An IoT Framework for Screening of COVID-19 Using Real-Time Data from Wearable Sensors

The role of blockchain to secure internet of medical things

This study explores integrating blockchain technology into the Internet of Medical Things (IoMT) to address security and privacy challenges. Blockchain's transparency, confidentiality, and decentralization offer significant potential benefits in the healthcare domain. The research examines various blockchain components, layers, and protocols, highlighting their role in IoMT. It also explores IoMT applications, security challenges, and methods for integrating blockchain to enhance security. Blockchain integration can be vital in securing and managing this data while preserving patient privacy. It also opens up new possibilities in healthcare, medical research, and data management. The results provide a practical approach to handling a large amount of data from IoMT devices. This strategy makes effective use of data resource fragmentation and encryption techniques. It is essential to have well-defined standards and norms, especially in the healthcare sector, where upholding safety and protecting the confidentiality of information are critical. These results illustrate that it is essential to follow standards like HIPAA, and blockchain technology can help ensure these criteria are met. Furthermore, the study explores the potential benefits of blockchain technology for enhancing inter-system communication in the healthcare industry while maintaining patient privacy protection. The results highlight the effectiveness of blockchain's consistency and cryptographic techniques in combining identity management and healthcare data protection, protecting patient privacy and data integrity. Blockchain is an unchangeable distributed ledger system. In short, the paper provides important insights into how blockchain technology may transform the healthcare industry by effectively addressing significant challenges and generating legal, safe, and interoperable solutions. Researchers, doctors, and graduate students are the audience for our paper.

Image Smart Segmentation Analysis Against Diabetic Foot Ulcer Using Internet of Things with Virtual Sensing

Diabetic foot ulcer (DFU) is a problem worldwide, and prevention is crucial. The image segmentation analysis of DFU identification plays a significant role. This will produce different segmentation of the same idea, incomplete, imprecise, and other problems. To address these issues, a method of image segmentation analysis of DFU through internet of things with the technique of virtual sensing for semantically similar objects, the analysis of four levels of range segmentation (region-based, edge-based, image-based, and computer-aided design-based range segmentation) for deeper segmentation of images is implemented. In this study, the multimodal is compressed with the object co-segmentation for semantical segmentation. The result is predicting the better validity and reliability assessment. The experimental results demonstrate that the proposed model can efficiently perform segmentation analysis, with a lower error rate, than the existing methodologies. The findings on the multiple-image dataset show that DFU obtains an average segmentation score of 90.85% and 89.03% correspondingly in two types of labeled ratios before DFU with virtual sensing and after DFU without virtual sensing (i.e., 25% and 30%), which is an increase of 10.91% and 12.22% over the previous best results. In live DFU studies, our proposed system improved by 59.1% compared with existing deep segmentation-based techniques and its average image smart segmentation improvements over its contemporaries are 15.06%, 23.94%, and 45.41%, respectively. Proposed range-based segmentation achieves interobserver reliability by 73.9% on the positive test namely likelihood ratio test set with only a 0.25 million parameters at the pace of labeled data.

Mobile Apps for COVID-19 Detection and Diagnosis for Future Pandemic Control: Multidimensional Systematic Review

BACKGROUND

In the modern world, mobile apps are essential for human advancement, and pandemic control is no exception. The use of mobile apps and technology for the detection and diagnosis of COVID-19 has been the subject of numerous investigations, although no thorough analysis of COVID-19 pandemic prevention has been conducted using mobile apps, creating a gap.

OBJECTIVE

With the intention of helping software companies and clinical researchers, this study provides comprehensive information regarding the different fields in which mobile apps were used to diagnose COVID-19 during the pandemic.

METHODS

In this systematic review, 535 studies were found after searching 5 major research databases (ScienceDirect, Scopus, PubMed, Web of Science, and IEEE). Of these, only 42 (7.9%) studies concerned with diagnosing and detecting COVID-19 were chosen after applying inclusion and exclusion criteria using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol.

