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Background: COVID-19 has become a global concern as it has ended the lives of lakhs of people. The study done by. Lin proposed the results of an analysis of a theoretical computational model of epidemiology taking into account the crucial components such as response of an individual behavior, measures taken by government, rapid transmission and emigration of fever virus to a huge extent of human population. Simple model framework has been used by them to examine the requirements of essential elements.Problem: We claim the perfect validation of the assumptions stated for the purpose of modeling and its analysis, however, the analytical solution of the presented model is not discussed. And thus have a poor potential to make computational estimates.Methodology adopted: We have tried to present and validate the mathematical/analytic solution of the developed model, considering their assumptions.Conclusion: We have reached to the outcome that the work presented by Q. Lin should be demonstrated adequately with a cautionary advice of not to consider the results and analysis of such numerical models.The current study proposed the results of an analysis of a theoretical computational model of epidemiology taking into account the crucial components such as response of an individual behavior, measures taken by government, rapid transmission and emigration of fever virus to a huge extent of human population. Simple model framework has been used by them to examine the requirements of essential elements. We claim the perfect validation of the assumptions stated for the purpose of modeling and its analysis, however, the analytical solution of the presented model is not discussed. The decision point of this work is to adequately demonstrate along with the cautionary advice of not to use the outcomes and analysis of such numerical models. [ABSTRACT FROM AUTHOR]

In a pandemic outbreak such as COVID-19, that spreads widely around the world requires in depth research to predict the pattern of its spread and strategies to reduce the number of spreads. Predictive control is a mathematical approach to control systems where a model is used to make predictions about the future behavior of a system, and these predictions are then used to determine the control actions that should be taken. Based on these predictions, control measures such as lockdowns and social distancing could be optimized to reduce the spread of the virus and minimize its impact. A dynamic model is needed for quick and precise handling decisions. Mathematical models Epidemiology can be used in various fields, the implementation of which is COVID-19. The presentation of a dynamical system can use the basic compartment model, in which critical 퐶 and death 풟 states are considered, addressing more possibilities of mutual transitions between compartment states. 풮ℰℐℛ풞풟 estimates and forecasts the COVID-19 spread under uncertainties and constraints. This paper focuses on This paper focuses on fit and predict the 풮ℰℐℛ풞풟 model with time dependent basic reproduction numbers and health resource-dependent death rates to real COVID-19 data in Indonesia. Data have been collected for a specific period of time, the best-fitting parameters have been obtained, and the changes in the basic reproduction number over time have been estimated. The presented model has included the transition probability of the infected state to the critical state and the transition probability of the critical state to the death state, based on different age groups, so that the number of hospital beds has also been cooperative in computing the compartment states. Using the best-fitting parameters of the 풮ℰℐℛ풞풟 model, the development of the compartment states in the near future was estimated. The simulation result shows that the obtained parameters of the 풮ℰℐℛ풞풟 model were reasonably satisfying where the simulated states from the model could fit the real data with a small degree of error so that it can be used to predict the path of further deployment. [ABSTRACT FROM AUTHOR]

In this paper, we describe the dynamics of the spread of the COVID-19 disease. In this paper, a mathematical model of the problem is rewritten. Mathematical models can be used to find strategies to prevent the Coronavirus disease spread. A Susceptible-Exposed-Infected-Recovered (SEIR) mathematical model in the spread of COVID-19 disease is solved using fourth and fifth-order Runge-Kutta methods. The simulation results of the two methods show the same pattern of solution behavior as the results of the reference solution obtained using the ODE45 algorithm in the MATLAB software. Theoretically, the accuracy of the fourth-order Runge-Kutta method is not better than that of the fifth-order Runge-Kutta method. We show the correctness of this theory through simulations in this paper by solving the SEIR mathematical model for the COVID-19 disease. [ABSTRACT FROM AUTHOR]

This concept paper explores the challenges faced by employees from various industries as the world faces the COVID-19 pandemic at this point. The current situation finds that employees cannot go out to work face to face or are not allowed to come to the office physically as usual as a result of this COVID-19 pandemic outbreak. Furthermore, all citizens from almost all countries are involved in the implementation of the movement control order (MCO). However, jobs that involve day-to-day operations in the organisation still need to carry out their activities as usual for the sake of the survival of their respective organisations. Thus, there are various online platforms that are used to enable activities in the organisation to continue in the interest of stakeholders. To continue daily work activities there are several online platforms that are often used as communication media in organisations such as 'Google Meet', 'Google Classroom', 'Zoom', 'Webex', 'Microsoft Team' and 'Skype'. When the COVID-19 pandemic situation was announced to have subsided, there were organisations that adopted hybrid methods where online and face-to-face communication media were practiced for the purpose of discussions and meetings. This concept paper aims to identify the challenges faced by employees throughout working from home (WFH) when faced with this COVID-19 pandemic. Researchers have proposed three (3) recommendations in facing employee challenges throughout WFH with reference to Sociotechnical Systems Theory (STS) and Motivation-Hygiene Theory based on reference highlights of papers reviewed from 15 journals in previous studies. The results of this study can contribute to knowledge and in addition to the highlights of the work for this topic, especially to employers and employees who practice the concept of WFH. [ABSTRACT FROM AUTHOR]

