Defending against the Novel Coronavirus (COVID-19) outbreak: How can the Internet of Things (IoT) help to save the world?

The role of digital surveillance during outbreaks: the Ghana experience from COVID-19 response

Over the years, Ghana has made notable strides in adopting digital approaches to address societal challenges and meet demands. While the health sector, particularly the disease surveillance structure, has embraced digitization to enhance case detection, reporting, analysis, and information dissemination, critical aspects remain to be addressed. Although the Integrated Disease Surveillance and Response (IDSR) structure has experienced remarkable growth in digitization, certain areas require further attention as was observed during the COVID-19 pandemic. Ghana during the COVID-19 pandemic, recognized the importance of leveraging digital technologies to bolster the public health response. To this end, Ghana implemented various digital surveillance tools to combat the pandemic. These included the 'Surveillance Outbreak Response Management and Analysis System (SORMAS)', the digitalized health declaration form, ArcGIS Survey123, Talkwalker, 'Lightwave Health information Management System' (LHIMS), and the 'District Health Information Management System (DHIMS)'. These digital systems significantly contributed to the country's success in responding to the COVID-19 pandemic. One key area where digital systems have proved invaluable is in the timely production of daily COVID-19 situational updates. This task would have been arduous and delayed if reliant solely on paper-based forms, which hinder efficient reporting to other levels within the health system. By adopting these digital systems, Ghana has been able to overcome such challenges and provide up-to-date information for making informed public health decisions. This paper attempts to provide an extensive description of the digital systems currently employed to enhance Ghana's paper-based disease surveillance system in the context of its response to COVID-19. The article explores the strengths and challenges or limitations associated with these digital systems for responding to outbreaks, offering valuable lessons that can be learned from their implementation.

Designing a new sustainable Test Kit supply chain network utilizing Internet of Things

The advent of COVID-19 put much economic pressure on countries worldwide, especially low-income countries. Providing test kits for Covid-19 posed a huge challenge at the beginning of the pandemic. Especially the low-income and less developed countries that did not have the technology to produce this kit and had to import it into the country, which itself cost a lot to buy and distribute these kits. This paper proposes a sustainable COVID-19 test kits supply chain network (STKSCN) for the first time to fill this gap. Distribution and transportation of test kits, location of distribution centers, and management of used test kits are considered in this network. A mixed integer linear programming Multi-Objective (MO), multi-period, multi-resource mathematical model is extended for the proposed supply chain. Another contribution is designing a platform based on the Internet of Things (IoT) to increase the speed, accuracy and security of the network. In this way, patients set their appointment online by registering their personal details and clinical symptoms. An augmented ɛ-constraint2 (AUGMECON2) is proposed for solving small and medium size of problem. Also, two meta-heuristic algorithms, namely NSGA-II and PESA-II are presented to solve the small, medium and large size of the problem. Taguchi method is utilized to control the parameters, and for comparison between meta-heuristic, five performance metrics are suggested. In addition, a case study in Iran is presented to validate the proposed model. Finally, the results show that PESA-II is more efficient and has better performance than the others based on assessment metrics and computational time.

A novel authentication protocol to ensure confidentiality among the Internet of Medical Things in covid-19 and future pandemic scenario

Diagnosing the patients remotely, controlling the medical equipment, and monitoring the quarantined patients are some of the necessary and frequent activities in COVID-19. Internet of Medical Things (IoMT) makes this works easy and feasible. Sharing information from patients and sensors associated with the patients to doctors is always an integral part of IoMT. Unauthorized access to such information may invite adversaries to disturb patients financially and mentally; furthermore, leaks in its confidentiality will lead to dangerous health concerns for patients. While ensuring authentication and confidentiality, We must focus on the constraints of IoMT, such as low energy consumption, deficient memory, and the dynamic nature of devices. Numerous protocols have been proposed for authentication in healthcare systems such as IoMT and telemedicine. However, many of these protocols were neither computationally efficient nor provided confidentiality, anonymity, and resistance against several attacks. In the proposed protocol, we have considered the most common scenario of IoMT and tried to overcome the limitations of existing works. Describing the system module and security analysis proves it is a panacea for COVID-19 and future pandemics.

Security issues and challenges in cloud of things-based applications for industrial automation

Due to the COVID-19 outbreak, industries have gained a thrust on contactless processing for computing technologies and industrial automation. Cloud of Things (CoT) is one of the emerging computing technologies for such applications. CoT combines the most emerging cloud computing and the Internet of Things. The development in industrial automation made them highly interdependent because the cloud computing works like a backbone in IoT technology. This supports the data storage, analytics, processing, commercial application development, deployment, and security compliances. Now amalgamation of cloud technologies with IoT is making utilities more useful, smart, service-oriented, and secure application for sustainable development of industrial processes. As the pandemic has increased access to computing utilities remotely, cyber-attacks have been increased exponentially. This paper reviews the CoT's contribution to industrial automation and the various security features provided by different tools and applications used for the circular economy. The in-depth analysis of security threats, availability of different features corresponding the security issues in traditional and non-traditional CoT platforms used in industrial automation have been analysed. The security issues and challenges faced by IIoT and AIoT in industrial automation have also been addressed.

Influenza surveillance with Baidu index and attention-based long short-term memory model

BACKGROUND

The prediction and prevention of influenza is a public health issue of great concern, and the study of timely acquisition of influenza transmission trend has become an important research topic. For achieving more quicker and accurate detection and prediction, the data recorded on the Internet, especially on the search engine from Google or Baidu are widely introduced into this field. Moreover, with the development of intelligent technology and machine learning algorithm, many updated and advanced trend tracking and forecasting methods are also being used in this research problem.

METHODS

In this paper, a new recurrent neural network architecture, attention-based long short-term memory model is proposed for influenza surveillance. This is a kind of deep learning model which is trained by processing from Baidu Index series so as to fit the real influenza survey time series. Previous studies on influenza surveillance by Baidu Index mostly used traditional autoregressive moving average model or classical machine learning models such as logarithmic linear regression, support vector regression or multi-layer perception model to fit influenza like illness data, which less considered the deep learning structure. Meanwhile, some new model that considered the deep learning structure did not take into account the application of Baidu index data. This study considers introducing the recurrent neural network with long short-term memory combined with attention mechanism into the influenza surveillance research model, which not only fits the research problems well in model structure, but also provides research methods based on Baidu index.

RESULTS

The actual survey data and Baidu Index data are used to train and test the proposed attention-based long short-term memory model and the other comparison models, so as to iterate the value of the model parameters, and to describe and predict the influenza epidemic situation. The experimental results show that our proposed model has better performance in the mean absolute error, mean absolute percentage error, index of agreement and other indicators than the other comparison models.

CONCLUSION

Our proposed attention-based long short-term memory model vividly verifies the ability of this attention-based long short-term memory structure for better surveillance and prediction the trend of influenza. In comparison with some of the latest models and methods in this research field, the model we proposed is also excellent in effect, even more lightweight and robust. Future research direction can consider fusing multimodal data based on this model and developing more application scenarios.

