Prospect and application of Internet of Things technology for prevention of SARIs

A Security Framework for Increasing Data and Device Integrity in Internet of Things Systems

The trustworthiness of a system is not just about proving the identity or integrity of the hardware but also extends to the data, control, and management planes of communication between devices and the software they are running. This trust in data and device integrity is desirable for Internet of Things (IoT) systems, especially in critical environments. In this study, we developed a security framework, IoTAttest, for building IoT systems that leverage the Trusted Platform Module 2.0 and remote attestation technologies to enable the establishment of IoT devices' collected data and control plan traffic integrity. After presenting the features and reference architecture of IoTAttest, we evaluated the privacy preservation and validity through the implementation of two proof-of-concept IoT applications that were designed by two teams of university students based on the reference architecture. After the development, the developers answered open questions regarding their experience and perceptions of the framework's usability, limitations, scalability, extensibility, potential, and security. The results indicate that IoTAttest can be used to develop IoT systems with effective attestation to achieve device and data integrity. The proof-of-concept solutions' outcomes illustrate the functionalities and performance of the IoT framework. The feedback from the proof-of-concept developers affirms that they perceived the framework as usable, scalable, extensible, and secure.

Technology Applications in Tracking 2019-nCoV and Defeating Future Outbreaks: Iraqi Healthcare Industry in IoT Remote

A serious effect on people's life, social communication, and surely on medical staff who were forced to monitor their patients' status remotely relying on the available technologies to avoid potential infections and as a result reducing the workload in hospitals. this research tried to investigate the readiness level of healthcare professionals in both public and private Iraqi hospitals to utilize IoT technology in detecting, tracking, and treating 2019-nCoV pandemic, as well as reducing the direct contact between medical staff and patients with other diseases that can be monitored remotely.A cross-sectional descriptive research via online distributed questionnaire, the sample consisted of 113 physicians and 99 pharmacists from three public and two private hospitals who randomly selected by simple random sampling. The 212 responses were deeply analyzed descriptively using frequencies, percentages, means, and standard deviation.The results confirmed that the IoT technology can facilitate patient follow-up by enabling rapid communication between medical staff and patient relatives. Additionally, remote monitoring techniques can measure and treat 2019-nCoV, reducing direct contact by decreasing the workload in healthcare industries. This paper adds to the current healthcare technology literature in Iraq and middle east region an evidence of the readiness to implement IoT technology as an essential technique. Practically, it is strongly advised that healthcare policymakers should implement IoT technology nationwide especially when it comes to safe their employees' life.Iraqi medical staff are fully ready to adopt IoT technology as they became more digital minded after the 2019-nCoV crises and surely their knowledge and technical skills will be improved spontaneously based on diffusion of innovation perspective.

Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare

Artificial intelligence (AI) is a modern approach based on computer science that develops programs and algorithms to make devices intelligent and efficient for performing tasks that usually require skilled human intelligence. AI involves various subsets, including machine learning (ML), deep learning (DL), conventional neural networks, fuzzy logic, and speech recognition, with unique capabilities and functionalities that can improve the performances of modern medical sciences. Such intelligent systems simplify human intervention in clinical diagnosis, medical imaging, and decision-making ability. In the same era, the Internet of Medical Things (IoMT) emerges as a next-generation bio-analytical tool that combines network-linked biomedical devices with a software application for advancing human health. In this review, we discuss the importance of AI in improving the capabilities of IoMT and point-of-care (POC) devices used in advanced healthcare sectors such as cardiac measurement, cancer diagnosis, and diabetes management. The role of AI in supporting advanced robotic surgeries developed for advanced biomedical applications is also discussed in this article. The position and importance of AI in improving the functionality, detection accuracy, decision-making ability of IoMT devices, and evaluation of associated risks assessment is discussed carefully and critically in this review. This review also encompasses the technological and engineering challenges and prospects for AI-based cloud-integrated personalized IoMT devices for designing efficient POC biomedical systems suitable for next-generation intelligent healthcare.

