROM AUTHOR]

To screen out the main active ingredients of Shufeng Jiedu Granules based on network pharmacology, predict the potential targets and signaling pathways of Shufeng Jiedu Granules for the treatment of Novel Coronavirus Pneumonia (NCP), and explore its mechanism. The main active compounds and the targets of Shufeng Jiedu Granules were screened out from TCMSP database and related literature papers; The targets related to NCP were by OMIM and GeneCards database. The drug-compound-target network were made by and Cytoscape 3.7.2 software and target protein interaction PPI network were constructed through STRING database. GO enrichment and KEGG pathway annotation analysis of core targets were performed using Bioconductor database. Results: According to the screening conditions, 179 compounds and 2560 drug targets were obtained, including IL6, MAPK3, MAPK8, CASP3, IL10, MAPK1, CXCL8, CCL2, IL1B PTGS2 etc. PPI core network contained 16 proteins, including MAPK1, MAPK14 and CCL2, etc. PPI core network contained 27 proteins. GO functional enrichment analysis obtained 78 GO items (P<0.05), KEGG pathway enrichment analysis obtained 153 pathways related to NCP (P<0.05), involving AGE-RAGE signaling pathway in diabetic complications, IL-17 signaling pathway, Influenza A, Chagas disease (American trypanosomiasis), TNF signaling pathway, Hepatitis B, Tuberculosis, Kaposi sarcoma-associated herpesvirus infection, Human cytomegalovirus infection, Pertussis, etc. In conclusion, the active compounds in Shufeng Jiedu granules can treat the Novel Coronavirus Pneumonia by multiple targets and multiple pathways. [ABSTRACT FROM AUTHOR]

The goal of this paper is to provide the structural similarity analysis of SARS-CoV-2 and human proteins. To achieve this goal the experimental framework is proposed based on multiple innovative Bioinformatics platforms. The suggested workflow includes multiple sequence alignment, phylogeny analysis and pairwise sequence alignment. The investigation results are analyzed with respect to identity percentage between Human and SARS-2 protein pairs. Finally, the authors present conclusion of the experiment and provide foundation for further research. [ABSTRACT FROM AUTHOR]

On February 11, 2020, the International Committee on Taxonomy of Viruses (ICTV) officially named severe acute respiratory syndrome coronavirus 2. The World Health Organization (WHO) called the epidemic infectious disease caused by it Covid-19, which is a lung disease caused by Sars-Cov-2 infection. There are no effective antiviral drugs for Sars-Cov-2-induced covid-19 going public on a large scale, but many kinds of vaccines prevent sars-cov-2 infection. At present, the more common vaccines are CoronaVAC, developed by Sinovac, ad26. Cov2. S developed by Johnson & Johnson, BNT162b2 developed by Pfizer, and mRNA-1273 developed by Moderna. At the same time, Remdesivir, developed by Gilead company in the United States, has become the first drug approved by many countries for the treatment of Covid-19, but there are still many uncertainties. In addition, Chinese herbal medicine called Lianhuaqingwen has been proved to be effective in treating Sars-Cov-2 infection. This paper, combined with the current clinical research and some toxicity tests, from the treatment of infected patients to different vaccines against sars-cov-2, was discussed and compared to provide a reference for clinical. [ABSTRACT FROM AUTHOR]

SARS-Cov-2, a new strain of coronavirus that has never been found in humans before, has become one of the biggest public health issues in the 21st century. By March 2021, more than 100 million people around the world had been diagnosed with the disease. Confirmed cases of COVID-19 have ranged from mild symptoms to severe or even fatal. In the early stage of the disease, fever, fatigue, dry cough is the main clinical manifestations. About half of the patients developed dyspnea more than one week later, and the severe cases rapidly progressed to acute respiratory distress syndrome. Thus, the development of the COVID-19 vaccine is important because of the impact and damage caused by the virus worldwide. This paper will first introduce some information related to the vaccine, such as the safety and efficacy of the vaccine. Then, It will illustrate data on some COVID-19 vaccines that are on the market or still in development with comparison and analysis. [ABSTRACT FROM AUTHOR]

China is in the critical period of building a moderately prosperous society in an all-round way. It is the period of rapid economic transformation and the decisive battle for deepening the reform. China's development is facing new opportunities and challenges. The risk of foreseeable and unforeseen is increasing. Combined with the new coronavirus, (coronavirus disease 2019 is a new type of pneumonia, also named Covid-19) one of the global biological risk events, this paper analyzes the challenges faced by China in the future in response to biological risk, and discusses how to deal with microbial risk in the future, makes in-depth thinking and puts forward suggestions. [ABSTRACT FROM AUTHOR]

Viral outbreaks, such as the COVID-19 pandemic, are commonly described by compartmental models by means of ordinary differential equation (ODE) systems. The parameter values of these ODE models are typically unknown and need to be estimated based on accessible data. In order to describe realistic pandemic scenarios with strongly varying situations, these model parameters need to be assumed as time-dependent. While parameter estimation for the typical case of time-constant parameters does not pose larger issues, the determination of time-dependent parameters, e.g. the transition rates of compartmental models, remains notoriously difficult, since the function class of these time-dependent parameters is unknown. Here, we propose a combination of an Augmented Kalman Smoother with an Expectation-Maximization algorithm to simultaneously estimate all time-dependent parameters in an SIRD compartmental model. The approach is applicable to general systems of ODEs with time-varying parameters. In particular, it does not require prior knowledge on model parameters or any further assumptions on the function class of the time-dependencies of the ODE. In contrast to other approaches, no assumptions on the parameterization of the serial interval distribution are required for the estimation of SIRD-model parameters. With the presented method, we are able to adequately describe time series of COVID-19 data in Germany including strong fluctuations and multiple waves, and to give non-parametric model-based time course estimates for the effective reproduction number. [ABSTRACT FROM AUTHOR]

A common network model of interactions is a graph. It is a metric space in which the distance between two nodes is defined as the number of edges of the shortest path connecting the two nodes. This definition of distance assumed in many mathematical models for applications in various fields may not reflect the latent geometry that underlies the structure of the network. The most frequently assumed model of metric space is that of Euclidean space, often without verifying that this assumption is plausible or supported by experimental data. It therefore becomes very important to develop methods that are able to identify the latent geometry of a network before constructing a mathematical model of its static and dynamic properties. With this motivation, I present here a method for the determination of the latent geometry of a network and show its application on a real-life case study concerning the protein-protein network of the SARS-CoV-2 virus. [ABSTRACT FROM AUTHOR]

The analysis of stock markets can be done through an existing scientific approach, used in the field of physics. We can make a correlation between the stock market and the theory of open thermodynamic system and its negentropy. Stock markets are not isolated. They are continuously influenced by the existing information on the market, so that within the system, which tends towards a state of equilibrium, this external factor disturbs the initial state of equilibrium. We can deduce that markets can be influenced by a multitude of other factors (also, markets can influence each other) and can be influenced from the outside (different financial events that cause increases or decreases in market values). Thus, we find in the "econo-dynamic system", an energy transfer based on the exchange of information in the markets. We will focus on the evaluation of capital market negentropy. In this study, we will analyze the elements that make us associate a stock market system with a thermodynamic one. In the end, we have shown the Covid's (SARS CoV-2) quantitative impact on capital markets, especially concerning two stock market indicators, Dow Jones Industrial Average and Nikkei 225, respectively. [ABSTRACT FROM AUTHOR]