The Coronavirus Disease 2021 or COVID-19 has affected almost all individuals around the globe. Consequently, Malaysia is one of the nations that has been facing tremendous changes in various sectors due to COVID-19. As the number of positive cases is on the rise, the Malaysian government has executed its National COVID-19 Immunization Program to reduce the spread of COVID-19 and the severity of disease in case a vaccinated person is infected. However, there are also individuals who have contributed to vaccine refusal due to many factors including misinformation about vaccines, religious beliefs, lack of knowledge, and awareness. Therefore, this paper presents a combination of instructional design and cognitive load theory named CoLoMo model, to increase the awareness of COVID-19 among parents. The CoLoMo model is a theory for mobile application development involving three elements in mobile instruction design which include platform, design, accessibility, and design page. These elements have been chosen prior to the target users which are parents or adults. The three cognitive load theory types in this model are intrinsic, extraneous, and germane cognitive load. The combination of the mobile interaction design and cognitive load theory types will affect the user's cognitive performance which are concentration, learning capabilities and attributes. This paper also discusses the previous research on the implementation of cognitive load theory through mobile applications. Several strategies that emphasize different relevant cognitive processes which are useful to consider when developing mobile applications are also discussed. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak impacted drastically to education and most of educational institutions started preferring online education for students. However, after the settlement of the pandemic there is uncertainty among people about whether they should prefer online education for furthermore or start in offline mode to make it more interactive, so this paper is about an analysis of people's sentiments and emotions through Tweets about COVID-19 Education. This paper aims to study the reaction of people around the world toward online education during COVID-19. This study is conducted on the basis of the responses of students, teachers, parents, college professors, etc. We started with labeling the data into three sentiments namely positive, neutral, and negative and for validation then we used Machine learning (ML) classifiers namely, Logistic regression, Decision tree, Random Forest, Multilayer Perceptron (MLP), Naïve Bayes, Support vector machine (SVM), K-nearest neighbors (KNN), and XG-Boost. Then we performed emotion detection by considering 5 emotions namely happy, surprise, sad, fear, and angry and for validation we used ML classifiers. After applying all these ML approaches, the XG Boost ML classifier achieved the highest accuracy of 94% in classifying the tweets as positive, neutral, or negative, and 96% accuracy in classifying the tweets as happy, surprised, sad, fearful, or angry. [ABSTRACT FROM AUTHOR]

COVID 19 disease has spread worldwide, disrupting the economy, society, education, and health. It became crucial to understand the transmission curve and identify the best action in combating this highly transmissible epidemic. Epidemiological parameters of the deterministic Susceptible-Infected-Removed (SIR) model are widely used in explaining the characteristics and behavior of the disease spreading. Disease like COVID-19 is highly influenced by the uncontrolled factors of randomness, hence in this paper, the deterministic susceptible-infected-recovered-death (SIRD) model is extended to the stochastic SIRD (SSIRD) model. SSIRD model takes into consideration the noisy behavior of the process which explains the uncontrolled effects of the COVID-19 outbreak. The epidemiological parameter of the model changes throughout the epidemic due to external factors such as enforcement and public obedience to the control measures as well as the changes in healthcare facilities. These parameters need to be estimated. This paper estimates the epidemiological parameters of the SSIRD model using the Markov Chain Monte Carlo (MCMC) method of the Metropolis Hasting algorithm. The COVID-19 data from Malaysia and Indonesia are used and the dynamical behavior of the COVID-19 outbreak in both countries is simulated. The results show the parameter changing due to the uncontrolled factors influencing the trend of the pandemic curve. [ABSTRACT FROM AUTHOR]

Today, the world is exposed to many widespread health crises. One of these crises is the emergence and spread of the covid-19 virus, where any person becomes infected with this disease through the rapid transmission of infection between people, either by palpation or by inhaling droplets of carriers of this virus. Hence, there is a need to build a tracking system to respond quickly to problems. This paper proposes using one of the unsupervised machine learning algorithms, the K-Means Clustering Data, to determine the areas with the most significant percentage of infections by dividing them into infected and uninfected human groups. Where the infected persons are divided into three high groups (C1), medium (C2), and low (C3), and follow the path of the infected persons using Google Maps. The contribution to this paper is to support appropriate decision-making by public health authorities by predicting endemic areas and how to address problems that occur as they can use this system to reduce other infectious diseases. [ABSTRACT FROM AUTHOR]

Coronavirus (Covid-19) appeared at the end of 2019 in the Chinese city of Wuhan, and it should be mentioned that there are research centers in both America and China that have worked on these viruses for many years ago before the appearance of this virus. At the beginning of the year 2020, this virus has spread widely in most countries worldwide and has led to great human and material losses, and the World Health Organization considered it a pandemic. A review analysis is conducted to be focused on many factors affecting the published papers on Covid-19 that applied to Science Direct library on 7/4/2020. We observed that most research studies have shown that this virus attacks the human respiratory system, which leads to shortness of breath and also leads to death in some cases. The obtained results showed that most of these research papers are focusing on statistics (41%), medicine (40%), management (17%), and healthcare (2%) respectively. Statistical analysis is helpful to understand the orientation of research papers published on Covid-19. Statistics and medicine respectively, occupy a large part of the research side of the Covid-19. It is a short time since the spread of the Corona virus, so research results cannot reach maturity. [ABSTRACT FROM AUTHOR]