A Large-Scale Clinical Validation Study Using nCapp Cloud Plus Terminal by Frontline Doctors for the Rapid Diagnosis of COVID-19 and COVID-19 pneumonia in China

Background

The outbreak of coronavirus disease 2019 (COVID-19) has become a global pandemic acute infectious disease, especially with the features of possible asymptomatic carriers and high contagiousness. Currently, it is difficult to quickly identify asymptomatic cases or COVID-19 patients with pneumonia due to limited access to reverse transcription-polymerase chain reaction (RT-PCR) nucleic acid tests and CT scans.

Goal

This study aimed to develop a scientific and rigorous clinical diagnostic tool for the rapid prediction of COVID-19 cases based on a COVID-19 clinical case database in China, and to assist doctors to efficiently and precisely diagnose asymptomatic COVID-19 patients and cases who had a false-negative RT-PCR test result.

Methods

With online consent, and the approval of the ethics committee of Zhongshan Hospital Fudan University (NCT04275947, B2020-032R) to ensure that patient privacy is protected, clinical information has been uploaded in real-time through the New Coronavirus Intelligent Auto-diagnostic Assistant Application of cloud plus terminal (nCapp) by doctors from different cities (Wuhan, Shanghai, Harbin, Dalian, Wuxi, Qingdao, Rizhao, and Bengbu) during the COVID-19 outbreak in China. By quality control and data anonymization on the platform, a total of 3,249 cases from COVID-19 high-risk groups were collected. The effects of different diagnostic factors were ranked based on the results from a single factor analysis, with 0.05 as the significance level for factor inclusion and 0.1 as the significance level for factor exclusion. Independent variables were selected by the step-forward multivariate logistic regression analysis to obtain the probability model.

Findings

We applied the statistical method of a multivariate regression model to the training dataset (1,624 cases) and developed a prediction model for COVID-19 with 9 clinical indicators that are accessible. The area under the receiver operating characteristic (ROC) curve (AUC) for the model was 0.88 (95% CI: 0.86, 0.89) in the training dataset and 0.84 (95% CI: 0.82, 0.86) in the validation dataset (1,625 cases).

Discussion

With the assistance of nCapp, a mobile-based diagnostic tool developed from a large database that we collected from COVID-19 high-risk groups in China, frontline doctors can rapidly identify asymptomatic patients and avoid misdiagnoses of cases with false-negative RT-PCR results.

Epidemic Risk Assessment by a Novel Communication Station Based Method

The COVID-19 pandemic has caused serious consequences in the last few months and trying to control it has been the most important objective. With effective prevention and control methods, the epidemic has been gradually under control in some countries and it is essential to ensure safe work resumption in the future. Although some approaches are proposed to measure people's healthy conditions, such as filling health information forms or evaluating people's travel records, they cannot provide a fine-grained assessment of the epidemic risk. In this paper, we propose a novel epidemic risk assessment method based on the granular data collected by the communication stations. We first compute the epidemic risk of these stations in different intervals by combining the number of infected persons and the way they pass through the station. Then, we calculate the personnel risk in different intervals according to the station trajectory of the queried person. This method could assess people's epidemic risk accurately and efficiently. We also conduct extensive simulations and the results verify the effectiveness of the proposed method.

Internet of Things-based smart helmet to detect possible COVID-19 infections

At the beginning of 2020, while the world was celebrating New Year’s Eve, China’s headquarter of the World Health Organization came across a case of pneumonia in the city of Wuhan, China and was termed as coronavirus. Initially the symptoms were fever, cold, and cough; so thermal screening was done that could cause infection to the medical staff. In this chapter we discuss the design of the system known as smart helmet that has the capability to detect coronavirus automatically by using thermal imaging, which is used to capture the image with less human interaction. The thermal camera technology is integrated with smart helmets and combined with Internet of Things technology for monitoring of the screening process to get the real-time data. It is equipped with facial recognition technology; it can also display personal information of the infectee, which can automatically take temperature and can detect more infectee than normal thermal screening.