A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States

Affective, emotional, and physiological states (AFFECT) detection and recognition by capturing human signals is a fast-growing area, which has been applied across numerous domains. The research aim is to review publications on how techniques that use brain and biometric sensors can be used for AFFECT recognition, consolidate the findings, provide a rationale for the current methods, compare the effectiveness of existing methods, and quantify how likely they are to address the issues/challenges in the field. In efforts to achieve the key goals of Society 5.0, Industry 5.0, and human-centered design better, the recognition of emotional, affective, and physiological states is progressively becoming an important matter and offers tremendous growth of knowledge and progress in these and other related fields. In this research, a review of AFFECT recognition brain and biometric sensors, methods, and applications was performed, based on Plutchik's wheel of emotions. Due to the immense variety of existing sensors and sensing systems, this study aimed to provide an analysis of the available sensors that can be used to define human AFFECT, and to classify them based on the type of sensing area and their efficiency in real implementations. Based on statistical and multiple criteria analysis across 169 nations, our outcomes introduce a connection between a nation's success, its number of Web of Science articles published, and its frequency of citation on AFFECT recognition. The principal conclusions present how this research contributes to the big picture in the field under analysis and explore forthcoming study trends.

Internet of Things (IoT) Adoption Model for Early Identification and Monitoring of COVID-19 Cases: A Systematic Review

Background

The 2019 coronavirus disease (COVID-19) is a mysterious and highly infectious disease that was declared a pandemic by the World Health Organization. The virus poses a great threat to global health and the economy. Currently, in the absence of effective treatment or vaccine, leveraging advanced digital technologies is of great importance. In this respect, the Internet of Things (IoT) is useful for smart monitoring and tracing of COVID-19. Therefore, in this study, we have reviewed the literature available on the IoT-enabled solutions to tackle the current COVID-19 outbreak.

Methods

This systematic literature review was conducted using an electronic search of articles in the PubMed, Google Scholar, ProQuest, Scopus, Science Direct, and Web of Science databases to formulate a complete view of the IoT-enabled solutions to monitoring and tracing of COVID-19 according to the FITT (Fit between Individual, Task, and Technology) model.

Results

In the literature review, 28 articles were identified as eligible for analysis. This review provides an overview of technological adoption of IoT in COVID-19 to identify significant users, either primary or secondary, required technologies including technical platform, exchange, processing, storage and added-value technologies, and system tasks or applications at "on-body," "in-clinic/hospital," and even "in-community" levels.

Conclusions

The use of IoT along with advanced intelligence and computing technologies for ubiquitous monitoring and tracking of patients in quarantine has made it a critical aspect in fighting the spread of the current COVID-19 and even future pandemics.

Role of artificial intelligence-internet of things (AI-IoT) based emerging technologies in the public health response to infectious diseases in Bangladesh

Digital technologies are the need of today to predict, prevent and control emerging infectious diseases. Bangladesh is one of the world's poorest and most densely populated countries and faces a double burden of two deadly diseases, COVID-19 and dengue. In response to both these diseases, the absence of a digital healthcare system and insufficient preparedness, lack of public awareness pose unique challenges and a large threat to the population, resulting in epidemics of escalating severity. This paper suggests a digital health care and surveillance system based on the internet of things (IoT) and artificial intelligence (AI) for timely identification of COVID-19 and dengue cases and improving the prevention and control strategies in the country.

•

Digital technologies enable smart healthcare solutions to sustain and improve health services.

•

Bangladesh is vulnerable to both COVID-19 and dengue epidemics.

•

Epidemic preparedness requires improved digital health policy by integrating AI-IoT based emerging technologies.

•

Campaigning to raise awareness of COVID-19 and dengue infections is a public health urgency.

The role of contemporary digital tools and technologies in COVID-19 crisis: An exploratory analysis

Following the COVID-19 pandemic, there has been an increase in interest in using digital resources to contain pandemics. To avoid, detect, monitor, regulate, track, and manage diseases, predict outbreaks and conduct data analysis and decision-making processes, a variety of digital technologies are used, ranging from artificial intelligence (AI)-powered machine learning (ML) or deep learning (DL) focused applications to blockchain technology and big data analytics enabled by cloud computing and the internet of things (IoT). In this paper, we look at how emerging technologies such as the IoT and sensors, AI, ML, DL, blockchain, augmented reality, virtual reality, cloud computing, big data, robots and drones, intelligent mobile apps, and 5G are advancing health care and paving the way to combat the COVID-19 pandemic. The aim of this research is to look at possible technologies, processes, and tools for addressing COVID-19 issues such as pre-screening, early detection, monitoring infected/quarantined individuals, forecasting future infection rates, and more. We also look at the research possibilities that have arisen as a result of the use of emerging technology to handle the COVID-19 crisis.

Detection and Application of Wearable Devices Based on Internet of Things in Human Physical Health

In order to improve the detection function of wearable intelligent devices in the Internet of things and facilitate people to control a variety of information such as heart rate, exercise state, blood oxygen saturation, and so on, the scientific detection of human physical health based on wearable devices based on Internet of things technology is proposed. Through the combination of software- and hardware-related functional modules, the real-time detection of human physical health information can be effectively realized. Firstly, the detection principle of optical capacitance product pulse wave signal and the waveform characteristics of pulse wave are introduced, and then the application scenarios and advantages of wearable devices are further introduced; then, the convolutional neural network for pulse wave signal denoising and the basic principle of self-encoder are introduced; finally, the regression prediction method and support vector machine method for pulse wave signal feature extraction are introduced in detail. The pulse wave based on optical capacitance product is removed to improve the waveform quality of pulse wave signal. Firstly, the system software development environment is briefly described. Then, the software design of watch terminal master device based on MSP432 and belt terminal slave device based on MSP430 are described in detail, and the detailed program implementation flow of each key technology in the system is given. In addition, the fall detection algorithm based on threshold discrimination is studied, and the program implementation of the algorithm is also described in detail. Finally, the system is tested. The results show that normal state mainly include normal walking, jogging, and fast sitting, and the accuracy rate is 97%, 95%, and 93%, respectively. For fall state, the experimenter needs to simulate various possible fall states, and the accuracy rate is 95%, 93%, and 95%, respectively, which verifies the detection accuracy of the algorithm. The system can automatically turn on the satellite positioning function when the user’s physical sign parameters are abnormal or the user’s current fall dangerous situation occurs, and send the current position information and alarm content information through the GSM module, so that the dangerous situation can be found and handled at the first time.

Development of Advanced Artificial Intelligence and IoT Automation in the Crisis of COVID-19 Detection

Internet of Things (IoT) is a successful area for many industries and academia domains, particularly healthcare is one of the application areas that uses IoT sensors and devices for monitoring. IoT transition replaces contemporary health services with scientific and socioeconomic viewpoints. Since the epidemic began, diverse scientific organizations have been making accelerated efforts to use a wide range of tools to tackle this global challenge and the founders of IoT analytics. Artificial intelligence (AI) plays a key role in measuring, assessing, and diagnosing the risk. It could be used to predict the number of alternate incidents, recovered instances, and casualties, also used for forecasting cases. Within the COVID-19 background, IoT technologies are used to minimize COVID-19 exposure to others by prenatal screening, patient monitoring, and postpatient incident response in specified procedures. In this study, the importance of IoT technology and artificial intelligence in COVID-19 is explored, and the 3 important steps discussed such as the evaluation of networks, implementations, and IoT industries to battle COVID-19, including early detection, quarantine times, and postrecovery activities, are reviewed. In this study, how IoT handles the COVID-19 pandemic at a new level of healthcare is investigated. In this research, the long short-term memory (LSTM) with recurrent neural network (RNN) is used for diagnosis purpose and in particular, its important architecture for the analysis of cough and breathing acoustic characteristics. In comparison with both coughing and respiratory samples, our findings indicate poor accuracy of the voice test.