Due to the imposing of the lockdown in India because of Covid-19, the boiler units in industries and power plants get shutdown. It leads to chances of occurrence of downtime corrosion in boilers. Generally, the downtime corrosion is said to be negligible as compare to corrosion that occurred during the working of boilers. However, if the shut down is longer than some weeks then this corrosion cannot be negligible at that level. It results in the loss of life of the material of the boiler and its parts. Thus this paper aims to point out the types of corrosion that can occur and preventive measures needed to be taken for the different types of downtime corrosion. [ABSTRACT FROM AUTHOR]

In this paper novel making use of a CNN to forecast the spread of COVID-19 and comparing its accuracy with Logistic Regression is the main objective. Applying Logistic Regression and Convolutional Neural Networks to Regression are two groups used each with twenty samples to assess the accuracy of COVID-19 forecast. We used G power with an 80% pretest power to decide on the sample size. The CNN and LR algorithms were trained using data models that included both normal and COVID-19 chest X-rays. The accuracy of CNN is 98.5 % and LR has an accuracy of 77.7 % with a statistical significance of 0.496 (p>0.05). Compared to other methods, Convolutional Neural Networks use a to the Logistic Regression. [ABSTRACT FROM AUTHOR]

This paper examines the dynamics of the spread of the BA4 and BA5 variants of the COVID-19 virus in DKI Jakarta Province using the SIR Model. The parameters contained in the model are assumed to change over time according to the daily case empirical data. Based on the available data, the value of these parameters is estimated using FIR filtering and viewed as an optimization problem solved by the ridge regression algorithm. The model formed is used to predict the number of cases of the spread of COVID-19 for the next few months. The results obtained indicate that the model can produce stable output and it is predicted that COVID-19 for BA4 and BA5 variants will subside for the next few months. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to prepare the most commonly used cluster analysis; hierarchical and non-hierarchical cluster analysis algorithms which are the K-means, the Partition Around Medoids (PAM) and the agglomerative hierarchical, to group 37 African countries on the basis of measures of COVID-19 cases, economic development and general health resources. We found that the optimum number of clusters is four and countries like Morocco, Algeria, Libya, Gabon, Botswana, Tunisia, Mauritius, and Seychelles are grouped together into one cluster, and how Egypt and South Africa formed another cluster. A Hierarchical clustering was found to be more precise compared to the other two algorithms. [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]

In this paper a mathematical model is developed to identify the meantime to respiratory failure in Covid-19. According to this model the respiratory failure occurs whenever the total number of damaged cells in lowers respiratory and air sacs in lungs of the infected exceeds their corresponding thresholds. Due to uncertainty, these parameters are considered as random variables. The performance measures of respiratory failure are derived by assuming suitable distributions for the random variables. Finally, the performance predictions are numerically illustrated. This study will help to predict the number of affected who needs ICU and also it creates awareness to peoples, which would helps to reduce the spread of Covid-19. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) outbreak from Wuhan, China has spread very quickly in the world and caused many deaths. In Indonesia, the first case of COVID-19 was reported by the government in March 2020 then it became a pandemic disease. Estimating case increase rate (CIR) and case fatality rates (CFR) are important parameters for studying the basics of pandemic disease. In this paper, we analyse the case increase and case fatality rates of COVID-19 in Indonesia based on longitudinal data and average temperature by using biresponse local linear estimator. The characteristics of case increase and case fatality rates of COVID-19 in each province are different, so locally modeling is more appropriate. The result shows, on average, the highest rate of case increase was in July 2020 and the highest rate of case fatality in August 2020. This happened because of government policies that loosen social distancing programs and increased human mobility due to the holiday in the previous month. Based on average temperature, in November 2020, there were the lowest decreassing CIR and the highest increassing CFR because in November, it entered the rainy season which makes the average temperature lower. It means that the number of COVID-19 cases will be higher if the average temperature is lower. [ABSTRACT FROM AUTHOR]