RESULTS

Mobile apps were categorized into 6 areas based on the content of these 42 studies: contact tracing, data gathering, data visualization, artificial intelligence (AI)-based diagnosis, rule- and guideline-based diagnosis, and data transformation. Patients with COVID-19 were identified via mobile apps using a variety of clinical, geographic, demographic, radiological, serological, and laboratory data. Most studies concentrated on using AI methods to identify people who might have COVID-19. Additionally, symptoms, cough sounds, and radiological images were used more frequently compared to other data types. Deep learning techniques, such as convolutional neural networks, performed comparatively better in the processing of health care data than other types of AI techniques, which improved the diagnosis of COVID-19.

CONCLUSIONS

Mobile apps could soon play a significant role as a powerful tool for data collection, epidemic health data analysis, and the early identification of suspected cases. These technologies can work with the internet of things, cloud storage, 5th-generation technology, and cloud computing. Processing pipelines can be moved to mobile device processing cores using new deep learning methods, such as lightweight neural networks. In the event of future pandemics, mobile apps will play a critical role in rapid diagnosis using various image data and clinical symptoms. Consequently, the rapid diagnosis of these diseases can improve the management of their effects and obtain excellent results in treating patients.

Efficient Feature-Selection-Based Stacking Model for Stress Detection Based on Chest Electrodermal Activity

Contemporary advancements in wearable equipment have generated interest in continuously observing stress utilizing various physiological indicators. Early stress detection can improve healthcare by lessening the negative effects of chronic stress. Machine learning (ML) methodologies have been modified for healthcare equipment to monitor user health situations utilizing sufficient user information. Nevertheless, more data are needed to make applying Artificial Intelligence (AI) methodologies in the medical field easier. This research aimed to detect stress using a stacking model based on machine learning algorithms using chest-based features from the Wearable Stress and Affect Detection (WESAD) dataset. We converted this natural dataset into a convenient format for the suggested model by performing data visualization and preprocessing using the RESP feature and feature analysis using the Z-score, SelectKBest feature, the Synthetic Minority Over-Sampling Technique (SMOTE), and normalization. The efficiency of the proposed model was estimated regarding accuracy, precision, recall, and F1-score. The experimental outcome illustrated the efficacy of the proposed stacking technique, achieving 0.99% accuracy. The results revealed that the proposed stacking methodology performed better than traditional methodologies and previous studies.

COVID-19 symptom identification using Deep Learning and hardware emulated systems

The COVID-19 pandemic disrupted regular global activities in every possible way. This pandemic, caused by the transmission of the infectious Coronavirus, is characterized by main symptoms such as fever, fatigue, cough, and loss of smell. A current key focus of the scientific community is to develop automated methods that can effectively identify COVID-19 patients and are also adaptable for foreseen future virus outbreaks. To classify COVID-19 suspects, it is required to use contactless automatic measurements of more than one symptom. This study explores the effectiveness of using Deep Learning combined with a hardware-emulated system to identify COVID-19 patients in Sri Lanka based on two main symptoms: cough and shortness of breath. To achieve this, a Convolutional Neural Network (CNN) based on Transfer Learning was employed to analyze and compare the features of a COVID-19 cough with other types of coughs. Real-time video footage was captured using a FLIR C2 thermal camera and a web camera and subsequently processed using OpenCV image processing algorithms. The objective was to detect the nasal cavities in the video frames and measure the breath cycles per minute, thereby identifying instances of shortness of breath. The proposed method was first tested on crowd-sourced datasets (Coswara, Coughvid, ESC-50, and a dataset from Kaggle) obtained online. It was then applied and verified using a dataset obtained from local hospitals in Sri Lanka. The accuracy of the developed methodologies in diagnosing cough resemblance and recognizing shortness of breath was found to be 94% and 95%, respectively.

