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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 tourism and hospitality sectors were among the most negatively impacted industries during the COVID-19 pandemic. Many studies have reflected on the impact of COVID-19 on the tourism and hospitality industry; however, more needs to be written about the effect on the customers. Therefore, this paper aims to report if COVID-19 has affected the use of hospitality establishments and service customers during and after the lockdown in South Africa. This was a cross-sectional quantitative study. Google Forms were used for data collection using snowball and convenience sampling techniques. Seven hospitality establishment categories (Bed and breakfast, guesthouses, hotels, Airbnb, sit-down restaurants, fast food outlets, and wine farms) and two hospitality services (Catering services and delivery services) participated in the study. Data were analysed using SPSS version 26 and presented using descriptive statistics. Demographic data was introduced to give an overview of all the respondents. Most respondents were female (64%) and Africans (69%). Most respondents were employees (49%), earning salaries ranging between R5000 and R50 000. Most customers indicated that they continued to use these establishments and services during the lockdown and were willing to continue doing so post-lockdown. In terms of salaries versus the categories of establishments, customers who earned less visited establishments the most. However, it was observed that customers who visited restaurants were from all salary ranges (brackets). This indicates that customers always continue visiting hospitality establishments and utilising hospitality services. This affirms the importance of the hospitality industry in the social and economic space. [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]

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]

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]

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]

The COVID-19 pandemic has had a significant impact on the transportation sector, affecting travel behaviours and modes of transportation all over the world. The study questionnaire is divided into three sections: a) socio-demographic parameters; b) mode choice factors prior to and during the COVID-19 epidemic; and c) post-COVID transportation planning. The perspectives and travel choices of people from various socioeconomic backgrounds were gathered through a web-based survey. The findings of descriptive statistical analyses and statistical choice of modes modelling both prior to and during the COVID-19 era are included in the paper. Binary logistic regression models have been created for determining the tendency of people to travel for various reasons, such as work, study, and other trips. The findings found that critical employees were about 2% more likely than the overall population to travel for work during the epidemic. Furthermore, having a car improves the likelihood of making a journey for primary needs during the pandemic. Multinomial logistic regression forecasts categorical position inside or the likelihood of belonging to a category for predictor variables by using a number of unique parameters as inputs. If the overall transport sector is perceived to be improperly transferring to post-pandemic circumstances, the perception regarding public transport being unsafe may gain traction and be sustained. While executing the survey, the questionnaire was addressed by public transport guidelines. The percentages of public acceptance show how much people are willing to accept these strategies. The study's findings may be beneficial for planning transport during epidemics and determining travel constraints by considering people's travel motives and mode choices. [ABSTRACT FROM AUTHOR]

This paper presents a study on determining a conceptual of Virtual Reality (VR) of a healing environment that incorporated a physical movement. This additional activity is added to create a more dynamic experience. Dynamic experience of physical activities is acknowledged can improve human immune, where the human immune is beneficial in reducing the infection of Covid 19. The study also saw the other activities that can be a supplement feature in the scene of the VR. The steps of the study included some literature reviews on healing environment, endorphin hormone, indoor physical activities as well as to study people behavior in using social media. Subsequently to study how these aspects can be integrated in a VR scene. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the wind flow in a building that has been built and equipped with a Heating, Ventilation, and Air Conditioning (HVAC) system. With the number of COVID-19 cases still increasing every day, society and the environment must adapt to conditions that require high awareness of everyone's health. Based on previous research, it is proven that the possibility of airborne dispersal is higher in a full room, such as a classroom. One of the important health protocols is to emphasize good ventilation so that indoor air circulation can run perfectly. The notion of the passive design itself refers to the architectural design strategies used by designers to develop designs based on environmental conditions. The concept used to achieve passive design is to increase the comfort of building users. Observations were made on the 5th floor of the i-CELL building, an integrated engineering laboratory building in Universitas Indonesia, as a typical floor sample in a building to obtain data on the existing state of the building. Observations were made by measuring existing data and then simulating the recommendations for passive cooling optimization as a design intervention. As a result of this study, the recommendation to use sun shading to direct the wind into the building could potentially cause an increase of indoor airflow. [ABSTRACT FROM AUTHOR]

