Smart Health Technologies for the COVID-19 Pandemic: Internet of medical things perspectives
2: Senac Faculty of Ceará, Brazil
Smart Health Technologies for the COVID-19 Pandemic: Internet of medical things perspectives looks at the role technology has played to monitor, map and fight the global COVID-19 pandemic. Chapters outline risk assessment methodologies and social distancing and infection control technologies in the face of this disease outbreak.
The applications of Big Data and artificial intelligence in the fight against the spread of COVID-19 are explored in this edited book, as well as advances in early diagnostic testing and remote monitoring systems, and blockchain-based solutions for secure data handling. The implementation of machine learning for reviewing and analysing biomedical data and assisting with drug design is also discussed.
Emphasising the vital role that intelligent advanced healthcare informatics has played during this crucial time, this book is a valuable resource for researchers in the fields of biomedical engineering, bioengineering, electronics engineering, health informatics, wireless body area networks (WBAN), data analytics, telemedicine, and those in related fields.
Inspec keywords: diseases; neural nets; Internet of Things; medical image processing; computerised tomography; microorganisms; epidemics; edge detection; learning (artificial intelligence); molecular biophysics
Other keywords: microorganisms; computerised tomography; patient diagnosis; edge detection; neural nets; learning (artificial intelligence); Internet of medical things; image segmentation; COVID-19 pandemics; image recognition; molecular biophysics; diseases; smart health technology
Subjects: Computer vision and image processing techniques; Biomedical measurement and imaging; Molecular biophysics; Textbooks; Neural nets; Mobile, ubiquitous and pervasive computing; Medical and biomedical uses of fields, radiations, and radioactivity; health physics; Optical, image and video signal processing; Biomedical engineering; Biology and medical computing
- Book DOI: 10.1049/PBHE042E
- Chapter DOI: 10.1049/PBHE042E
- ISBN: 9781839535185
- e-ISBN: 9781839535192
- Page count: 478
- Format: PDF
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Front Matter
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1 Internet of Things (IoT) and blockchain-based solutions to confront COVID-19 pandemic
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COVID-19 pandemic, an unprecedented event that has severely affected every aspect of human civilization. From the beginning of the pandemic, the contagious nature of this virus resulted in its rapid transmission throughout the world. As a result, worldwide health organizations and governments are facing tremendous pressure to deal with the affected populations. In this demanding period, the applications of the latest technologies to prevent the spread of the virus are critical. Among various technologies, the Internet of Things (IoT) and blockchain are being used in several solutions starting from contact tracing to forecasting. The use of IoT technologies has proved to be highly effective in such a state of the pandemic. Conducting real-time health monitoring on patients or suspected cases of COVID-19, tracking medications, detecting any new suspected cases, diagnosing patients from a distance, etc. have become exclusively possible with the use of IoT technologies in this COVID-19 pandemic. On the other hand, the blockchain technology that became popular with the increase of different cryptocurrencies has seen its applications almost everywhere. The technology uses a decentralized system instead of a single point of contact, proving to be more secure than existing solutions. Blockchain-based solutions are also being used during the ongoing pandemic in various aspects for secure contact tracing, data handling, and preventing data fabrication. In this chapter, IoT and blockchain technology are discussed briefly while describing their core elements. Then, the latest solutions were presented based on these technologies in different aspects of COVID-19 pandemic prevention. These solutions mainly focus on using these technologies for remote patient monitoring, secure data handling, and telemedicine. Finally, challenges of using these technologies were discussed, and possible solutions were recommended to improve their efficacy in the future.
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2 Application of big data and computational intelligence in fighting COVID-19 epidemic
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The current epidemic called coronavirus (COVID-19) is wreaking havoc on society, humanity, and causing economic difficulties around the world. Many techniques have been attempted to manage and contain the COVID-19 outbreak; however, many governments remain powerless to combat and contain the virus. Big data is driving the digital revolution in an increasingly knowledge-driven, healthcare-innovation-driven, and connected society. The combination of computational intelligence (CI) and big data analytics (BDAs) has developed methods that make accessing and processing vast amounts of data easier and less demanding on human expert. Hence, in combating the outbreak, big data and CI can be applied since the use of both technologies empowered BDA and yielded imaginable results in combating infectious diseases globally. Therefore, this chapter reviews the applicability and importance of big data and CI methods to data produced from the countless of ubiquitously connected healthcare devices that produced entrenched and distributed information handling capabilities in fighting COVID-19 outbreak. The use of CI in BDAs has resulted in knowledge-based system that transformed big data into big knowledge with new approaches and visions in order to provide people with better understanding and information-driven results. There have been tremendous positive results using IoT-based capture data with BDA and CI models for monitoring, diagnosis, and prediction of COVID-19 outbreak. The huge amount of data can be managed using CI and BDA by developing models that will reduce the spread of any infectious diseases to monitoring, tracking and in the production of drugs and vaccines for the treatment of the any outbreak globally.