Development and performance characteristics evaluation of a new Bioelectric Recognition Assay (BERA) method for rapid Sars-CoV-2 detection in clinical samples

Introduction

As the second wave of COVID-19 pandemic is in progress the development of fast and cost-effective approaches for diagnosis is essential. The aim of the present study was to develop and evaluate the performance characteristics of a new Bioelectric Recognition Assay (BERA) regarding Sars-CoV-2 detection in clinical samples and its potential to be used as a point of care test.

Materials and methods

All tests were performed using a custom portable hardware device developed by EMBIO DIAGNOSTICS (EMBIO DIAGNOSTICS Ltd, Cyprus). 110 positive and 136 negative samples tested by RT-PCR were used in order to define the lower limit of detection (L.O.D.) of the system, as well as the sensitivity and the specificity of the method.

Results

The system was able to detect a viral concentration of 4 genome copies/μL. The method displayed total sensitivity of 92.7 % (95 %CI: 86.2–96.8) and 97.8 % specificity (95 %CI: 93.7–99.5). When samples were grouped according to the recorded Ct values the BERA biosensor displayed 100.00 % sensitivity (95 %CI: 84.6–100.0) for Ct values <20−30. For the aforementioned Ct values the Positive Predictive Value (PPV) of the method was estimated at 31.4 % for COVID-19 prevalence of 1% and at 70.5 % for 5% prevalence. At the same time the Negative Predictive Value (NPV) of the BERA biosensor was at 100.0 % for both prevalence rates.

Conclusions

EMBIO DIAGNOSTICS BERA for the detection of SARS-CoV-2 infection has the potential to allow rapid and cost-effective detection and subsequent isolation of confirmed cases, and therefore reduce household and community transmissions.

A remote management system for control and surveillance of echinococcosis: design and implementation based on internet of things

Background

As a neglected cross-species parasitic disease transmitted between canines and livestock, echinococcosis remains a global public health concern with a heavy disease burden. In China, especially in the epidemic pastoral communities on the Qinghai-Tibet Plateau, the harsh climate, low socio-economic status, poor overall hygiene, and remote and insufficient access to all owned dogs exacerbate the difficulty in implementing the ambitious control programme for echinococcosis. We aimed to design and implement a remote management system (RMS) based on internet of things (IoT) for control and surveillance of echinococcosis by combining deworming devices to realise long-distance smart deworming control, smooth statistical analysis and result display. New methods and tools are urgently needed to increase the deworming coverage and frequency, promote real-time scientific surveillance, and prevent transmission of echinococcosis in remoted transmission areas.

Methods

From 2016 to 2019, we had cooperated and developed the smart collar and smart feeder with the Central Research Institute of Shanghai Electric Group Co., Ltd. (Shanghai, China) and Shenzhen Jizhi Future Technology Co., Ltd. (Shenzhen, China). From September 2019 to March 2020, We had proposed the RMS based on IoT as a novel tool to control smart deworming devices to deliver efficient praziquantel (PZQ) baits to dogs regularly and automatically and also as a smart digital management platform to monitor, analyse, and display the epidemic trends of echinococcosis dynamically, in real time in Hezuo City, Gannan Tibetan Autonomous Prefecture, Gansu Province, China. Starting from January 2018, The RMS has been maintained and upgraded by Shanghai Yier Information Technology Co., Ltd (Shanghai, China). The database was based on MySQL tools and the Chi-square test was used to probe the difference and changes of variables in different groups.