A scalable framework for smart COVID surveillance in the workplace using Deep Neural Networks and cloud computing

A smart and scalable system is required to schedule various machine learning applications to control pandemics like COVID-19 using computing infrastructure provided by cloud and fog computing. This paper proposes a framework that considers the use case of smart office surveillance to monitor workplaces for detecting possible violations of COVID effectively. The proposed framework uses deep neural networks, fog computing and cloud computing to develop a scalable and time-sensitive infrastructure that can detect two major violations: wearing a mask and maintaining a minimum distance of 6 feet between employees in the office environment. The proposed framework is developed with the vision to integrate multiple machine learning applications and handle the computing infrastructures for pandemic applications. The proposed framework can be used by application developers for the rapid development of new applications based on the requirements and do not worry about scheduling. The proposed framework is tested for two independent applications and performed better than the traditional cloud environment in terms of latency and response time. The work done in this paper tries to bridge the gap between machine learning applications and their computing infrastructure for COVID-19.

Home quarantine patient monitoring in the era of COVID-19 disease

Patients’ remote monitoring becomes even more crucial due to the spreading of the COVID-19 disease. Hospitals cannot accommodate all the patients who need to be taken care. Hence, tele-medicine or, as also named, tele-health, remains the only means available to keep the situation under control. In particular, it is important to monitor the patients who are subject to the home quarantine period. The reason is twofold: (i) their live status and symptoms must be controlled; (ii) they must not leave the permitted area during the quarantine period. To this end, the paper defines a set of rules and processes based on the Internet of Things (IoT) paradigm, which enable the integration of different devices, in order to monitor the required parameters related to the patient and notify life-threatening situations to the connected health-care structure. The conceived IoT network is developed by means of Node-RED, which is a flow-based programming tool targeted to the IoT. Particular attention is also paid to security and privacy requirements, since sensitive data related to the patients must be kept safe. The proposed solution is preliminary assessed by means of a test-bed.

HRNET: AI-on-Edge for Mask Detection and Social Distancing Calculation

The purpose of the paper is to provide innovative emerging technology framework for community to combat epidemic situations. The paper proposes a unique outbreak response system framework based on artificial intelligence and edge computing for citizen centric services to help track and trace people eluding safety policies like mask detection and social distancing measure in public or workplace setup. The framework further provides implementation guideline in industrial setup as well for governance and contact tracing tasks. The adoption will thus lead in smart city planning and development focusing on citizen health systems contributing to improved quality of life. The conceptual framework presented is validated through quantitative data analysis via secondary data collection from researcher's public websites, GitHub repositories and renowned journals and further benchmarking were conducted for experimental results in Microsoft Azure cloud environment. The study includes selective AI models for benchmark analysis and were assessed on performance and accuracy in edge computing environment for large-scale societal setup. Overall YOLO model outperforms in object detection task and is faster enough for mask detection and HRNetV2 outperform semantic segmentation problem applied to solve social distancing task in AI-Edge inferencing environmental setup. The paper proposes new Edge-AI algorithm for building technology-oriented solutions for detecting mask in human movement and social distance. The paper enriches the technological advancement in artificial intelligence and edge computing applied to problems in society and healthcare systems. The framework further equips government agency, system providers to design and construct technology-oriented models in community setup to increase the quality of life using emerging technologies into smart urban environments.

The Role of Emerging Technologies to Fight Against COVID-19 Pandemic: An Exploratory Review

Since the end of the year 2019, the whole world is experiencing a global emergency due to the COVID-19 pandemic. The major sectors including industry, economics, education have been affected. Ongoing pandemics confined us to avoid mass gathering and rigorously maintain social distancing to mitigate the spreading of this infectious disease. In this situation emerging technologies including the internet of things (IoT), Artificial Intelligence (AI) is playing a very important role in various fields such as healthcare, economics, educational system, and others to monitoring or tackle the impact of COVID-19 pandemic. Several papers discussed the impact of IoT on the COVID-19 pandemic in various aspects. However, the challenges and designing issues towards the implementation of IoT-based monitoring systems are not deeply investigated. Alongside, the adaptation of IoT and other technologies in the post-covid situation is not addressed properly. Our review article provides an up to date extensive survey on how IoT-enabled technologies are helping to combat the pandemic and to manage industry, education, economic, and medical system. As result, the realization is that IoT and other associated technologies have a great impact on virus detection, tracking, and mitigate the spread. In the face of an expeditiously spreading pandemic, the associated designing issues of the IoT-based framework have been looked into as a part of this review. Alongside, this review highlights the major challenges like privacy, security scalability, etc. facing in using such technologies. Finally, we explore ’The New Normal’ and the use of technologies to help in the post-pandemic era.

COVID-19: Challenges and its Technological Solutions using IoT

COVID-19 is a global pandemic that has affected many countries in a short span of time. People worldwide are susceptible to this deadly disease. To control the prevailing havoc of coronavirus, researchers are adopting techniques like plasma therapy, proning, medicines, etc. To stop the rapid spread of COVID-19, contact tracing is one of the important ways to check the infected people. This paper explains the various challenges people and health practitioners are facing due to COVID-19. In this paper, various ways with which the impact of COVID-19 can be controlled using IoT technology have been discussed. A six-layer architecture of IoT solutions for containing the deadly COVID-19 has been proposed. In addition to this, the role of machine learning techniques for diagnosing COVID-19 has been discussed in this paper, and a quick explanation of the unmanned aerial vehicle (UAVs) applications for contact tracing has also been specified. From the study conducted, it is evident that IoT solutions can be used in various ways for restricting the impact of COVID-19. Furthermore, IoT can be used in the healthcare sector to assure people's safety and good health with minimal costs.

SDN-IoT empowered intelligent framework for industry 4.0 applications during COVID-19 pandemic

The industrial ecosystem has been unprecedentedly affected by the COVID-19 pandemic because of its immense contact restrictions. Therefore, the manufacturing and socio-economic operations that require human involvement have significantly intervened since the beginning of the outbreak. As experienced, the social-distancing lesson in the potential new-normal world seems to force stakeholders to encourage the deployment of contactless Industry 4.0 architecture. Thus, human-less or less-human operations to keep these IoT-enabled ecosystems running without interruptions have motivated us to design and demonstrate an intelligent automated framework. In this research, we have proposed "EdgeSDN-I4COVID" architecture for intelligent and efficient management during COVID-19 of the smart industry considering the IoT networks. Moreover, the article presents the SDN-enabled layer, such as data, control, and application, to effectively and automatically monitor the IoT data from a remote location. In addition, the proposed convergence between SDN and NFV provides an efficient control mechanism for managing the IoT sensor data. Besides, it offers robust data integration on the surface and the devices required for Industry 4.0 during the COVID-19 pandemic. Finally, the article justified the above contributions through particular performance evaluations upon appropriate simulation setup and environment.