Recent Progress in Emerging Novel MXenes Based Materials and their Fascinating Sensing Applications

Early transition metals based 2D carbides, nitrides and carbonitrides nanomaterials are known as MXenes, a novel and extensive new class of 2D materials family. Since the first accidently synthesis based discovery of Ti₃ C₂ in 2011, more than 50 additional compositions have been experimentally reported, including at least eight distinct synthesis methods and also more than 100 stoichiometries are theoretically studied. Due to its distinctive surface chemistry, graphene like shape, metallic conductivity, high hydrophilicity, outstanding mechanical and thermal properties, redox capacity and affordable with mass-produced nature, this diverse MXenes are of tremendous scientific and technological significance. In this review, first we'll come across the MXene based nanomaterials possible synthesis methods, their advantages, limitations and future suggestions, new chemistry related to their selected properties and potential sensing applications, which will help us to explain why this family is growing very fast as compared to other 2D families. Secondly, problems that help to further improve commercialization of the MXene nanomaterials based sensors are examined, and many advances in the commercializing of the MXene nanomaterials based sensors are proposed. At the end, we'll go through the current challenges, limitations and future suggestions.

Smart Machine Health Prediction Based on Machine Learning in Industry Environment

In an industrial setting, consistent production and machine maintenance might help any company become successful. Machine health checking is a method of observing the status of a machine to predict mechanical mileage and predict the machine’s disappointment. The most often utilized traditional approaches are reactive and preventive maintenance. These approaches are unreliable and wasteful in terms of time and resource utilization. The use of system health management in conjunction with a predictive maintenance strategy allows for the scheduling of maintenance times in such a way that device malfunction is avoided, and thus the repercussions are avoided. IoT can help monitor equipment health and provide the best outcomes, especially in an industrial setting. Internet of Things (IoT) and machine learning models are quite successful in providing ongoing knowledge and comprehensive study on infrastructure performance. Our suggested technique uses a mobile application that seeks to anticipate the machine’s health status using a classification method utilizing IoT and machine learning technologies, which might benefit the industry environment by alerting the appropriate maintenance team before inflicting significant harm to the system and disrupting normal operations. A comparison of decision tree, XGBoost, SVM, and KNN performance has been carried out. According to our findings, XGBoost achieves higher classification accuracy compared to the other algorithms. As a result, this model is selected for creating a user-based application that allows the user to easily check the state of the machine’s health.

Chronic cough-the limitation and advances in assessment techniques

Accurate and consistent assessments of cough are essential to advance the understanding of the mechanisms of cough and individualised the management of patients. Considerable progress has been made in this work. Here we reviewed the currently available tools for subjectively and objectively measuring both cough sensitivity and severity. We also provided some opinions on the new techniques and future directions. The simple and practical Visual Analogue Scale (VAS), the Leicester Cough Questionnaire (LCQ), and the Cough Specific Quality of Life Questionnaire (CQLQ) are the most widely used self-reported questionnaires for evaluating and quantifying cough severity. The Hull Airway Reflux Questionnaire (HARQ) is a tool to elucidate the constellation of symptoms underlying the diagnosis of chronic cough. Chemical excitation tests are widely used to explore the pathophysiological mechanisms of the cough reflex, such as capsaicin, citric acid and adenosine triphosphate (ATP) challenge test. Cough frequency is an ideal primary endpoint for clinical research, but the application of cough counters has been limited in clinical practice by the high cost and reliance on aural validation. The ongoing development of cough detection technology for smartphone apps and wearable devices will hopefully simplify cough counting, thus transitioning it from niche research to a widely available clinical application.

Smart Hand Sanitisers in the Workplace: A Survey of Attitudes towards an Internet of Things Technology

An online survey was circulated to employees from a wide range of organisations to gauge attitudes towards the idea of using smart hand sanitisers in the workplace. The sanitisers are capable of real-time monitoring and providing feedback that varies according to the hand hygiene behaviour of users. In certain circumstances, the sanitisers can monitor individuals, making it possible to identify workers whose hand hygiene falls below a certain standard. The survey was circulated between July and August 2021 during the COVID-19 pandemic. Data gathered from 314 respondents indicated support for some features of the technology, but also indicated concern about invasions of privacy and the possibility of coercion. Attitudes towards the possible implementation of the technology varied significantly according to certain characteristics of the sample, but particularly with age. Respondents above the median age were more likely to support the use of data in ways that could facilitate the promotion and enforcement of hand hygiene practices.