The general population is concerned about the usage of the internet as a result of advances in information and technology. News website on the internet are an example of a technology that has been developed to disseminate the most up-to-date worldwide information. When it comes to numbers, newsreaders are more than adequate for obtaining the needed information. However, as a result of the amount of data collected, there will be an information explosion as well as the possibility of information redundancy. One of the methods targeted at supporting the input query in locating the required or relevant information is the search system. In weighing to measure statistics from a collection of papers on the search for information about the Covid 19 vaccine on tempo news, the approaches TF-IDF and VSM (Vector Space Model) are used, followed by tokenizing the text to split it, stopword removal, or filtering to remove extraneous words like conjunctions and others. The next process is sentence stemming, which aims to reduce word inflection to its most basic form. News document 1 (DOC 1) has a weight of 17.242, news document 5 (DOC 5) has a weight of 11.687, news document 4 (DOC 4) has a weight of 9.243, news document 2 (DOC 2) has a weight of 5.737, and news document 3 (DOC 3) has a weight of 0. The precision with which a document's word weighting is calculated can be determined using the Term Frequency (TF) - Inverse Document Frequency (IDF) approach and Vector Space Model. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has prompted an urgent need for reliable and efficient diagnostic tools to combat its spread. Since its origin in 2019, researchers have been trying to find out effective methods for identifying and classifying COVID-19 from other similar disease like pneumonia. This review paper gives a comparable overview of various diagnostic techniques employed by various experts. Artificial Intelligence (AI) based methods, like Machine Learning (ML) and Deep learning (DL) have widely been used for predicting COVID-19. These models usually generate good accuracy rates but still there is a scope of improvement. Hence, attention was shifted towards hybrid approaches which show better detection accuracy results then individual AI models. However, both AI and hybrid models overlook human social connections and migration patterns, which are diverse and not well-mixed in populations. COVID-19 modeling should consider the heterogeneity of individuals, especially as it disproportionately affects the elderly and those with underlying medical conditions like cancer, heart disease, diabetes, and hypertension. Furthermore, people's mobility patterns and behaviors significantly influence virus spread dynamics. Therefore, role of multi-agent systems (MAS) comes into play, wherein each agent represents different data or information. However, not much work has been done in this filed and hence it needs to be explored more in future studies. Furthermore, it was noted that a significant proportion of researchers favored the utilization of CNN and its variants and SVM classifiers, while a minority opted for RF and regression models for the purpose of classification. Overall, this review provides insights into the strengths and limitations of each approach, emphasizing the need for a comprehensive strategy that harnesses multi-agent systems for COVID-19 detection. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT)-based health monitoring devices may be very helpful for COVID-19 pandemic patients. The main goal of patient monitoring in post-covid life is to have a quantitative assessment of the vital physiological markers of patients. An IoT-based health monitoring and surveillance system is presented in this paper after the COVID pandemic. This device monitors a patient's oxygen saturation level, heart rate, temperature, pulse rate, and systolic and diastolic blood pressure. New evidence suggests that ML and AI approaches could considerably mitigate the viral attack. In this article, we establish a collaboration between an IoT system and a machine learning prediction model to keep tabs on people's health in the post-COVID age. The algorithm's severity forecasts for patients were very accurate, taking into account a number of criteria including laboratory data and vital signs. The data was analysed using two classification techniques: random forest (RF) and support vector machine (SVM). Multiple techniques were used to pre-process the data. Outperforming the SVM, RF achieved a score of 0.956. [ABSTRACT FROM AUTHOR]

On data that are not normally distributed, classical linear regression is developed into the Generalized Linear Model (GLM). One of them is logistic regression which is used for binary discrete data. A different strategy is to consider all variables (response and predictors) to be random observations from the joint probability distribution. A model with dependencies should be used because some factors are dependent. The copula model, one of which is the Gaussian copula, is a popular technique for creating multivariate models with dependencies. The Gaussian copula regression model (GCMR) is a general structure that can be used to model any form of dependent response. The Gaussian copula blends the ease of interpretation of marginal modeling with the flexibility of the dependency structure specification. In this case, the estimated parameter is the regression coefficient and the error is expressed in the dependency structure. The model estimation uses the maximum likelihood method. This paper aims to apply the GCMR model to time series data where the dependent variable is discrete with a binomial distribution. A case study was conducted to see the effect of weather factors on Covid cases in DKI Jakarta. The regression model used is binomial data logistic regression with the dependency structure expressed in the correlation matrix with the assumption that the error is obtained from the ARMA(p,q) process. The dependent variable is the number of Covid-19 cases and the independent variable is the maximum temperature, average temperature and humidity. The data are a daily time series with a range of March 1, 2020 to April 30, 2022. Based on the GCMR model, Maximum temperature, average temperature and Humidity have a significant influence on the number of Covid-19 cases in Jakarta. For this case study, the results show that the GCMR model is better than logistic regression analysis. [ABSTRACT FROM AUTHOR]

In this paper, the population was divided into seven subpopulations, namely susceptible (S), vaccination (V), exposed (L), infected not reported (I1), infected reported (I2), quarantine (Q) and recovered (R) subpopulations. Analytical study on the COVID-19 model, to determine the non-endemic equilibrium point, reproduction number, and system stability. The model solution was determined by numerical analysis, and model sensitivity analysis by changing the values of the vaccination rate and treatment rate parameters. Based on numerical simulation results, if the value of the vaccination parameter or the value of the treatment parameter increases, the number of individuals in the subpopulation infected with COVID-19 decreases, and the number of individuals recovered from the subpopulation increases. [ABSTRACT FROM AUTHOR]

The majority of individuals on the earth have been affected by COVID-19 as a result of which researchers from across the psychological sciences are trying to document and examine the influence of COVID-19 in.a number of ways. This paper offers a rapid and comprehensive summary of high-quality studies that are available through early 2021 that examine the mental-health issues during COVID-19 pandemic. This is required because policy and practice aimed at assisting people live healthier and happier lives requires an insight into robust patterns of evidence. After the careful analysis of the available literature it was revealed that there was an increase in anxiety, depression, despair, and distress in the early stages of the epidemic. Throughout the first year of the epidemic, however, the rates of suicide, life satisfaction, and loneliness remained relatively steady. This paper also offers suggestions to support mental health both during and after the epidemic in response to these discoveries. [ABSTRACT FROM AUTHOR]

Coronavirus diseases 2021 (COVID-19) on going situation in Iraq is characterized in this paper. The pandemic handling by the government and the difficulties of public health measures enforcement in Iraq. Estimation of the COVID-19 data set was performed. Iraq is endangered to the pandemic, like the rest of the world besides sharing borders with hotspot neighbouring country Iran. The government of Iraq launched proactive measures in an attempt to prevent the viral spread. Nevertheless, reports of new cases keep escalating leaving the public health officials racing to take more firm constriction to face the pandemic. The paper bring forth the current COVID-19 scenario in Iraq, the government measures towards the public health challenges, and the future potential health risks that may emerge. [ABSTRACT FROM AUTHOR]