NextGen Public Health Surveillance and the Internet of Things (IoT)

Recent advances in technology have led to the rise of new-age data sources (e.g., Internet of Things (IoT), wearables, social media, and mobile health). IoT is becoming ubiquitous, and data generation is accelerating globally. Other health research domains have used IoT as a data source, but its potential has not been thoroughly explored and utilized systematically in public health surveillance. This article summarizes the existing literature on the use of IoT as a data source for surveillance. It presents the shortcomings of current data sources and how NextGen data sources, including the large-scale applications of IoT, can meet the needs of surveillance. The opportunities and challenges of using these modern data sources in public health surveillance are also explored. These IoT data ecosystems are being generated with minimal effort by the device users and benefit from high granularity, objectivity, and validity. Advances in computing are now bringing IoT-based surveillance into the realm of possibility. The potential advantages of IoT data include high-frequency, high volume, zero effort data collection methods, with a potential to have syndromic surveillance. In contrast, the critical challenges to mainstream this data source within surveillance systems are the huge volume and variety of data, fusing data from multiple devices to produce a unified result, and the lack of multidisciplinary professionals to understand the domain and analyze the domain data accordingly.

FSS superstrate antenna for satellite cynosure on IoT to combat COVID-19 pandemic

The global pandemic, COVID-19 needs joint techniques and technology to combat it. The internet of things (IoT) has been at the forefront in solving problems, not only in the health care sector but in other sectors. It delivers accuracy with robustness in the developing service and application. However, it remains clear that the use of IoT is limited to coverage, longevity, security, connectivity issue, immediacy, and multicasting, we proposed in this paper frequency selective surface (FSS) as superstrate for rectangular microstrip antenna. An FSS design combine with the rectangular microstrip antenna for better performance is placed over FSS parallel configuration. The rectangular microstrip antenna was titled 45 degrees to change the band-stop. Analysis of the proposed performance in terms of gain, return loss, and directivity shows that the FSS structure's integration brings better results. With the help of a 3D electromagnetic computer simulation technology CST studio suite, we model the proposed antenna, perform the simulation with a frequency-domain solver, and validate it with a time-domain solver. The proposed impressive result is suitable for satellite networks, which hybrid with IoT can provide a sustainable long-time solution in fighting the COVID-19 pandemic.

The Role of Internet of Things to Control the Outbreak of COVID-19 Pandemic

Currently, COVID-19 pandemic is the major cause of disease burden globally. So, there is a need for an urgent solution to fight against this pandemic. Internet of Things (IoT) has the ability of data transmission without human interaction. This technology enables devices to connect in the hospitals and other planned locations to combat this situation. This article provides a road map by highlighting the IoT applications that can help to control it. This study also proposes a real-time identification and monitoring of COVID-19 patients. The proposed framework consists of four components using the cloud architecture: 1) data collection of disease symptoms (using IoT-based devices); 2) health center or quarantine center (data collected using IoT devices); 3) data warehouse (analysis using machine learning models); and 4) health professionals (provide treatment). To predict the severity level of COVID-19 patients on the basis of IoT-based real-time data, we experimented with five machine learning models. The results reveal that random forest outperformed among all other models. IoT applications will help management, health professionals, and patients to investigate the symptoms of contagious disease and manage COVID-19 +ve patients worldwide.

Review of Recent Technologies for Tackling COVID-19

The current pandemic caused by the COVID-19 virus requires more effort, experience, and science-sharing to overcome the damage caused by the pathogen. The fast and wide human-to-human transmission of the COVID-19 virus demands a significant role of the newest technologies in the form of local and global computing and information sharing, data privacy, and accurate tests. The advancements of deep neural networks, cloud computing solutions, blockchain technology, and beyond 5G (B5G) communication have contributed to the better management of the COVID-19 impacts on society. This paper reviews recent attempts to tackle the COVID-19 situation using these technological advancements.

Impact of IoT devices in E-Health: A Review on IoT in the context of COVID-19 and its variants

Actually, COVID-19 and its variants present a big challenge for the public health security. COVID-19 is a new form of the coronaviruses characterized by a set of symptoms like laboratory and radiological symptoms, when the first case has confirmed in December 2019 in Wuhan City, as well as a new variant of this form has appeared in December 2020 in the United Kingdom. Internet of things (IoT) is a technological revolution employed in different areas in the aim to serve the asked purposes. The implementation of IoT solutions in healthcare area has several benefits such as reducing the cost of services and improving treatment results. In this paper, we present a review on the impact of IoT on this new health challenge (COVID-19 and its variants), we will focus this study on the impact of the use of IoT devices to reduce transmissions of COVID-19 and its variants.

Wearable IoTs and Geo-Fencing Based Framework for COVID-19 Remote Patient Health Monitoring and Quarantine Management to Control the Pandemic

The epidemic disease of Severe Acute Respiratory Syndrome (SARS) called COVID-19 has become a more frequently active disease. Managing and monitoring COVID-19 patients is still a challenging issue for advanced technologies. The first and foremost critical issue in COVID-19 is to diagnose it timely and cut off the chain of transmission by isolating the susceptible and patients. COVID-19 spreads through close interaction and contact with an infected person. It has affected the entire world, and every country is facing the challenges of having adequate medical facilities along with the availability of medical staff in rural and urban areas that have a high number of patients due to the pandemic. Due to the invasive method of treatment, SARS-COVID is spreading swiftly. In this paper, we propose an intelligent health monitoring framework using wearable Internet of Things (IoT) and Geo-fencing for COVID-19 susceptible and patient monitoring, and isolation and quarantine management to control the pandemic. The proposed system consists of four layers, and each layer has different functionality: a wearable sensors layer, IoT gateway layer, cloud server layer, and client application layer for visualization and analysis. The wearable sensors layer consists of wearable biomedical and GPS sensors for physiological parameters, and GPS and Wi-Fi Received Signal Strength Indicator acquisition for health monitoring and user Geo-fencing. The IoT gateway layer provides a Bluetooth and Wi-Fi based wireless body area network and IoT environment for data transmission anytime and anywhere. Cloud servers use Raspberry Pi and ThingSpeak cloud for data analysis and web-based application layers for remote monitoring based on user consent. The susceptible and patient conditions, real-time sensor’s data, and Geo-fencing enables minimizing the spread through close interaction. The results show the effectiveness of the proposed framework.

Internet of Things (IoT) Technology Research in Business and Management Literature: Results from a Co-Citation Analysis

In coherence with the progressive digitalization of all areas of life, the Internet of Things (IoT) is a flourishing concept in both research and practice. Due to the increasing scholarly attention, the literature landscape has become scattered and fragmented. With a focus on the commercial application of the IoT and corresponding research, we employ a co-citation analysis and literature review to structure the field. We find and describe 19 research themes. To consolidate the extant research, we propose a research framework, which is based on a theoretical implementation process of IoT as a concept, specific IoT applications, or architectures integrated in an adapted input–process–output model. The main variables of the model are an initial definition and conceptualization of an IoT concept (input), which goes through an evaluation process (process), before it is implemented and can have an impact in practice (output). The paper contributes to interdisciplinary research relating to a business and management perspective on IoT by providing a holistic overview of predominant research themes and an integrative research framework.

Short-term forecasting of the COVID-19 outbreak in India

As the outbreak of coronavirus disease 2019 (COVID-19) is rapidly spreading in different parts of India, a reliable forecast for the cumulative confirmed cases and the number of deaths can be helpful for policymakers in making the decisions for utilizing available resources in the country. Recently, various mathematical models have been used to predict the outbreak of COVID-19 worldwide and also in India. In this article we use exponential, logistic, Gompertz growth and autoregressive integrated moving average (ARIMA) models to predict the spread of COVID-19 in India after the announcement of various unlock phases. The mean absolute percentage error and root mean square error comparative measures were used to check the goodness-of-fit of the growth models and Akaike information criterion for ARIMA model selection. Using COVID-19 pandemic data up to 20 December 2020 from India and its five most affected states (Maharashtra, Karnataka, Andhra Pradesh, Tamil Nadu and Kerala), we report 15-days-ahead forecasts for cumulative confirmed cases and the number of deaths. Based on available data, we found that the ARIMA model is the best-fitting model for COVID-19 cases in India and its most affected states.