Implementation of Combined Machine Learning with the Big Data Model in IoMT Systems for the Prediction of Network Resource Consumption and Improving the Data Delivery

In recent years, health applications based on the internet of medical things have exploded in popularity in smart cities (IoMT). Many real-time systems help both patients and professionals by allowing remote data access and appropriate responses. The major research problems include making timely medical judgments and efficiently managing massive data utilising IoT-based resources. Furthermore, in many proposed solutions, the dispersed nature of data processing openly raises the risk of information leakage and compromises network integrity. Medical sensors are burdened by such solutions, which reduce the stability of real-time transmission systems. As a result, this study provides a machine-learning approach with SDN-enabled security to forecast network resource usage and enhance sensor data delivery. With a low administration cost, the software define network (SDN) design allows the network to resist dangers among installed sensors. It provides an unsupervised machine learning approach that reduces IoT network communication overheads and uses dynamic measurements to anticipate link status and refines its tactics utilising SDN architecture. Finally, the SDN controller employs a security mechanism to efficiently regulate the consumption of IoT nodes while also protecting them against unidentified events. When the number of nodes and data production rate varies, the suggested approach enhances network speed. As a result, to organise the nodes in a cluster, the suggested model uses an iterative centroid technique. By balancing network demand via durable connections, the multihop transmission technique for routing IoT data improves speed while simultaneously lowering the energy hole problem.

Blockchain for Modern Applications: A Survey

Blockchain is a modern technology that has revolutionized the way society interacts and trades. It could be defined as a chain of blocks that stores information with digital signatures in a distributed and decentralized network. This technique was first adopted for the creation of digital cryptocurrencies, such as Bitcoin and Ethereum. However, research and industrial studies have recently focused on the opportunities that blockchain provides in various other application domains to take advantage of the main features of this technology, such as: decentralization, persistency, anonymity, and auditability. This paper reviews the use of blockchain in several interesting fields, namely: finance, healthcare, information systems, wireless networks, Internet of Things, smart grids, governmental services, and military/defense. In addition, our paper identifies the challenges to overcome, to guarantee better use of this technology.

Multichannel DenseNet Architecture for Classification of Mammographic Breast Density for Breast Cancer Detection

Percentage mammographic breast density (MBD) is one of the most notable biomarkers. It is assessed visually with the support of radiologists with the four qualitative Breast Imaging Reporting and Data System (BIRADS) categories. It is demanding for radiologists to differentiate between the two variably allocated BIRADS classes, namely, “BIRADS C and BIRADS D.” Recently, convolution neural networks have been found superior in classification tasks due to their ability to extract local features with shared weight architecture and space invariance characteristics. The proposed study intends to examine an artificial intelligence (AI)-based MBD classifier toward developing a latent computer-assisted tool for radiologists to distinguish the BIRADS class in modern clinical progress. This article proposes a multichannel DenseNet architecture for MBD classification. The proposed architecture consists of four-channel DenseNet transfer learning architecture to extract significant features from a single patient's two a mediolateral oblique (MLO) and two craniocaudal (CC) views of digital mammograms. The performance of the proposed classifier is evaluated using 200 cases consisting of 800 digital mammograms of the different BIRADS density classes with validated density ground truth. The classifier's performance is assessed with quantitative metrics such as precision, responsiveness, specificity, and the area under the curve (AUC). The concluding preliminary outcomes reveal that this intended multichannel model has delivered good performance with an accuracy of 96.67% during training and 90.06% during testing and an average AUC of 0.9625. Obtained results are also validated qualitatively with the help of a radiologist expert in the field of MBD. Proposed architecture achieved state-of-the-art results with a fewer number of images and with less computation power.