The spread of the Corona Virus epidemic (COVID-19)arouses the human normal life and seen a million of death around the world. Researchers are designing machine learning models to predict and judge the spread of COVID-19 epidemic. The significance of this paper is to predict the number of people who will be infected with COVID19 using machine learning algorithms. In this context, four models such as Autoregressive Integrated Moving Average (ARIMA), Support Vector Machines (SVM), Bayesian Ridge Regression(BRR), and Polynomial Regression (PR) have been employed to anticipate COVID-19 prediction in this paper. All models predict newly infected case's location wise including countries, states, cities etc. for the next 30 days. Furthermore, all models have applied on the time series dataset taken from WHO's official website and build a comparative study on five evaluation metrics such as RMSE, MAE, MSE, MAPE, and R2. The feature selection has been performed on the raw datasets via. Time series properties and demographic attributes. The results showed SVM outperformed the other models, while in the available dataset the Polynomial regression performs poorly in all the predictions. This model also scales on different geography, country and city. [ABSTRACT FROM AUTHOR]

The healthcare information dissemination on the diagnostic procedures and medicines is the need of the day to combat against Covid-19. The governments approved for the treatment protocol of Home Isolation for mild symptoms & no symptoms yet Covid-19 infected patients. The paper discusses the issues and concerns of an individual in treatment to Covid-19. Objective: To analyze with the existing research available on the issues and concerns in management of covid-19 among the patients. The science research databases revealed that self-medicated was exhibited among individuals with the Over-the-Counter (OTC) medicines. The information dissemination due to vast coverage brought in fear and ambiguity in the educated and uneducated population Methodology: The bibliometric analysis is carried to know the area of research undertaken by researchers across with the misuse of drugs, issues with home confinement and the concerns of public to get the vaccinations for the prevention of Covid-19. Conclusion: the paper concludes by stating the governments across have to formulate a strategy in disseminating information to public (an educative approach) but not misleading. Also, reveals the role of government, health-care providers, media-partners and the NGOs are high in disseminating information on drugs and their usage in prevention and management of covid-19. [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]

In our paper discussed about the advantages and some difficult aspects that have impacted upon Uzbek universities and technical institutions (UzHEIs), which are continued to train students during of COVID-19 pandemic time. This reflects the impact of the unprecedented COVID-19 on the educational services of higher education institutions. Suddenly, there was an expectation that universities would comply with COVID-19 prevention measures, including social distancing and hygiene. As a result, many universities have switched to the remote transfer of knowledge and courses. This paper encourages university leaders to use online learning methods and distance education systems, which are support universities continue educational processes in the post-COVID-19. Paper summarizes by demonstrating main findings for university rectors and identifying directions for future research for academia. [ABSTRACT FROM AUTHOR]

This paper studies the impact of a covid-19 pandemic on the global economy. The empirical analysis of the work is based on data before and after (2019-2021) covid-19 and uses OECD data figures. The paper uses comparative analysis to find out changes during the pandemic and at the height of the pandemic, the global economy has passed the lowest point of the crisis, and it began economic growth in the leading countries of the world. This growth, like the crisis, developed in different ways in the economies of the US, EU and China. Our results show that during the pandemic, as a result of the lockdown, almost simultaneously, there was an abrupt increase in unemployment and a decline in production in all countries. In OECD countries, unemployment jumped from 5.2% in February to 8.5% in April, in the US - from 3.5% in February to 14.4% in April, in China - from 5.3% in January up to 6.2% in February. In China, both the epidemic and the economic crisis began earlier than in all other countries, respectively, the economy began to grow earlier. Our findings come to conclusion in a such way, despite the fact that, according to the forecast, the damage in the medium term will be less than after the global financial crisis, it is still significant. So, in 2024, the expected level of world production will be about 3 percent lower than before the pandemic. Countries differ in the degree of expected scarring, depending on the structure of the economy and the scale of the policy response. Emerging market and developing economies are predicted to face more palpable scars than advanced economies. [ABSTRACT FROM AUTHOR]

The sudden appearance of the extremely infectious COVID 19 virus has thrown the whole globe into disarray and compelled a transformation in the structure of the global economy so that it can adjust to the new conditions. The education industry, which formerly placed a significant emphasis on more conventional forms of contact such as face to face communication, has been obliged to embrace the concept of online learning. This shift, however, has not been an easy one, and the bulk of them are still having trouble converting into an e-learning platform that operates full-time. Both students and teachers need to make the transition from traditional classroom instruction to online learning platforms for the ecosystem of digital learning to exist. Parents, policymakers, and educational institutions need to play the role of facilitator to ensure that there is as little disruption as possible to the education process during this transition. Several educational establishments were first hesitant to abandon their time-honoured instructional approach in favour of the more modern approach of online learning, but with time, many were compelled to make the transition. The purpose of this essay is to suggest a developing model for efficient e-learning by including the important aspects that are necessary for online delivery under crisis conditions similar to those experienced by Covid. It also sheds light on the difficulties that important stakeholders are having in adjusting to the e-learning process as well as the potential solutions to those difficulties. Previous research has not concentrated on eLearning models that are applicable during times of emergency. Research Methodology – The authors want to conduct a comprehensive literature evaluation of a wide variety of publications and research papers related to Covid and its effect on online education in order to achieve their goal of developing a conceptual paper. In the event of a crisis comparable to a pandemic caused by the Coronavirus, an online learning paradigm is suggested for academic institutions. [ABSTRACT FROM AUTHOR]