Impact of COVID-19 on IoT Adoption in Healthcare, Smart Homes, Smart Buildings, Smart Cities, Transportation and Industrial IoT

COVID-19 has disrupted normal life and has enforced a substantial change in the policies, priorities and activities of individuals, organisations and governments. These changes are proving to be a catalyst for technology and innovation. In this paper, we discuss the pandemic's potential impact on the adoption of the Internet of Things (IoT) in various broad sectors, namely healthcare, smart homes, smart buildings, smart cities, transportation and industrial IoT. Our perspective and forecast of this impact on IoT adoption is based on a thorough research literature review, a careful examination of reports from leading consulting firms and interactions with several industry experts. For each of these sectors, we also provide the details of notable IoT initiatives taken in the wake of COVID-19. We also highlight the challenges that need to be addressed and important research directions that will facilitate accelerated IoT adoption.

A hover view over effectual approaches on pandemic management for sustainable cities - The endowment of prospective technologies with revitalization strategies

The COVID-19 pandemic affects all of society and hinders day-to-day activities from a straightforward perspective. The pandemic has an influential impact on almost everything and the characteristics of the pandemic remain unclear. This ultimately leads to ineffective strategic planning to manage the pandemic. This study aims to elucidate the typical pandemic characteristics in line with various temporal phases and its associated measures that proved effective in controlling the pandemic. Besides, an insight into diverse country's approaches towards pandemic and their consequences is provided in brief. Understanding the role of technologies in supporting humanity gives new perspectives to effectively manage the pandemic. Such role of technologies is expressed from the viewpoint of seamless connectivity, rapid communication, mobility, technological influence in healthcare, digitalization influence, surveillance and security, Artificial Intelligence (AI), and Internet of Things (IoT). Furthermore, some insightful scenarios are framed where the full-fledged implementation of technologies is assumed, and the reflected pandemic impacts in such scenarios are analyzed. The framed scenarios revolve around the digitalized energy sector, an enhanced supply chain system with effective customer-retailer relationships to support the city during the pandemic scenario, and an advanced tracking system for containing virus spread. The study is further extended to frame revitalization strategies to highlight the expertise where significant attention needs to be provided in the post-pandemic period as well as to nurture sustainable development. Finally, the current pandemic scenario is analyzed in terms of occurred changes and is mapped into SWOT factors. Using Fuzzy Technique for Order of Preference by Similarity to Ideal Solution based Multi-Criteria Decision Analysis, these SWOT factors are analyzed to determine where prioritized efforts are needed to focus so as to traverse towards sustainable cities. The results indicate that the enhanced crisis management ability and situational need to restructure the economic model emerges to be the most-significant SWOT factor that can ultimately support humanity for making the cities sustainable.

Internet of Things (IoT) enabled healthcare helps to take the challenges of COVID-19 Pandemic

BACKGROUND/OBJECTIVES

The Internet of Things (IoT) can create disruptive innovation in healthcare. Thus, during COVID-19 Pandemic, there is a need to study different applications of IoT enabled healthcare. For this, a brief study is required for research directions.

METHODS

Research papers on IoT in healthcare and COVID-19 Pandemic are studied to identify this technology's capabilities. This literature-based study may guide professionals in envisaging solutions to related problems and fighting against the COVID-19 type pandemic.

RESULTS

Briefly studied the significant achievements of IoT with the help of a process chart. Then identifies seven major technologies of IoT that seem helpful for healthcare during COVID-19 Pandemic. Finally, the study identifies sixteen basic IoT applications for the medical field during the COVID-19 Pandemic with a brief description of them.

CONCLUSIONS

In the current scenario, advanced information technologies have opened a new door to innovation in our daily lives. Out of these information technologies, the Internet of Things is an emerging technology that provides enhancement and better solutions in the medical field, like proper medical record-keeping, sampling, integration of devices, and causes of diseases. IoT's sensor-based technology provides an excellent capability to reduce the risk of surgery during complicated cases and helpful for COVID-19 type pandemic. In the medical field, IoT's focus is to help perform the treatment of different COVID-19 cases precisely. It makes the surgeon job easier by minimising risks and increasing the overall performance. By using this technology, doctors can easily detect changes in critical parameters of the COVID-19 patient. This information-based service opens up new healthcare opportunities as it moves towards the best way of an information system to adapt world-class results as it enables improvement of treatment systems in the hospital. Medical students can now be better trained for disease detection and well guided for the future course of action. IoT's proper usage can help correctly resolve different medical challenges like speed, price, and complexity. It can easily be customised to monitor calorific intake and treatment like asthma, diabetes, and arthritis of the COVID-19 patient. This digitally controlled health management system can improve the overall performance of healthcare during COVID-19 pandemic days.

Information technology solutions, challenges, and suggestions for tackling the COVID-19 pandemic

Various technology innovations and applications have been developed to fight the coronavirus pandemic. The pandemic also has implications for the design, development, and use of technologies. There is an urgent need for a greater understanding of what roles information systems and technology researchers can play in this global pandemic. This paper examines emerging technologies used to mitigate the threats of COVID-19 and relevant challenges related to technology design, development, and use. It also provides insights and suggestions into how information systems and technology scholars can help fight the COVID-19 pandemic. This paper helps promote future research and technology development to produce better solutions for tackling the COVID-19 pandemic and future pandemics.

Roles of Artificial Intelligence (AI) on COVID-19 Pandemic Crisis Management Policies

The new type of coronaviruses (COVID-19) crisis has had a devastating impact across the world. Public administration discipline addresses emergency crisis management in various ways and dimensions. This article seeks answers to the question “How AI can contribute to crisis management policies to fight against COVID-19 and its impacts?” To this, the techniques and methods of AI in fighting against the COVID-19 virus will be explained in various dimensions. AI can make significant contributions in the preparation, mitigation-prevention, response, and recovery policies in the COVID-19 pandemic crisis. If adopted, AI can be used to find better treatment routes and drug development. Equally, policymakers can benefit from AI as decision support to reach high-quality decisions through fast and accurate data. The paper concludes that governments should create and implement effective AI-based crisis management strategies to fight against the epidemic locally, regionally, nationally, and internationally with a multi-level governance perspective.

iResponse: An AI and IoT-Enabled Framework for Autonomous COVID-19 Pandemic Management

SARS-CoV-2, a tiny virus, is severely affecting the social, economic, and environmental sustainability of our planet, causing infections and deaths (2,674,151 deaths, as of 17 March 2021), relationship breakdowns, depression, economic downturn, riots, and much more. The lessons that have been learned from good practices by various countries include containing the virus rapidly; enforcing containment measures; growing COVID-19 testing capability; discovering cures; providing stimulus packages to the affected; easing monetary policies; developing new pandemic-related industries; support plans for controlling unemployment; and overcoming inequalities. Coordination and multi-term planning have been found to be the key among the successful national and global endeavors to fight the pandemic. The current research and practice have mainly focused on specific aspects of COVID-19 response. There is a need to automate the learning process such that we can learn from good and bad practices during pandemics and normal times. To this end, this paper proposes a technology-driven framework, iResponse, for coordinated and autonomous pandemic management, allowing pandemic-related monitoring and policy enforcement, resource planning and provisioning, and data-driven planning and decision-making. The framework consists of five modules: Monitoring and Break-the-Chain, Cure Development and Treatment, Resource Planner, Data Analytics and Decision Making, and Data Storage and Management. All modules collaborate dynamically to make coordinated and informed decisions. We provide the technical system architecture of a system based on the proposed iResponse framework along with the design details of each of its five components. The challenges related to the design of the individual modules and the whole system are discussed. We provide six case studies in the paper to elaborate on the different functionalities of the iResponse framework and how the framework can be implemented. These include a sentiment analysis case study, a case study on the recognition of human activities, and four case studies using deep learning and other data-driven methods to show how to develop sustainability-related optimal strategies for pandemic management using seven real-world datasets. A number of important findings are extracted from these case studies.