Results

The smart collars are fully capable of anti-collision, waterproof, and cold-proof performance, and the battery’s energy is sufficient, the anti-collision rate, water-proof rate, cold-proof rate and voltage normal rate is 99.6% (521/523), 100.0% (523/523), 100.0% (523/523) and 100.0% (523/523), respectively. The RMS can accurately analyse the monitoring data and parameters including positive rates of canine faeces, and the prevalence of echinococcosis in the general population livestock, and children. The data of dogs deworming and surveillance for echinococcosis is able to be controlled using RMS and has expanded gradually in townships to the whole Hezuo region. The automatic delivering PZQ rate, collar positioning rate, deliver PZQ reminding rate, and fault report rate is 91.1% (1914/2102), 92.1% (13 580/14 745), 92.1% (1936/2102) and 84.7% (1287/1519), respectively. After using the RMS from 2019, the missing rate of monitoring data decreased from 32.1% (9/28) to 0 (0/16). A total of 48 administrators (3, 3, 8, 11, 23 at the provincial, municipal, county, township, village levels, respectively) participated in the questionnaire survey, with 93.8% of its overall satisfaction rate.

Conclusions

The existing difficulties and challenges in the way of prevention and control for echinococcosis can partially be resolved using the innovative, IoT-based technologies and tools. The proposed RMS advance the upgrade of existing manual prevention and control models for echinococcosis, especially in the current ongoing COVID-19 pandemic, as social distance and community blockade continue.

Graphic abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s40249-021-00833-4.

Fuzzy Cloud Based COVID-19 Diagnosis Assistant for identifying affected cases globally using MCDM

The COVID-19, Coronavirus Disease 2019, emerged as a hazardous disease that led to many causalities across the world. Early detection of COVID-19 in patients and proper treatment along with awareness can help to contain COVID-19. Proposed Fuzzy Cloud-Based (FCB) COVID-19 Diagnosis Assistant aims to identify the patients as confirmed, suspects, or suspicious of COVID-19. It categorized the patients into four categories as mild, moderate, severe, or critical. As patients register themselves online on the FCB COVID-19 DA in real-time, it creates the database for the same. This database helps to improve diagnostic accuracy as it contains the latest updates from real-world cases data. A team of doctors, experts, consultants are integrated with the FCB COVID-19 DA for better consultation and prevention. The ultimate aim of this proposed theory of FCB COVID-19 DA is to take control of COVID-19 pandemic and de-accelerate its rate of transmission among the society.

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.

Electrochemical SARS-CoV-2 Sensing at Point-of-Care and Artificial Intelligence for Intelligent COVID-19 Management

To manage the COVID-19 pandemic, development of rapid, selective, sensitive diagnostic systems for early stage β-coronavirus severe acute respiratory syndrome (SARS-CoV-2) virus protein detection is emerging as a necessary response to generate the bioinformatics needed for efficient smart diagnostics, optimization of therapy, and investigation of therapies of higher efficacy. The urgent need for such diagnostic systems is recommended by experts in order to achieve the mass and targeted SARS-CoV-2 detection required to manage the COVID-19 pandemic through the understanding of infection progression and timely therapy decisions. To achieve these tasks, there is a scope for developing smart sensors to rapidly and selectively detect SARS-CoV-2 protein at the picomolar level. COVID-19 infection, due to human-to-human transmission, demands diagnostics at the point-of-care (POC) without the need of experienced labor and sophisticated laboratories. Keeping the above-mentioned considerations, we propose to explore the compartmentalization approach by designing and developing nanoenabled miniaturized electrochemical biosensors to detect SARS-CoV-2 virus at the site of the epidemic as the best way to manage the pandemic. Such COVID-19 diagnostics approach based on a POC sensing technology can be interfaced with the Internet of things and artificial intelligence (AI) techniques (such as machine learning and deep learning for diagnostics) for investigating useful informatics via data storage, sharing, and analytics. Keeping COVID-19 management related challenges and aspects under consideration, our work in this review presents a collective approach involving electrochemical SARS-CoV-2 biosensing supported by AI to generate the bioinformatics needed for early stage COVID-19 diagnosis, correlation of viral load with pathogenesis, understanding of pandemic progression, therapy optimization, POC diagnostics, and diseases management in a personalized manner.