In 2019, the TL0200559 project entitled "Safe Cities for Pedestrians and Seniors" was launched. Part of the solution of this project was also a survey and analysis of the use of pedestrian roads in selected cities. The town of Chotěboř was chosen for pilot processing. In the first half of 2019, a questionnaire was compiled for the questionnaire survey. At the same time, a local survey was conducted. In October and early November, data were collected by a questionnaire survey. In the following five months, the digitization of paper forms of the questionnaires took place. Their evaluation was interrupted by ongoing quarantine measures due to SARS-CoV-2 (Covid-19). In 2021, quarantine measures (especially the return of children and students to full-time school attendance) were relaxed to the extent that it was possible to return to the standard scientific research work and analyze and verify the obtained data by re-conducting local surveys. The procedure for compiling the questionnaire for this survey, the course of the actual data collection, and the results of this pilot survey are presented in this paper. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, the world has witnessed rampant use of digital technology in almost every facet of huma lives. However, these digital techniques like use of mobile apps have certain drawbacks and constrains. These could be addressed with the use of emerging technologies like Internet of Things (IoT). In healthcare, that is Internet of Medical Things (IoMT). A number of ongoing studies show that integrating security measures with technology will lead to the adoption of stable IoMT applications. Emerging IoMT technologies used in conjunction with Blockchain, Artificial Intelligence, and Big Data, provide feasible solutions. The paper focuses on IoMT models, innovations, and security progress made to tackle COVID-19 issues. This paper contains the bibliometric research highlighting the use of disruptive technology for the diagnosis of COVID-19. It reviews the adoption of contactless services in the post-COVID-19 period along with potential opportunities as well as challenging obstacles for healthcare agencies, policymakers, and customers. [ABSTRACT FROM AUTHOR]

In 2019, the TL0200559 project entitled "Safe Cities for Pedestrians and Seniors" was launched. Part of the solution of this project was also a survey and analysis of the use of pedestrian roads in selected cities. The town of Chotěboř was chosen for pilot processing. In the first half of 2019, a questionnaire was compiled for the questionnaire survey. At the same time, a local survey was conducted. In October and early November, data were collected by a questionnaire survey. In the following five months, the digitization of paper forms of the questionnaires took place. Their evaluation was interrupted by ongoing quarantine measures due to SARS-CoV-2 (Covid-19). In 2021, quarantine measures (especially the return of children and students to full-time school attendance) were relaxed to the extent that it was possible to return to the standard scientific research work and analyze and verify the obtained data by re-conducting local surveys. The procedure for compiling the questionnaire for this survey, the course of the actual data collection, and the results of this pilot survey are presented in this paper. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, the world has witnessed rampant use of digital technology in almost every facet of huma lives. However, these digital techniques like use of mobile apps have certain drawbacks and constrains. These could be addressed with the use of emerging technologies like Internet of Things (IoT). In healthcare, that is Internet of Medical Things (IoMT). A number of ongoing studies show that integrating security measures with technology will lead to the adoption of stable IoMT applications. Emerging IoMT technologies used in conjunction with Blockchain, Artificial Intelligence, and Big Data, provide feasible solutions. The paper focuses on IoMT models, innovations, and security progress made to tackle COVID-19 issues. This paper contains the bibliometric research highlighting the use of disruptive technology for the diagnosis of COVID-19. It reviews the adoption of contactless services in the post-COVID-19 period along with potential opportunities as well as challenging obstacles for healthcare agencies, policymakers, and customers. [ABSTRACT FROM AUTHOR]

This paper is a study about COVID-19 cases in India to analyze and visualize the spread of COVID-19 cases in INDIA. For the analysis, the concept Exploratory Data Analysis (EDA) and some well know prediction models like Prophet Model, ARIMA Light GBM, Random Forest Regressor, XGBoost Regressor have been used to get the correct analyzed result. Furthermore, it has been observed that these prediction models and tools are helpful to analyze and visualize the COVID-19 situation in India and reason to spread it in optimal ways. Here, Matplotlib library has also been used to show the proper output in the form of graphs and charts. Analysis has been done on different Age/Gender Group. The Spike of Cases in India, State-wise Insights, the reason for the spread of COVID 19 cases. So, the main objective of this paper is to do analysis to know the reasonforCOVID-19 cases in India, State-wise Insight and State-Wise Testing, Prediction. To understand the optimal result of growth factors, the concept of Prophet Model ARIMA, LightGBM, Random Forest Regressor, XGBoost Regressor have been used on the data set. In addition, the data set includes the parameters of Age Group, Spike of Cases and Testing in India. From the literature survey, it has been observed that authors have used two and three techniques to analyze the result, but they have not used the methods we stated earlier for optimal result. This paper will provide the State-wise Insight and State-Wise Testing going in India with reason for spreading of COVID-19 cases. [ABSTRACT FROM AUTHOR]