Building predictive model for COVID-19 using artificial neural network (ANN) algorithm

Machine learning plays an important role in addressing the pandemic crisis to analyse, identify and to forecast the infection and the spread of any contagious virus. Nowadays, most of the organizations and researchers are moving towards machine learning algorithms to develop predictive models, trying to reduce the death rate and to identify the patients who are at the increased risk of mortality. The major challenge of Covid-19 is, its identification and classification, due to the fact that the symptoms of Covid -19 are similar to other infectious diseases such as viral fever, typhoid, dengue, pneumonia and other lung infectious diseases. The objective of this paper is to build a predictive model for covid-19 using the Artificial Neural Network (ANN), a supervised machine learning Algorithm. In this study, the data set from Kaggle Sírio-Libanês data for AI and Analytics by the Data Intelligence Team has been used to build the predictive model. It is observed that there is 73% of accuracy in predicting the number of corona infected cases.

Implementation of network information security monitoring system based on adaptive deep detection

For a better detection in Network information security monitoring system, the author proposes a method based on adaptive depth detection. A deep belief network (DBN) was designed and implemented, and the intrusion detection system model was combined with a support vector machine (SVM). The data set adopts the NSL-KDD network communication data set, and this data set is authoritative in the security field. Redundant cleaning, data type conversion, normalization, and other processing operations are performed on the data set. Using the data conversion method based on the probability mass function probability mass function coding, a standard data set with low redundancy and low dimensionality can be obtained. Research indicates that when the batch size reaches 64, the accuracy of the test set reaches its maximum value. As the batch size increases, the accuracy first increases and then decreases. When the batch size continues to increase, the model will inevitably fall into the local optimal state, resulting in the degradation of the detection performance of the system. In terms of the false alarm rate, the DBN-SVM model is also the highest; however, it is only 10.73%. Under the premise of increasing the detection rate, the false alarm rate is improved; for the overall detection performance of the model, it is within an acceptable range. In terms of accuracy, the DBN-SVM model also scored the highest. The accuracy rate is the ratio of normal and correct classification for intrusion detection. It can explain the detection ability of the model. In summary, the overall detection ability of the DBN-SVM model is the best. The good classification ability to use SVM is proved, and the classification of low-dimensional features is expected to increase the detection rate of the system.

A physiological signal compression approach using optimized Spindle Convolutional Auto-encoder in mHealth applications

Background and Objectives

The COVID-19 pandemic manifested the need of developing robust digital platforms for facilitating healthcare services such as consultancy, clinical therapies, real time remote monitoring, early diagnosis and future predictions. Innovations made using technologies such as Internet of Things (IoT), edge computing, cloud computing and artificial intelligence are helping address this crisis. The urge for remote monitoring, symptom analysis and early detection of diseases lead to tremendous increase in the deployment of wearable sensor devices. They facilitate seamless gathering of physiological data such as electrocardiogram (ECG) signals, respiration traces (RESP), galvanic skin response (GSR), pulse rate, body temperature, photoplethysmograms (PPG), oxygen saturation (SpO2) etc. For diagnosis and analysis purpose, the gathered data needs to be stored. Wearable devices operate on batteries and have a memory constraint. In mHealth application architectures, this gathered data is hence stored on cloud based servers. While transmitting data from wearable devices to cloud servers via edge devices, a lot of energy is consumed. This paper proposes a deep learning based compression model SCAElite that reduces the data volume, enabling energy efficient transmission.

Results

Stress Recognition in Automobile Drivers dataset and MIT-BIH dataset from PhysioNet are used for validation of algorithm performance. The model achieves a compression ratio of up to 300 fold with reconstruction errors within 8% over the stress recognition dataset and 106.34-fold with reconstruction errors within 8% over the MIT-BIH dataset. The computational complexity of SCAElite is 51.65% less compared to state-of-the-art deep compressive model.

