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The computational model proposed in this work uses the data's of COVID-19 cases in India. From the analysis, it can be observed that the proposed immunity model decides the recovery rate of COVID −19 patients; moreover, the recovery rate does not depend on the age of the patients. These analytic models can be used by public health professionals, hospital administrators and epidemiologists for strategic decision-making to enhance health requirements based on various demographic and social factors of those affected by the pandemic. Mobile-based computational model can be used to compute the travel history of the affected people by accessing the near geographical maps of the path traveled.

In this paper, the authors developed a pediatric and geriatric person’s immunity network-based mobile computational model for COVID-19 patients. As the computational model is hard to analyze mathematically, the authors simplified the computational model as general COVID-19 infected people, the computational immunity model. The model proposed in this work used the data's of COVID-19 cases in India.

This study proposes a pediatric and geriatric people immunity network model for COVID- 19 patients. For the analysis part, the data's on COVID-19 cases in India was used. In this model, the authors have taken two sets of people (pediatric and geriatric), both are facing common symptoms such as fever, cough and myalgia. From the analysis, it was observed and also proved that the immunity level of patients decides the recovery rate of COVID-19 patients and the age of COVID-19 patients has no significant influence on the recovery rate of the patient.

COVID-19 has created a global health crisis that has had a deep impact on the way we perceive our world and our everyday lives. Not only the rate of contagion and patterns of transmission threatens our sense of agency, but the safety measures put in place to contain the spread of the virus also require social distancing. The novel model in this work focus on the Indian scenario and thereby may help Indian health organizations for future planning and organization. The factors model in this work such as age, immunity level, recovery rate can be used by machine leaning models for predicting other useful outcomes.

This paper aims to address the role of Internet of Things (IoT) in preventing COVID-19. The IoT devices can be used in various ways to track the patients and suspected person. Remote data collection can be done with the help of IoT and sensors. Later, the data can be analyzed with the help of data science engineers and researchers to predict and prevent the COVID-19.

IoT is a creative mean of amalgamating clinical gadgets and their applications to associate with the human services and data innovation frameworks. An investigation on the conceivable outcomes of defying progressive COVID-19 pandemic by implementing the IoT approach while offering treatment to all classes of patient without any partiality in poor and rich. The information sharing, report checking, patient tracking, data social affair, investigation, cleanliness clinical consideration and so forth are the different cloud-based administrations of IoT. It can totally change the working format of the medical services while rewarding the huge volume of patients with a predominant degree of care and more fulfilment, particularly during this pandemic of COVID-19 lockdown. Health workers can quickly focus on patient zero and identify everyone who has come into contact with the infected person and move these people to quarantine/isolation. As COVID-19 has emerged from the Wuhan province of China, IoT tools such as geographic information system could be used as an effective tool to curb the spread of pandemics by acting as an early warning system. Scanners at airports across the world could be used to monitor temperature and other symptoms. This paper addresses the role of IoT in preventing COVID-19.

In the period of continuous pandemic of COVID-19, IoT offers many propelled cloud-based administrations and offices to serve a greater number of patients effectively. The remote medicinal services framework provides a lot of significance in such a crucial time of lockdown. The powerful interconnected arrangement of gadgets, applications, Web, database and so on encourages the consumers to benefit the administrations in smart way. IoT additionally advances its administrations by building up the quality culture of perceptive medicinal services or portable centre. It is a “distinct advantage innovation,” which may totally change the practices universally. Indeed, even its quality administrations in this extreme time make this methodology progressively productive and beneficial. IoT helps in observing and tracking more recognized people and patients in remote areas for their human service prerequisites. The customary medicinal services are probably going to observe a huge change in perspective sooner rather than later, as the computerized revolution would place cutting-edge innovation and its associated items in the possession of the patients and give both patients and doctors in remote areas better access to quality clinical services.

The contemporary exploration study focuses on the proposed IoT system for the treatment of patients in this progressing COVID-19. The working principle of IoT approach incorporates the mix of human services apparatuses, clinical treatment framework, Web organize, programming and administrations. IoT framework empowers the information assortment, report observing, understanding database, testing pictures and investigation and so forth. Data has been collected through online mode; in this study, the authors adopted empirical research design. Total 150 (118/150 = 78.66% respondent response ratio) online questionnaires were sent in the Chennai city of Tamilnadu, India. The participated nature of work is clinical examination in critical care division.