Information technology in emergency management of COVID-19 outbreak

Emergency management of the emerging infectious disease outbreak is critical for public health threats. Currently, control of the COVID-19 outbreak is an international concern and has become a crucial challenge in many countries. This article reviews significant information technologyIT) applications in emergency management of COVID-19 by considering the prevention/mitigation, preparedness, response, and recovery phases of the crisis. This review was conducted using MEDLINE PubMed), Embase, IEEE, and Google Scholar. Expert opinions were collected to show existence gaps, useful technologies for each phase of emergency management, and future direction. Results indicated that various IT-based systems such as surveillance systems, artificial intelligence, computational methods, Internet of things, remote sensing sensor, online service, and GIS geographic information system) could have different outbreak management applications, especially in response phases. Information technology was applied in several aspects, such as increasing the accuracy of diagnosis, early detection, ensuring healthcare providers' safety, decreasing workload, saving time and cost, and drug discovery. We categorized these applications into four core topics, including diagnosis and prediction, treatment, protection, and management goals, which were confirmed by five experts. Without applying IT, the control and management of the crisis could be difficult on a large scale. For reducing and improving the hazard effect of disaster situations, the role of IT is inevitable. In addition to the response phase, communities should be considered to use IT capabilities in prevention, preparedness, and recovery phases. It is expected that IT will have an influential role in the recovery phase of COVID-19. Providing IT infrastructure and financial support by the governments should be more considered in facilitating IT capabilities.

A case-study to examine doctors’ intentions to use IoT healthcare devices in Iraq during COVID-19 pandemic

Purpose

Several countries have been using internet of things (IoT) devices in the healthcare sector to combat COVID-19. Therefore, this study aims to examine the doctors’ intentions to use IoT healthcare devices in Iraq during the COVID-19 pandemic.

Design/methodology/approach

This study proposed a model based on the integration of the innovation diffusion theory (IDT). This included compatibility, trialability and image and a set of exogenous factors such as computer self-efficacy, privacy and cost into the technology acceptance model comprising perceived ease of use, perceived usefulness, attitude and behavioral intention to use.

Findings

The findings revealed that compatibility and image of the IDT factors, have a significant impact on the perceived ease of use, perceived usefulness and behavioral intention, but trialability has a significant impact on perceived ease of use, perceived usefulness and insignificant impact on behavioral intention. Additionally, external factors such as privacy and cost significantly impacted doctors’ behavioral intention to use. Moreover, doctors’ computer self-efficacy significantly influenced the perceived ease of use, perceived usefulness and behavioral intention to use. Furthermore, perceived ease of use has a significant impact on perceived usefulness and attitude, perceived usefulness has a significant impact on attitude, which, in turn, significantly impacting doctors' behavior toward an intention to use.

Research limitations/implications

The limitations of the present study are the retractions of the number of participants and the lack of qualitative methods.

Originality/value

The finding of this study could benefit researchers, doctors and policymakers in the adaption of IoT technologies in the health sectors, especially in developing counties.

Chinese experts’ consensus on the Internet of Things-aided diagnosis and treatment of coronavirus disease 2019 (COVID-19)

The aim is to diagnose COVID-19 earlier and to improve its treatment by applying medical technology, the “COVID-19 Intelligent Diagnosis and Treatment Assistant Program (nCapp)” based on the Internet of Things. Terminal eight functions can be implemented in real-time online communication with the “cloud” through the page selection key. According to existing data, questionnaires, and check results, the diagnosis is automatically generated as confirmed, suspected, or suspicious of 2019 novel coronavirus (2019-nCoV) infection. It classifies patients into mild, moderate, severe or critical pneumonia. nCapp can also establish an online COVID-19 real-time update database, and it updates the model of diagnosis in real time based on the latest real-world case data to improve diagnostic accuracy. Additionally, nCapp can guide treatment. Front-line physicians, experts, and managers are linked to perform consultation and prevention. nCapp also contributes to the long-term follow-up of patients with COVID-19. The ultimate goal is to enable different levels of COVID-19 diagnosis and treatment among different doctors from different hospitals to upgrade to the national and international through the intelligent assistance of the nCapp system. In this way, we can block disease transmission, avoid physician infection, and epidemic prevention and control as soon as possible.