IoT MEMS: IoT-Based Paradigm for Medical Equipment Management Systems of ICUs in Light of COVID-19 Outbreak

Recently, COVID-19 has infected a lot of people around the world. The healthcare systems are overwhelmed because of this virus. The intensive care unit (ICU) as a part of the healthcare sector has faced several challenges due to the poor information quality provided by current ICUs' medical equipment management. IoT has raised the ability for vital data transfer in the healthcare sector of the new century. However, most of the existing paradigms have adopted IoT technology to track patients' health statuses. Therefore, there is a lack of understanding on how to utilize such technology for ICUs' medical equipment management. This paper proposes a novel IoT-based paradigm called IoT Based Paradigm for Medical Equipment Management Systems (IoT MEMS) to manage medical equipment of ICUs efficiently. It employs IoT technology to enhance the information flow between medical equipment management systems (THIS) and ICUs during the COVID-19 outbreak to ensure the highest level of transparency and fairness in reallocating medical equipment. We described in detail the theoretical and practical aspects of IoT MEMS. Adopting IoT MEMS will enhance hospital capacity and capability in mitigating COVID-19 efficiently. It will also positively influence the information quality of (THIS) and strengthen trust and transparency among the stakeholders.

Narrowband-Internet of Things Device-to-Device Simulation: An Open-Sourced Framework

Narrowband-Internet of Things (NB-IoT) displays high-quality connectivity underpinned by fifth-generation (5G) networks to cover a wide array of IoT applications. The devices’ development and integration into different smart systems require permanent control, supervision, and the study of power consumption models to determine the performance of the network topology and allow for the measurement of the efficiency of the network topology’s application. This paper reports on an architecture and open-sourced simulation that was developed to study NB-IoT in Device-to-Device (D2D) mode, which includes the Physical (PHY), network, and application layers, as well as a queuing model, the model for uplink and downlink delays, the throughput, the overall NB-IoT D2D network performance, and the energy consumption based on the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. Our results prove that the suggested framework contributes to a reduction in power consumption, a minimization of queuing delays, a decrease in communication cost, a reduction in inter-cluster collisions, and the prevention of attacks from malicious nodes. Consequently, the framework manages the battery’s State of Charge (SOC), improves the battery’s State of Health (SOH), and maximizes the whole network lifetime. The proposed framework, the code of which has been open-sourced, can be effectively used for scientific research and development purposes to evaluate different parameters and improve the planning of NB-IoT networks.

Utilizing IoT to design a relief supply chain network for the SARS-COV-2 pandemic

The current universally challenging SARS-COV-2 pandemic has transcended all the social, logical, economic, and mortal boundaries regarding global operations. Although myriad global societies tried to address this issue, most of the employed efforts seem superficial and failed to deal with the problem, especially in the healthcare sector. On the other hand, the Internet of Things (IoT) has enabled healthcare system for both better understanding of the patient's condition and appropriate monitoring in a remote fashion. However, there has always been a gap for utilizing this approach on the healthcare system especially in agitated condition of the pandemics. Therefore, in this study, we develop two innovative approaches to design a relief supply chain network is by using IoT to address multiple suspected cases during a pandemic like the SARS-COV-2 outbreak. The first approach (prioritizing approach) minimizes the maximum ambulances response time, while the second approach (allocating approach) minimizes the total critical response time. Each approach is validated and investigated utilizing several test problems and a real case in Iran as well. A set of efficient meta-heuristics and hybrid ones is developed to optimize the proposed models. The proposed approaches have shown their versatility in various harsh SARS-COV-2 pandemic situations being dealt with by managers. Finally, we compare the two proposed approaches in terms of response time and route optimization using a real case study in Iran. Implementing the proposed IoT-based methodology in three consecutive weeks, the results showed 35.54% decrease in the number of confirmed cases.

IoMT amid COVID-19 pandemic: Application, architecture, technology, and security

In many countries, the Internet of Medical Things (IoMT) has been deployed in tandem with other strategies to curb the spread of COVID-19, improve the safety of front-line personnel, increase efficacy by lessening the severity of the disease on human lives, and decrease mortality rates. Significant inroads have been achieved in terms of applications and technology, as well as security which have also been magnified through the rapid and widespread adoption of IoMT across the globe. A number of on-going researches show the adoption of secure IoMT applications is possible by incorporating security measures with the technology. Furthermore, the development of new IoMT technologies merge with Artificial Intelligence, Big Data and Blockchain offers more viable solutions. Hence, this paper highlights the IoMT architecture, applications, technologies, and security developments that have been made with respect to IoMT in combating COVID-19. Additionally, this paper provides useful insights into specific IoMT architecture models, emerging IoMT applications, IoMT security measurements, and technology direction that apply to many IoMT systems within the medical environment to combat COVID-19.

CASE-CF: Context Aware Smart Epidemic Control Framework

Novel Coronavirus (COVID-19) has become one of the deadliest pandemics that has affected almost all the nations in the world. Lockdown and systematic re-opening of shopping malls, offices, etc. is still one of the major weapons against this virus. However, the government and medical agencies take long time to reopen the places due to risks involved in this deadly virus. The delay to reopen places has resulted in sharp decline in the growth of economy. In this paper a current context aware framework is proposed which uses multiple inputs for a specific region to decide whether to open it or not. The proposed framework used series of deep neural network models to generate recommendations specific to a particular region. Most of the inputs are real-time and readily available with the government. The main aim is to develop framework which can be used in any kind of pandemic even in small region to easily contain it. However, it has been tested using opensource data available for COVID-19. Data was crawled from web for 22 districts of Haryana state of India. Experimental result proved the efficiency of proposed framework.

Effects of the COVID-19 pandemic in India: An analysis of policy and technological interventions

Objectives

Following a surge in cases of coronavirus disease 2019 (COVID-19) in June 2020, India became the third-worst affected country worldwide. This study aims to analyse the underlying epidemiological situation in India and explain possible impacts of policy and technological changes.

Methods

Secondary data were utilized, including recently published literature from government sources, the COVID-19 India website and local media reports. These data were analysed, with a focus on the impact of policy and technological interventions.

Results

The spread of COVID-19 in India was initially characterized by fewer cases and lower case fatality rates compared with numbers in many developed countries, primarily due to a stringent lockdown and a demographic dividend. However, economic constraints forced a staggered lockdown exit strategy, resulting in a spike in COVID-19 cases. This factor, coupled with low spending on health as a percentage of gross domestic product (GDP), created mayhem because of inadequate numbers of hospital beds and ventilators and a lack of medical personnel, especially in the public health sector. Nevertheless, technological advances, supported by a strong research base, helped contain the damage resulting from the pandemic.