The purpose fo this paper is to Monitor and sense the sysmptoms of COVID-19 as a preliminary measure using electronic wearable devices. This variability is sensed by electrocardiograms observed from a multi-parameter monitor and electronic wearable. This field of interest has evolved into a wide area of investigation with today’s advancement in technology of internet of things for immediate sensing and processing information about profound pain. A window span is estimated and reports of profound pain data are used for monitoring heart rate variability (HRV). A median heart rate is considered for comparisons with a diverse range of variable information obtained from sensors and monitors. Observations from healthy patients are introduced to identify how root mean square of difference between inter beat intervals, standard deviation of inter-beat intervals and mean heart rate value are normalized in HRV analysis.

The function of a human heart relates back to the autonomic nervous system, which organizes and maintains a healthy maneuver of inter connected organs. HRV has to be determined for analyzing and reporting the status of health, fitness, readiness and possibilities for recovery, and thus, a metric for deeming the presence of COVID-19. Identifying the variations in heart rate, monitoring and assessing profound pain levels are potential lives saving measures in medical industries.

Experiments are proposed to be done in electrical and thermal point of view and this composition will deliver profound pain levels ranging from 0 to 10. Real time detection of pain levels will assist the care takers to facilitate people in an aging population for a painless lifestyle.

The presented research has documented the stages of COVID-19, symptoms and a mechanism to monitor the progress of the disease through better parameters. Risk factors of the disease are carefully analyzed, compared with test results, and thus, concluded that considering the HRV can study better in the presence of ignorance and negligence. The same mechanism can be implemented along with a global positioning system (GPS) system to track the movement of patients during isolation periods. Despite the stringent control measurements for locking down all industries, the rate of affected people is still on the rise. To counter this, people have to be educated about the deadly effects of COVID-19 and foolproof systems should be in place to control the transmission from affected people to new people. Medications to suppress temperatures, will not be sufficient to alter the heart rate variations, and thus, the proposed mechanism implemented the same. The proposed study can be extended to be associated with Government mobile apps for regular and a consortium of single tracking. Measures can be taken to distribute the low-cost proposal to people for real time tracking and regular updates about high and medium risk patients.

The situations of COVID-19 will certainly have an adverse effect over and above health care on factors of the internet of things (IoT) market. To overcome all the above issues, IoT devices and sensors can be used to track and monitor the movement of the people, so that necessary actions can be taken to prevent the spread of coronavirus disease (COVID-19). Mobile devices can be used for contact tracing of the affected person by analyzing the geomap of the travel history. This will prevent the spread and reset the economy to the normal condition.

To respond to the global COVID-19 outbreak, the social-economic implications of COVID-19 on specific dimensions of the global economy are analyzed in this study. The situations of COVID-19 will certainly have an adverse effect over and above health care on factors of the IoT market. To overcome these issues IoT devices and sensors can be used to track and monitor the movement of the people so that necessary actions can be taken to prevent the spread of COVID-19. Mobile devices can be used for contact tracing of the affected person by analyzing the geomap of the travel history. This will prevent the spread and reset the economy to the normal condition. A few reviews, approaches, and guidelines are provided in this article along these lines. Moreover, insights about the effects of the pandemic on various sectors such as agriculture, medical industry, finance, information technology, manufacturing and many others are provided. These insights may support strategic decision making and policy framing activities for the top level management in private and government sectors.

With insecurities of a new recession and economic crisis, key moments such as these call for strong and powerful governance in health, business, government, and large society. Instant support measures have to be initiated and adapted for those who can drop through the cracks. Mid- and long-term strategies are required to stabilize and motivate the economy during this recession.

A comprehensive social-economic development strategy that consists of sector by sector schemes and infrastructure that supports business to ensure the success of those with reliable and sustainable business models is necessary. From the literature analysis and real world observations it is concluded that the IoT, sensors, wearable devices and computational technologies plays major role in preserving the economy of the country by preventing the spread of COVID-19.