Conclusion

It is experimentally validated that SCAElite guarantees a high compression ratio with good quality restoration capabilities for physiological signal compression in mHealth applications. It has a compact architecture and is computationally more efficient compared to state-of-the-art deep compressive model.

Potential of Internet of Medical Things (IoMT) applications in building a smart healthcare system: A systematic review

Sudden spurting of Corona virus disease (COVID-19) has put the whole healthcare system on high alert. Internet of Medical Things (IoMT) has eased the situation to a great extent, also COVID-19 has motivated scientists to make new 'Smart' healthcare system focusing towards early diagnosis, prevention of spread, education and treatment and facilitate living in the new normal. This review aims to identify the role of IoMT applications in improving healthcare system and to analyze the status of research demonstrating effectiveness of IoMT benefits to the patient and healthcare system along with a brief insight into technologies supplementing IoMT and challenges faced in developing a smart healthcare system. An internet-based search in PUBMED, Google Scholar and IEEE Library for english language publications using relevant terms resulted in 987 articles. After screening title, abstract, and content related to IoMT in healthcare and excluding duplicate articles, 135 articles published in journal with impact factor ≥1 were eligible for inclusion. Also relevant articles from the references of the selected articles were considered. The habituation of IoMT and related technology has resolved several difficulties using remote monitoring, telemedicine, robotics, sensors etc. However mass adoption seems challenging due to factors like privacy and security of data, management of large amount of data, scalability and upgradation etc. Although ample knowledge has been compiled and exchanged, this structured systematic review will help the healthcare practitioners, policymakers/decision makers, scientists and researchers to gauge the applicability of IoMT in healthcare more efficiently.

Advances in the Rapid Diagnostic of Viral Respiratory Tract Infections

Viral infections are a significant public health problem, primarily due to their high transmission rate, various pathological manifestations, ranging from mild to severe symptoms and subclinical onset. Laboratory diagnostic tests for infectious diseases, with a short enough turnaround time, are promising tools to improve patient care, antiviral therapeutic decisions, and infection prevention. Numerous microbiological molecular and serological diagnostic testing devices have been developed and authorised as benchtop systems, and only a few as rapid miniaturised, fully automated, portable digital platforms. Their successful implementation in virology relies on their performance and impact on patient management. This review describes the current progress and perspectives in developing micro- and nanotechnology-based solutions for rapidly detecting human viral respiratory infectious diseases. It provides a nonexhaustive overview of currently commercially available and under-study diagnostic testing methods and discusses the sampling and viral genetic trends as preanalytical components influencing the results. We describe the clinical performance of tests, focusing on alternatives such as microfluidics-, biosensors-, Internet-of-Things (IoT)-based devices for rapid and accurate viral loads and immunological responses detection. The conclusions highlight the potential impact of the newly developed devices on laboratory diagnostic and clinical outcomes.

An Artificial Intelligence Approach for Expurgating Edible and Non-Edible Items

In the pandemic of COVID-19, it is crucial to consider the hygiene of the edible and nonedible things as it could be dangerous for our health to consume infected things. Furthermore, everything cannot be boiled before eating as it can destroy fruits and essential minerals and proteins. So, there is a dire need for a smart device that could sanitize edible items. The Germicidal Ultraviolet C (UVC) has proved the capabilities of destroying viruses and pathogens found on the surface of any objects. Although, a few minutes exposure to the UVC can destroy or inactivate the viruses and the pathogens, few doses of UVC light may damage the proteins of edible items and can affect the fruits and vegetables. To this end, we have proposed a novel design of a device that is employed with Artificial Intelligence along with UVC to auto detect the edible items and act accordingly. This causes limited UVC doses to be applied on different items as detected by proposed model according to their permissible limit. Additionally, the device is employed with a smart architecture which leads to consistent distribution of UVC light on the complete surface of the edible items. This results in saving the health as well as nutrition of edible items.