Conclusions

Following nationwide lockdown, the Indian economy was hit hard by unemployment and a steep decline in growth. The early implementation of lockdown initially decreased the doubling rate of cases and allowed time to upscale critical medical infrastructure. Measures such as asymptomatic testing, public–private partnerships, and technological advances will be essential until a vaccine can be developed and deployed in India.

Public interest summary

The spread of COVID-19 in India was initially characterized by lower case numbers and fewer deaths compared with numbers in many developed countries. This was mainly due to a stringent lockdown and demographic factors. However, economic constraints forced a staggered lockdown exit strategy, resulting in a spike in COVID-19 cases in June 2020. Subsequently, India became the third-worst affected country worldwide. Low spending on health as a percentage of gross domestic product (GDP) meant there was a shortage of hospital beds and ventilators and a lack of medical personnel, especially in the public health sector. Nevertheless, technological advances, supported by a strong research base, helped contain the health and economic damage resulting from the pandemic. In the future, measures such as asymptomatic testing, public–private partnerships, and technological advances will be essential until a vaccine against COVID-19 can be developed and rolled-out in India.

COVID-19 Epidemic in Bangladesh among Rural and Urban Residents: An Online Cross-Sectional Survey of Knowledge, Attitudes, and Practices

As other nations around the world, Bangladesh is facing enormous challenges with the novel coronavirus (COVID-19) epidemic. To design a prevention and control strategy for this new infectious disease, it is essential to first understand people's knowledge, attitudes, and practices (KAP) regarding COVID-19. This study sought to determine KAP among rural and urban residents as well as predictors of preventive practices associated with COVID-19 in Bangladesh. A social media-based (Facebook) cross-sectional survey was conducted to explore these variables among Bangladeshi adults. Of 1520 respondents who completed the questionnaire, low level of good or sufficient knowledge of COVID-19 (70.8%) and practices associated with COVID-19 (73.8%) were found. Despite the low level of knowledge and practices, respondents' attitude (78.9%) towards COVID-19 was relatively high. Results suggest that compared to urban, rural residents are at a particularly high risk of COVID-19 because they were found to have significantly lower knowledge (p = 0.001) and practice levels (p = 0.002) than were urban residents. Multivariable logistic regression analysis identified gender, education, knowledge of COVID-19 transmission, signs and symptoms, and sources of information as factors significantly associated with preventive practices against COVID-19. Further attention and effort should be directed toward increasing both knowledge and practices targeting the general population in Bangladesh, particularly the rural and less educated residents. Findings from this study provide baseline data that can be used to promote integrated awareness of and effective health education programs about COVID-19 prevention and control strategies in Bangladesh, and similar COVID-19 endemic countries.

COVID-19 Epidemic in Bangladesh among Rural and Urban Residents: An Online Cross-Sectional Survey of Knowledge, Attitudes, and Practices

As other nations around the world, Bangladesh is facing enormous challenges with the novel coronavirus (COVID-19) epidemic. To design a prevention and control strategy for this new infectious disease, it is essential to first understand people's knowledge, attitudes, and practices (KAP) regarding COVID-19. This study sought to determine KAP among rural and urban residents as well as predictors of preventive practices associated with COVID-19 in Bangladesh. A social media-based (Facebook) cross-sectional survey was conducted to explore these variables among Bangladeshi adults. Of 1520 respondents who completed the questionnaire, low level of good or sufficient knowledge of COVID-19 (70.8%) and practices associated with COVID-19 (73.8%) were found. Despite the low level of knowledge and practices, respondents' attitude (78.9%) towards COVID-19 was relatively high. Results suggest that compared to urban, rural residents are at a particularly high risk of COVID-19 because they were found to have significantly lower knowledge (p = 0.001) and practice levels (p = 0.002) than were urban residents. Multivariable logistic regression analysis identified gender, education, knowledge of COVID-19 transmission, signs and symptoms, and sources of information as factors significantly associated with preventive practices against COVID-19. Further attention and effort should be directed toward increasing both knowledge and practices targeting the general population in Bangladesh, particularly the rural and less educated residents. Findings from this study provide baseline data that can be used to promote integrated awareness of and effective health education programs about COVID-19 prevention and control strategies in Bangladesh, and similar COVID-19 endemic countries.

Towards Reinforcing Healthcare 4.0: A Green Real-Time IIoT Scheduling and Nesting Architecture for COVID-19 Large-Scale 3D Printing Tasks

With declaring the highly transmissible COVID-19 as a pandemic, an unprecedented strain on healthcare infrastructures worldwide occurred. An enormous shortage in the personal protective equipment (PPE) and the spare parts (SP) for the mechanical ventilators ensued as a consequence of the failure of the centralized global supply chains. Additive manufacturing and Industrial Internet of Things (IIoT), as the pillars of Industry 4.0, arose as the robust noncentralized alternatives. When gathered and properly managed in the IIoT, 3D Printers (3DPs) can complement and support Healthcare 4.0 to face the current and future pandemics. Thus, this paper proposes a real-time green allocation and scheduling architecture designed and dedicated particularly for the large-scale distributed 3D printing tasks (3DPTs) of both PPE and SPs. Our proposed architecture comprises; a broker (B) and a cluster manager (CM). Dynamic status check for the 3DPs and admission control for 3DPTs are among the interconnected roles of CM. CM also performs task allocation and scheduling according to our proposed Online Ascending Load-Balancing Modified Best-Fit (OALMBF) allocation algorithm and Green Real-time Nesting Priority-Based Adaptive (GRNPA) scheduling algorithm. The performance of the proposed architecture was investigated under extremely high-load environments which resulted in a success ratio and a response rate of 99.9667% and 10.9665 seconds, respectively, for the 3000 3DPTs trial. These results proved the robustness and the scalability of our architecture that surpasses its state-of-the-art counterparts. Besides respecting the real-time requirements of the 3DPTs, the proposed architecture improves the utilization of the 3DPs and guarantees an even workload distribution.

The application of wearable smart sensors for monitoring the vital signs of patients in epidemics: a systematic literature review

Wearable smart sensors are emerging technology for daily monitoring of vital signs with the reducing discomfort and interference with normal human activities. The main objective of this study was to review the applied wearable smart sensors for disease control and vital signs monitoring in epidemics outbreaks. A comprehensive search was conducted in Web of Science, Scopus, IEEE Library, PubMed and Google Scholar databases to identify relevant studies published until June 2, 2020. Main extracted specifications for each paper are publication details, type of sensor, disease, type of monitored vital sign, function and usage. Of 277 articles, 11 studies were eligible for criteria. 36% of papers were published in 2020. Articles were published in 10 different journals and only in the Journal of Medical Systems more than one article was published. Most sensors were used to monitor body temperature, heart rate and blood pressure. Wearable devices (like a helmet, watch, or cuff) and body area network sensors were popular types which can be used monitoring vital signs for epidemic trending. 65% of total papers (n = 6) were conducted by the USA, Malaysia and India. Applying appropriate technological solutions could improve control and management of epidemic disease as well as the application of sensors for continuous monitoring of vital signs. However, further studies are needed to investigate the real effects of these sensors and their effectiveness.