This paper presents a nonlinear mathematics model that is formulated and analyzed to assess the impacts of quarantine and intense medical intervention on the dynamics of Coronavirus Disease 2019 (COVID-19). An extended SEIR model with quarantine and the hospitalization compartment is proposed to simulate the COVID-19 spread. The model considers seven stages of infection: susceptible, exposed, asymptomatic, symptomatic, quarantined asymptomatic, hospitalized symptomatically, and recovered. A deterministic system of bilinear incidence nonlinear differential equations is used to model it. It was found that the model has two equilibrium points: the disease-free equilibrium point and the endemic equilibrium point. The next-generation matrix approach is used to calculate the basic reproduction numbers. The global asymptotical stability of the proposed model's equilibria is examined using the Lyapunov function and the LaSalle invariance principle. When the basic reproduction number is less than unity, the disease-free equilibrium point is globally stable, and when the basic reproduction number is more than unity, the endemic equilibrium point with a special case is globally stable, which takes the form of a deterministic system of nonlinear differential equations with bilinear incidence. Sensitivity analysis and numerical simulation results, using a reasonable set of parameter values. Finally, we investigate how the prevalence of COVID-19 is affected by the rates of transmission, quarantine, and hospitalization. [ABSTRACT FROM AUTHOR]

This research has been focused on the evaluation of COVID-19 impact on supply chain's disturbances in the European Union (EU) manufacturing industry. Export and import dependences of the EU Member States have been analyzed in relation to non-EU countries of the Organization for economic cooperation and development (OECD). In addition, keeping in mind the recent geopolitical situation, the EU trade with BRICS (Brazil, Russia, India, China and South Africa) countries has been evaluated. Risks of disruptions in supply chain of the EU manufacturing companies have been identified by careful statistical data analysis and simulation by a developed panel data model. The paper comes out with guidelines which suggest how to rearrange the supply chain of the EU manufacturing companies and help them to stay flexible under unexpected changes of the environment. [ABSTRACT FROM AUTHOR]

Health monitoring is a serious concern nowadays as there is a need for regular monitoring of some parameters in our body like temperature, heart rate, oxygen levels etc. In the present scenario, utmost care has to taken towards individual's health. As the world is now facing the issue of Covid 19, there is a shoot-up in the demand for temperature and heart sensors which can detect the temperature and heart rate of an individual. These values upon being measured can be stored in a cloud platform for further analysis. A doctor can remotely monitor a patient looking at the values stored in the database which avoids physical contact with the person. This application is a part of IoT system where there is a flow of data from bottom to top i.e. from device layer to cloud layer. This objective of this paper is providing a system which can monitor the patient's health and provide a basic treatment necessary in case of any medical supervision. [ABSTRACT FROM AUTHOR]

This paper examines the effects of COVID-19 on the capital structure of publicly listed firms in Malaysia. Using panel data regressions and total debt as proxy for capital structure, we study the relation between the leverage and asset tangibility, firm size, market to book value, liquidity, profitability, growth opportunity together with crisis dummies where it is built based on a sample of 478 firms listed on Bursa Malaysia within industrial products & services, consumer products & services, property, construction, energy, health care, plantation, technology, telecommunications & media, and transportation & logistics industries before and during COVID-19 pandemic for the period of 2016-2020. [ABSTRACT FROM AUTHOR]

This paper studied the impact of COVID-19 on SME. We study the impact of COVID-19 in terms of demand, supply, employment, long term financing and short-term financing. Survival strategies for SME during COVID-19 are also incorporated in this study. To study the impact of COVID-19 on SMEs empirically, primary data will be collected using questionnaires and will be analysed by quantitative approach. The sample size of research is targeted to achieve 384 respondents. Methods used for data collection of this research are mainly adapted questionnaires which are sourced from the public documents of the US Bureau of Labour Statistics. [ABSTRACT FROM AUTHOR]

The Coronavirus Disease 2019 (COVID-19) outbreak–first emerged in December 2019–has left a mark on how people throughout the world live, particularly students at educational institutions. Those students had to prepare for the rest of their studies emotionally and physically by adjusting to virtual teaching and learning methods, which could make a substantial impact on their academic achievement. As a result, this study was carriedout to investigate students' academic performance expectations depending on their various localities during the pandemic to forecast their academic success for the following semester. This research enables a variety of steps to be constructed to improve and maintain students' achievement in their learning activities. The research involved university students from the University of Education Sultan Idris' Faculty of Science and Mathematics. The Support Vector Machine model was used to predict a student's academic performance based on the respective locality in this paper. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop the Coronavirus disease (COVID-19) transmission model using the fractional-order differential equation defined by Caputo. This model is developed based on the susceptible-exposed-infected-recovered model, commonly known as the SEIR model. The basic reproduction number, denoted by R0, is computed using the next-generation matrix method. The disease-free equilibrium point is evaluated, and local stability analysis is performed. The analysis shows that the disease-free equilibrium is locally asymptotically stable when R0<1 and unstable when R0>1. In other words, the COVID-19 disease can be eliminated when R0< 1. Finally, numerical results are presented based on the real data of COVID-19 cases in Malaysia. [ABSTRACT FROM AUTHOR]