Many investigations are going on in monitoring, contact tracing, predicting and diagnosing the COVID-19 disease and many virologists are urgently seeking to create a vaccine as early as possible. Even though there is no specific treatment for the pandemic disease, the world is now struggling to control the spread by implementing the lockdown worldwide and giving awareness to the people to wear masks and use sanitizers. The new technologies, including the Internet of things (IoT), are gaining global attention towards the increasing technical support in health-care systems, particularly in predicting, detecting, preventing and monitoring of most of the infectious diseases. Similarly, it also helps in fighting against COVID-19 by monitoring, contract tracing and detecting the COVID-19 pandemic by connection with the IoT-based smart solutions. IoT is the interconnected Web of smart devices, sensors, actuators and data, which are collected in the raw form and transmitted through the internet. The purpose of this paper is to propose the concept to detect and monitor the asymptotic patients using IoT-based sensors.

In recent days, the surge of the COVID-19 contagion has infected all over the world and it has ruined our day-to-day life. The extraordinary eruption of this pandemic virus placed the World Health Organization (WHO) in a hazardous position. The impact of this contagious virus and scarcity among the people has forced the world to get into complete lockdown, as the number of laboratory-confirmed cases is increasing in millions all over the world as per the records of the government.

COVID-19 patients are either symptomatic or asymptotic. Symptomatic patients have symptoms such as fever, cough and difficulty in breathing. But patients are also asymptotic, which is very difficult to detect and monitor by isolating them.

Asymptotic patients are very hazardous because without knowing that they are infected, they might spread the infection to others, also asymptotic patients might be having very serious lung damage. So, earlier prediction and monitoring of asymptotic patients are mandatory to save their life and prevent them from spreading.

Sustainability is understood as a complex and integrating area, involving the most diverse areas and fields of knowledge. Because of the innumerable socio-environmental challenges in the current scenario, a sustainable development that finds the necessary changes and advances for communities, industry and the various stakeholders involved is required. In this process of promoting sustainable development, universities stand out for being institutions capable of taking an analytical and questioning look at the directions of the society in which they are inserted and not just helping them to pursue them, serving as a model and living laboratory for the implementation of greener practices in cities. One of the actions that contributes to the consolidation process of a more sustainable university and the development of the green campus is the use of green marketing, understood as a set of all the practices that involve conventional marketing, focused on the search to reduce the negative impact or promote positive effects on the relationship between the institution and the environment. This paper aims, based on the balanced scorecard (BSC), to propose a strategic management tool as support for green marketing strategies, thus promoting, more quickly, the promotion of sustainable development in higher education institutions (HEIs).

Four universities were chosen, from the literature, in terms of best practices for sustainable development, where the main dimensions used by green marketing were mapped. Based on them, the BSC structure was adapted to enhance its strategies.

To achieve the objective of this work, this paper proposed an adaptation of the original BSC for better management of green marketing strategies for universities, based on four dimensions: community members, university members, product and strategy.

The main contribution of this paper is to propose a BSC as a strategic management system focused on the green marketing of universities to accelerate the promotion of sustainable development in HEIs.

This study aims to investigate the impact of the COVID-19 pandemic on businesses, their operations, and the financial conditions in Sri Lanka. A sample of 19 executive-level employees from 19 companies registered at the Colombo Stock Exchange in Sri Lanka was interviewed. The thematic analysis method was used to analyse the data. It demonstrates the insecurity of the current business situation, as with the pandemic, most large-scale operating companies have been permanently or temporarily closed. The financial condition was categorised into main sub-themes such as business profitability, liquidity problems, the balance of payments, working capital, and cash flows and was highly impacted during the COVID-19 outbreak. The findings of the study help to improve the favourable image of Sri Lankan companies by facilitating solutions to overcome the challenges and difficulties and are beneficial for the relevant government parties to amend policies and for investors to make prudent investment decisions. As a maiden study, this one focused on investigating the pandemic’s impact on business operations and developed a nine-step plan for organisations, employees, and the government to minimise the impact of COVID-19 on their businesses.

To identify sources of the success and failure of COVID-19 control measures and develop best-practice public health policy in mitigating the spread of COVID-19, this paper aims to evaluate the efficiency of various combinations of government COVID-19 control measures among OECD countries. This paper also identifies which factors critically influence the efficiency of COVID-19 control measures.