Internet of Things for Current COVID-19 and Future Pandemics: an Exploratory Study

In recent years, the Internet of Things (IoT) has gained convincing research ground as a new research topic in a wide variety of academic and industrial disciplines, especially in healthcare. The IoT revolution is reshaping modern healthcare systems by incorporating technological, economic, and social prospects. It is evolving healthcare systems from conventional to more personalized healthcare systems through which patients can be diagnosed, treated, and monitored more easily. The current global challenge of the pandemic caused by the novel severe respiratory syndrome coronavirus 2 presents the greatest global public health crisis since the pandemic influenza outbreak of 1918. At the time this paper was written, the number of diagnosed COVID-19 cases around the world had reached more than 31 million. Since the pandemic started, there has been a rapid effort in different research communities to exploit a wide variety of technologies to combat this worldwide threat, and IoT technology is one of the pioneers in this area. In the context of COVID-19, IoT-enabled/linked devices/applications are utilized to lower the possible spread of COVID-19 to others by early diagnosis, monitoring patients, and practicing defined protocols after patient recovery. This paper surveys the role of IoT-based technologies in COVID-19 and reviews the state-of-the-art architectures, platforms, applications, and industrial IoT-based solutions combating COVID-19 in three main phases, including early diagnosis, quarantine time, and after recovery.

Adoption of Digital Technologies in Health Care During the COVID-19 Pandemic: Systematic Review of Early Scientific Literature

BACKGROUND

The COVID-19 pandemic is favoring digital transitions in many industries and in society as a whole. Health care organizations have responded to the first phase of the pandemic by rapidly adopting digital solutions and advanced technology tools.

OBJECTIVE

The aim of this review is to describe the digital solutions that have been reported in the early scientific literature to mitigate the impact of COVID-19 on individuals and health systems.

METHODS

We conducted a systematic review of early COVID-19-related literature (from January 1 to April 30, 2020) by searching MEDLINE and medRxiv with appropriate terms to find relevant literature on the use of digital technologies in response to the pandemic. We extracted study characteristics such as the paper title, journal, and publication date, and we categorized the retrieved papers by the type of technology and patient needs addressed. We built a scoring rubric by cross-classifying the patient needs with the type of technology. We also extracted information and classified each technology reported by the selected articles according to health care system target, grade of innovation, and scalability to other geographical areas.

RESULTS

The search identified 269 articles, of which 124 full-text articles were assessed and included in the review after screening. Most of the selected articles addressed the use of digital technologies for diagnosis, surveillance, and prevention. We report that most of these digital solutions and innovative technologies have been proposed for the diagnosis of COVID-19. In particular, within the reviewed articles, we identified numerous suggestions on the use of artificial intelligence (AI)-powered tools for the diagnosis and screening of COVID-19. Digital technologies are also useful for prevention and surveillance measures, such as contact-tracing apps and monitoring of internet searches and social media usage. Fewer scientific contributions address the use of digital technologies for lifestyle empowerment or patient engagement.

CONCLUSIONS

In the field of diagnosis, digital solutions that integrate with traditional methods, such as AI-based diagnostic algorithms based both on imaging and clinical data, appear to be promising. For surveillance, digital apps have already proven their effectiveness; however, problems related to privacy and usability remain. For other patient needs, several solutions have been proposed, such as telemedicine or telehealth tools. These tools have long been available, but this historical moment may actually be favoring their definitive large-scale adoption. It is worth taking advantage of the impetus provided by the crisis; it is also important to keep track of the digital solutions currently being proposed to implement best practices and models of care in future and to adopt at least some of the solutions proposed in the scientific literature, especially in national health systems, which have proved to be particularly resistant to the digital transition in recent years.

A, E. B, J. SJP, A. K, S. P. Detection and monitoring of the asymptotic COVID-19 patients using IoT devices and sensors

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

On contact tracing in COVID-19 (SARS-CoV-2) pandemic using lowest common ancestor in m-ary data aggregation tree in the fog-computing enhanced internet of things

Purpose

The purpose of this study is to exploit the lowest common ancestor technique in an m-ary data aggregation tree in the fog computing-enhanced IoT to assist in contact tracing in COVID-19. One of the promising characteristics of the Internet of Things (IoT) that can be used to save the world from the current crisis of COVID-19 pandemic is data aggregation. As the number of patients infected by the disease is already huge, the data related to the different attributes of patients such as patient thermal image record and the previous health record of the patient is going to be gigantic. The authors used the technique of data aggregation to efficiently aggregate the sensed data from the patients and analyse it. Among the various inferences drawn from the aggregated data, one of the most important is contact tracing. Contact tracing in COVID-19 deals with finding out a person or a group of persons who have infected or were infected by the disease.

Design/methodology/approach

The authors propose to exploit the technique of lowest common ancestor in an m-ary data aggregation tree in the Fog-Computing enhanced IoT to help the health-care experts in contact tracing in a particular region or community. In this research, the authors argue the current scenario of COVID-19 pandemic, finding the person or a group of persons who has/have infected a group of people is of extreme importance. Finding the individuals who have been infected or are infecting others can stop the pandemic from worsening by stopping the community transfer. In a community where the outbreak has spiked, the samples from either all the persons or the patients showing the symptoms are collected and stored in an m-ary tree-based structure sorted over time.

Findings

Contact tracing in COVID-19 deals with finding out a person or a group of persons who have infected or were infected by the disease. The authors exploited the technique of lowest common ancestor in an m-ary data aggregation tree in the fog-computing-enhanced IoT to help the health-care experts in contact tracing in a particular region or community. The simulations were carried randomly on a set of individuals. The proposed algorithm given in Algorithm 1 is executed on the samples collected at level-0 of the simulation model, and to aggregate the data and transmit the data, the authors implement Algorithm 2 at the level-1. It is found from the results that a carrier can be easily identified from the samples collected using the approach designed in the paper.

Practical implications

The work presented in the paper can aid the health-care experts fighting the COVID-19 pandemic by reducing the community transfer with efficient contact tracing mechanism proposed in the paper.

Social implications

Fighting COVID-19 efficiently and saving the humans from the pandemic has huge social implications in the current times of crisis.

Originality/value

To the best of the authors’ knowledge, the lowest common ancestor technique in m-ary data aggregation tree in the fog computing-enhanced IoT to contact trace the individuals who have infected or were infected during the transmission of COVID-19 is first of its kind proposed. Creating a graph or an m-ary tree based on the interactions/connections between the people in a particular community like location, friends and time, the authors can attempt to traverse it to find out who infected any two persons or a group of persons or was infected by exploiting the technique of finding out the lowest common ancestor in a m-ary tree.

The Role of Digital Technologies that Could Be Applied for Prescreening in the Mining Industry During the COVID-19 Pandemic

The novel COVID-19 (coronavirus disease of 2019) pandemic has caused global havoc and impacted almost every aspect of human life and the global economy. The mining industry is not immune to such impacts. The pandemic has accelerated the need for digital transformation in the mining industry and in the era of the fourth Industrial Revolution (4IR), there is further application of digital technologies in the early detection and prescreening of emerging infectious and viral diseases to keep mining areas and communities safer and less vulnerable. This paper aims to explore the application of smart digital technologies that could be applied for detection, prescreening and prevention of COVID-19 in the mining industry. The study will contribute, firstly, to demonstrate the utility and applications of digital technologies in the mining industry and, secondly, the development of a body of knowledge that can be consulted to prevent the spread of the disease in the mining industry.