The world has been living with the Coronavirus Disease 2019 (COVID-19) pandemic for more than two years and it has altered humanity in numerous ways, by the implementation of lockdowns, border closures, other movements, and social restrictions. The pandemic has also led to the deferral or termination of all events including sports, religion gathering, political elections, and social events. Industries, in particular oil and gas, entail to react within a short span of time to productively manage the stakeholders. As workforces began to work from home, business meetings, discussions, and engagements were swapped by video calls, and businesses around the globe began their contingency planning. The stakeholder management becomes critical when there is no standard procedure available under the circumstances, thus this paper aims to propose procedures on improving the stakeholder management in petrochemical projects during the COVID-19 pandemic. The paper assesses the impacts of the pandemic on stakeholder engagement in petrochemicals projects, examines current coping strategies in different projects, and recommends several practical actions to promote and maintain meaningful exchange with stakeholders in times of social distancing and lockdowns. The outcome of the paper enlists various improvement levers for a petrochemical company to adapt to ensure project stakeholders are satisfied and well-informed. [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]

World Health Organization (WHO) recommend usage of mask as an operative prevention to avoid spread of Covid 19. Various reports present that wearing face masks top virus transmission and reduces the risk of transmission. This paper focus on cloud-based AI methodology for the detection of face mask. A mask detection data base with two categories of information includes with mask and without mask images that are stored in cloud data Centre via webcam. The proposed work uses Full Convolution Neural Network (FCNN) and Gradient Boost Machine Learning (GBML) model for classification and detection mask. This paper focus on the objective of to recognize whether the person on image or video stream is wearing a mask or not and not wear properly. [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]

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]

The present study is about the detailed analysis of the pandemic Corona Virus 2019 which has rocked the entire world with a maximum impact with its structure, its appearance and nomenclature, Clinical presentation and transmission, diagnostic technique. It's a respiratory illness caused by the virus SARS-CoV2 (Severe Acute Respiratory Syndrome). It's a syndrome is different from other virus syndromes as this might act as Symptomatic also as Asymptomatic. The common symptoms would be cold, fever, cough, sneezing, running nose, breathing issues, Fatigue, etc. The paper may be a vivid picture on the COVID 19 cases in Tamilnadu which is within the Southern part of India and therefore the treatments like Ventilator, Plasma, etc and therefore the remedy offered to the patients like Naturopathy, Ayurvedic, Siddha, Unani, Homeopathy, etc,.for better recovery and immunity to fight against the virus. It analyses the detailed reports and statistics during a regression form with reference to daily new death rate and total death rate with the assistance of statistical tools of the general rectilinear regression equation. the entire confirmed cases and daily new confirmed cases are plotted using Normal P-P plot of regression Standardized Residual. The Pearson correlation value is 0.837 which features a strong direct correlation between active cases and total recoveries. [ABSTRACT FROM AUTHOR]

Current pandemic situations have worsened the economic situations in most developing countries. Malaysia is one of a developing country that has been impacted by covid-19 badly. In Malaysia, cases of Covid-19 is arising everyday especially in the year of 2021. Many have losses their job and income, and they were out of resources to support their families, and providing food for families becomes a challenge. Due to that, students who have been sent to home from university have to face this as well. Therefore, this paper intends to highlight food security and its impact towards academic performance by providing framework and determines the research trends of the area of food security and academic performance. A conceptual framework here explained the progress and event that may happen in future if unbalance diet were practiced by the community. Next, this research adopts a method of web-based data search for studies related to food security and academic performance. Result shows that the trend of this research is keep increasing according to the year. Current research concludes that food security is important for nation wellbeing and sustainable development of a country. [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]

The Indonesian government has taken several policies to break the chain of transmission of the COVID-19 virus infection since the spread of this virus variant was first discovered in March 2020 in Indonesia. One of them is to require everyone to get vaccinated. This national vaccination program has been started since early January 2021 until now and expected to be one of the solutions to control the spread of coronavirus in Indonesia. This paper presents robust k-means to show the cluster of the spread of COVID-19 in Indonesia after the implementation of the vaccine policy for Indonesian citizens. The k-means algorithm is an unsupervised machine learning method. This method is well known in clustering. This is a partition clustering algorithm which is widely used in practice but in general, this method is sensitive to the presence of outliers. The variables considered in this study are the number of deaths, the number of recovered patients and the number of patients. Experimental comparisons are made with the basic k-means and robust k-means algorithm. It seems that for data on May, 2021, the cluster with k=3 in the robust case will provide closer risk levels of the provinces in Indonesia with their risk in reality, namely provinces with high, mid and low-level risk. [ABSTRACT FROM AUTHOR]

Coronavirus Disease (COVID-19) pandemic has emerged as one of the biggest challenge faced by researchers, especially in public health system. Since the virus spread and mutates quickly, strategies are needed so that the public health system doesn't collapse due to overcapacity. Modeling and forecasting accurately the COVID-19 daily new cases is very important to understand and help carry out risk management for the outbreak control. To obtain the estimation of the Indonesian daily cases of COVID-19 based on daily confirmed cases, we present a comparative study between three deep learning methods in this paper: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Long Short-Term Memory (Conv-LSTM). The data used is daily confirmed cases from September 1st to September 7th, 2021. The result is Conv-LSTM model produces better performance than other methods. Here we also provide 7 day out-of-sample forecast for daily cases from September 1st to September 7th, 2021 using each model. [ABSTRACT FROM AUTHOR]