Internet of Things Concept in the Context of the COVID-19 Pandemic: A Multi-Sensor Application Design

In this paper, we present the design, development and implementation of an integrated system for the management of COVID-19 patient, using the LoRaWAN communication infrastructure. Our system offers certain advantages when compared to other similar solutions, allowing remote symptom and health monitoring that can be applied to isolated or quarantined people, without any external interaction with the patient. The IoT wearable device can monitor parameters of health condition like pulse, blood oxygen saturation, and body temperature, as well as the current location. To test the performance of the proposed system, two persons under quarantine were monitored, for a complete 14-day standard quarantine time interval. Based on the data transmitted to the monitoring center, the medical staff decided, after several days of monitoring, when the measured values were outside of the normal parameters, to do an RT-PCR test for one of the two persons, confirming the SARS-CoV2 virus infection. We have to emphasize the high degree of scalability of the proposed solution that can oversee a large number of patients at the same time, thanks to the LoRaWAN communication protocol used. This solution can be successfully implemented by local authorities to increase monitoring capabilities, also saving lives.

Ten public health strategies to control the Covid-19 pandemic: the Saudi Experience

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KSA plans during the pandemic in managing the healthcare system were robust and sustainable.

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Previous epidemics and mass gathering have helped to use the resources effectively.

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Digital health and the prompt response to pandemic warnings were factors of success

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Healthcare supplies and difficulties of online education were among the challenges.

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It is recommended to revise the disaster and emergency strategic plans in KSA.

Saudi Arabia plays an important strategic role within the Middle East and afar because of its geographical location, and being the host of one of the largest annual religious mass gatherings in the world “The Hajj”. During the recent coronavirus pandemic, the Kingdom of Saudi Arabia (KSA) developed a multisectoral plan that adopted multiple measures to limit the spread of Covid-19 transmission both domestically and internationally. In this article, we review all public health related policy decisions from the Saudi Ministry of Health, other government departments, and the private sector that contributed to limiting the severe consequences from Covid-19. Ten effective strategies are outlined and the challenges related to their implementation are explored. The strategies include: 1. Quarantine and travel restriction, 2. Expansion of serological screening, 3. Mask wearing (covering the face and nose) and social distancing, 4. Preparation of hospitals to deal with the influx of coronavirus cases, 5. Use of artificial intelligence, 6.Public assurance, 7.Removal of slum areas and re housing of its inhabitants, 8. Cancellation of the Hajj season, 9. Economic stimulus packages to safeguard the economy, and 10. fair and priority driven vaccine distribution.

Conclusion: The government of Saudi Arabia demonstrated responsibility at the highest level to prioritize the safety and well-being of its citizens and residents. Rapid early response to the pandemic warnings, extensive experience in previous epidemics and in mass gathering medicine, wise management of healthcare resources, and unprecedented harmonization of governmental and private sectors were significant factors for this success.

Applications of Machine Learning and High-Performance Computing in the Era of COVID-19

During the ongoing pandemic of the novel coronavirus disease 2019 (COVID-19), latest technologies such as artificial intelligence (AI), blockchain, learning paradigms (machine, deep, smart, few short, extreme learning, etc.), high-performance computing (HPC), Internet of Medical Things (IoMT), and Industry 4.0 have played a vital role. These technologies helped to contain the disease’s spread by predicting contaminated people/places, as well as forecasting future trends. In this article, we provide insights into the applications of machine learning (ML) and high-performance computing (HPC) in the era of COVID-19. We discuss the person-specific data that are being collected to lower the COVID-19 spread and highlight the remarkable opportunities it provides for knowledge extraction leveraging low-cost ML and HPC techniques. We demonstrate the role of ML and HPC in the context of the COVID-19 era with the successful implementation or proposition in three contexts: (i) ML and HPC use in the data life cycle, (ii) ML and HPC use in analytics on COVID-19 data, and (iii) the general-purpose applications of both techniques in COVID-19’s arena. In addition, we discuss the privacy and security issues and architecture of the prototype system to demonstrate the proposed research. Finally, we discuss the challenges of the available data and highlight the issues that hinder the applicability of ML and HPC solutions on it.