This paper employed two-stage network SBM (slacks-based measure of efficiency) models with variable returns-to-scale and constant returns-to-scale, respectively, among various forms of data envelopment analysis (DEA) models. As a post hoc analysis, the authors used Tobit regression for examining the causal relationship between a nation's cultural dimensions and its COVID-19 control measure's efficiency scores.

The authors found that the pervasive less individualistic and higher uncertainty avoiding culture positively influenced the efficient control of COVID-19 outbreaks since such a culture helped the government impose its mandatory COVID-19 control measures without people's strong resistance to those measures.

Many public health policymakers are wondering why COVID-19 control measures are not effective in coping with the COVID-19 outbreaks. This paper helps the government find the most efficient combination of COVID-19 controls measures for curbing the spread of the stubborn coronavirus. This paper is one of the first attempts to identify pandemic risk mitigation factors from a cultural perspective.

This paper has used the well-known machine learning (ML) computational algorithm with Internet of Things (IoT) devices to predict the COVID-19 disease and to analyze the peak rate of the disease in the world. ML is the best tool to analyze and predict the object in reasonable time with great level of accuracy. The Purpose of this paper is to develop a model to predict the coronavirus by considering majorly related symptoms, attributes and also to predict and analyze the peak rate of the disease.

COVID-19 or coronavirus disease threatens the human lives in various ways, which leads to deaths in most of the cases. It affects the respiratory organs slowly and this penetration leads to multiple organ failure, which causes death in some cases having poor immunity system. In recent times, it has drawn the international attention because of the pandemic threat that is harder to control the spreading of infection around the world.

This proposed model is implemented by support vector machine classifier and Bayesian network algorithm, which yields high accuracy. The K-means algorithm has been applied for clustering the data set models. For data collection, IoT devices and related sensors were used in the identified hotspots. The data sets were collected from the selected hotspots, which are placed on the regions selected by the government agencies. The proposed COVID-19 prediction models improve the accuracy of the prediction and peak accuracy ratio. This model is also tested with best, worst and average cases of data set to achieve the better prediction rate.

From that hotspots, the IoT devices were fixed and accessed through wireless sensors (802.11) to transfer the data to the authors’ database, which is dedicated in data collection server. The data set and the proposed model yield good results and perform well with expected accuracy rate in the analysis and monitoring of the recovery rate of COVID-19.

Blockchain technology (BCT), since its emergence touted to be disruptive, is gaining momentum, especially in the agri-food system owing to its multiple benefits.

The authors attempted to conduct a systematic bibliometric visualization analysis of the BCT in the agri-food system. The analysis investigated the list of countries and institutions that conducted research on BCT in agriculture, growth trend analysis in research publications, bibliographic coupling of journals using the VOSviewer tool, and the countries and institutions researching BCT.

The authors discovered that China, the USA and India were the highly active countries in BCT research and publication. However, India has only limited research collaboration with other countries as compared to China and the USA. The keyword analysis indicates the role of BCT in order to maintain the transparency of the supply chain by means of protecting the privacy of the personal data of the stakeholders.

More research related to the implementation of BCT in livestock, fishery and agro-forestry sector is recommended.

The case examined is of particular interest as it is concerned with efficient supply chain management.

This study adds value and evidence to the scope and benefits of BCT by providing a comprehensive literature review, with a special focus on the opportunities and challenges concerned with implementation of BCT in the Indian agri-food system.

1. Blockchain technology (BCT) – a promising tool to resolve issues in agriculture supply chain.

2. BCT ensures transparency and protection of information along the supply chain transactions.

3. China, the USA and India are the highly active countries in BCT research and publication.

4. Multiple potential benefits to stakeholders are attributed to the BCT in the agri-food system.

5. The key challenge is to bridge the digital gap between developed and developing nations.

6. Future research on BCT should aim at easing and undistorted competition among stakeholders.

Blockchain technology (BCT) – a promising tool to resolve issues in agriculture supply chain.

BCT ensures transparency and protection of information along the supply chain transactions.

China, the USA and India are the highly active countries in BCT research and publication.

Multiple potential benefits to stakeholders are attributed to the BCT in the agri-food system.

The key challenge is to bridge the digital gap between developed and developing nations.

Future research on BCT should aim at easing and undistorted competition among stakeholders.