Covid-19 is a deadly disease that has spread all over the world. It has a larger social influence. There are various ways for diagnosing the virus, including RTPCR, Xray, and others, but chest computed tomography(CT) is one of the most effective. CT images are employed to identify if there any abnormalities and level of abnormalities identification of abnormalities in CT images is highly challenging. This paper focuses on the classification of CT images into normal or abnormal and also to localize the lesions in the CT images. Here lung lesion localization for accurate screening of covid 19 in CT images is proposed by incorporating transfer learning and K-mean clustering. The proposed approach is based on Resnet 50 architecture followed by dense network for classification [ABSTRACT FROM AUTHOR]

The increase in world population witnesses the birth of strange diseases/viruses such as covid 19 that challenge the present healthcare system, but it also leads to the development of new technologies that help us fight those diseases/ viruses. The Internet of Medical Things (IoMT) is playing a crucial aspect in providing quality health treatment and reducing cost and error for both patients as well as frontline health workers. The goal of IoMT is to develop the advancement of intelligent and effective IoT devices in the health industry that can save countless lives by using advanced sensors and communications devices that help in the early prediction of critical diseases. This paper presents a succinct review of emerging technologies in healthcare, the role of IoMT devices, their applications, and the challenges that arise while implementing them. A discussion has been made on various security threats that come with the development of such devices, and their aftereffects on individuals and on health workers. [ABSTRACT FROM AUTHOR]

COVID-19 is a global public health problem that causes severe acute respiratory syndrome (SARS), and it is highly infectious with increasing mortality and often changing transmission forces. In this paper, we employ a mathematical model with SEIR-type (Susceptible–Exposed-Infected–Recovered) kinetics that takes into consideration the governmental actions and public sentiments. This model is analyzed to comprehend how the combined effects of several epidemiological factors (e.g., recovery and transmission rates) and governmental-individual reactions determine the transmission dynamics of COVID-19. The governmental-individual reactions are crucial to be examined in this epidemiological system due to: (i) to better understand the feasibility of certain intervention strategies before (and after) these measures are being implemented by the government; (ii) to comprehend the impacts of these distinct intervention measures to local communities; (iii) to create awareness and facilitate the public adherence to different preventive measures imposed by the government. Using bifurcation analysis techniques, we discover qualitatively different dynamical behaviours such as disease-free and endemic equilibria as the intensity of these epidemiological factors change. The occurrence of in-phase oscillatory dynamics, which would lead to recurrent pattern of endemic outbreaks is also evident in our findings. Our further analysis also proposes some of the intervention measures that can be enforced by the government and practiced by individuals to avoid the possibility of COVID-19 seasonal outbreaks throughout the years. Overall, the use of mathematical modelling approaches is important in order to better understand the interplay between different epidemiological and external factors in the transmission of infectious diseases. The knowledge extracted from the modelling frameworks can help to support authorities in making informed decisions so as to control the spread of the COVID-19 effectively. [ABSTRACT FROM AUTHOR]

Due to its extraordinarily contagious nature, the Corona virus disease (COVID-19), which initially started in December 2019, has seriously imperiled the world's healthcare systems. One of the most destructive global pandemics in history is due to its great infectiousness and high mortality rate. As a result, it is crucial to implement a variety of techniques to quickly identify the Corona virus. With the enhancement of early diagnostic and disease severity detection technologies the load on the healthcare structure can be lessened and the effectualness of treatment and rehabilitation for patients can be increased. In light of the severity classification of COVID 19 procedures, this survey analyses numerous methodologies. 25 research papers that focused on different techniques will be used in this study, and an overview of the techniques will be given. The analysis will then be highlighted in the work based on the year of publication, performance metrics, software utilized, datasets and success of the study approaches toward various severity levels of COVID 19and the most utilized techniques related to COVID-19 severity classification is deep learning techniques. At last, drawbacks and issues involved in COVID 19 severity classification techniques will be considered concluding declaration and future scope. [ABSTRACT FROM AUTHOR]

In 2020, the Covid-19 virus has become a global pandemic, which started in the Chinese province of Wuhan and spread around the world, The aim of the present study is to discuss the most important and latest methods of tracking Covid-19 patients and the strategies followed by developed countries in integrating digital technology to control the epidemic, it also covers the most important issues and discusses the difficulties these technologies as well as how people interact with and feel about the use of this type of technology. Among these methods used, are those developed by technology companies in cooperation with telecom companies, such as Covid-19 patient tracking applications that rely on raising case information. These applications have been effective (GPS, Wi-Fi, and Bluetooth), as also the most important prevention measures to be followed and symptoms that are likely to appear on infected or carriers of the virus have been announced by global health agencies, which have been accepted all over the world. In this paper we present. A progressive of digital solutions technologies from the methods used to confront COVID-19 since the appearance of the first case used to control and stop the spread of the pandemic and track infected people, to the contemporary technologies in use, depending on the country used for each of these strategies as well, in addition to the most prominent issues that impede these preventive methods. Then there are the advantage and disadvantages of these technologies, by analyzing the different features of contemporary technology in order to combat the COVID-19 pandemic, we provide a window of ideas for reviewing technological advances used to reduce and control the spread of the epidemic. Despite the emergence of various studies related to modern technology towards COVID-19, there are still limited applications and contributions of technology in this battle. On-going progress in modern technology has contributed to improving people's lives and hence there is a solid conviction that validated research plans including artificial intelligence will be of significant advantage in helping people to fight this infection. [ABSTRACT FROM AUTHOR]