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3 Cloud-based IoMT for early COVID-19 diagnosis and monitoring
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COVID-19 has been posing a threat to survival from the second half of 2019. Each country in the world must fight against the COVID-19 pandemic with caution. Many researchers around the world have developed many vaccines, but some of them are found to be effective. This chapter introduces an effective scheme for the diagnosis and tracking of patients with COVID-19 based on symptoms. The concept behind the proposed approach is to use an Internet of Things (IoT)-based system to handle the real-time symptom data from patients in order to diagnose coronavirus cases early. Additionally, the framework has the ability to track the medical records of those who have healed from the COVID-19 disease. The proposed framework must learn automatically about the origin of the virus by monitoring and analyzing necessary data. Because of the significant and rapid rise in the number of patients after the COVID-19 pandemic, it is crucial to focus an eye on patients' health before any new disease or infection occurs. IoT security has recently become a serious concern and a hard problem. For researchers, transferring the large amount of collected healthcare information data of patients who do not want their personal healthcare details shared has remained a difficult task. The health status of patients is determined in this model by predicting critical situations and examining physiological data received from smart medical IoT devices, ensuring that patients' personal information is kept private. Based on current advancements, the suggested model is thought to be suitable for delivering an appropriate remote patient monitoring model with accurate data in cloud-based IoT systems.
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4 Assessment analysis of COVID-19 on the global economics and trades
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The worldwide epidemic known as COVID-19 is being labeled a worldwide pandemic on the earlier of 2020, the globe has still not recovered. Cases rapidly spread from China; leading world governments to take extensive infection control measures to several cases and restrict the virus's global transmission rate. However, these controls have undermined the contemporary world economy's main supporting pillars of global commerce and collaboration. In light of the epidemic's context, this chapter critically evaluates the inventory of the pandemic's bad and positive influences in different sections. This advocates for a complete overhaul of the global economic development paradigm based on a linear economy system that leverages profiteering and energy-guzzling industrial processes.
Due to the worldwide breakout of the pandemic COVID-19, the world's political, social, economic, religious, and financial systems have all been thrown into total disarray. As of April 2020, an estimated 4.7 million individuals have been tested, and the illness has resulted in a confirmed infection count of around 2.7 million individuals, with 182,740 deaths attributed to the virus. More than 80 nations have forced companies to shut, locked borders to nations in transition, quarantined their people, and shuttered schools for around 1.5 billion school-age children. A total global economic collapse is inevitable because of the world's largest economies of different countries. The global financial markets have been battered, and tax income sources have crashed into a bottomless pit even more worrisome. Infection is substantially affecting global economic growth. It is anticipated that if the present growth rate continues, the virus might outpace world economic growth by almost 2.0% per month. If the global economic slump is deep and extensive, global commerce might decline from 13% to 32%. It will be years before the full impact of the outbreak is revealed. It looks into the correlation between COVID-19 and the development of the national economy and the stock market in order to prove how well the COVID-19 economic growth prediction is linked with the gross domestic product. The utilization of publicly accessible data, as found on Yahoo Finance, the International Monetary Fund, John Hopkins COVID-19 map, and regression models was used to carry out the goal of this study. COVID-19 is used to measure the economic effect, and the stock market serves as a proxy for economic variability to test whether or not the forecast is reliable. In the aim that the model can make predictions about two quarters out, it is supposed to provide explanations for changes in the quarter ahead. This study will aid those with government-level decision-making, business-stage strategic thinking, and capital-market investment to better comprehend the current state of affairs and use the model for forecasting.
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5 Early diagnosis and remote monitoring using cloud-based IoMT for COVID-19
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The entire globe has been battling with deadly coronavirus disease 2019 (COVID-19) pandemic from the time December 2019. Around 190 million people have been affected by the virus, and 4 million have lost their lives to it. It has adversely influenced the socio-economical lives of people in almost all countries across the world. Hence, it is essential to detect the disease at an early stage and ensure that the transmission of the virus is curbed, in turn, saving the lives of many other people. With the advancements and developments in the information technology field, it is possible to diagnose infectious diseases like the current COVID-19 pandemic at an early stage and give proper treatment to the infected. In addition to analyzing the disease early, many other approaches are employed to deal with this deadly disease. In this chapter, various Internets of Things that are being used to track the patients' health and provide them the necessary care and treatment even in remote locations have been discussed. Also, machine learning and deep learning for early diagnosis and remote monitoring have been discussed. An experimental case study using COVIDX dataset has been discussed along with the results. Comprehensive experiments have been carried out with varying computed tomography (CT) sizes of CT images and an average accuracy of above 80% has been achieved. In all, how the use of technology in the medical field proves beneficial and how it can be leveraged even further to control the spread of the diseases has been elucidated in this chapter.
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6 Blockchain technology for secure COVID-19 pandemic data handling
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In recent times, COVID-19 pandemic data collected via the Internet of Medical Things-enabled channels can be transmitted seamlessly and processed remotely by medical practitioners globally. However, the confidentiality and integrity of these channels pose serious security concerns. The transmission of medical information, which comprises highly sensitive personal data, must meet stringent security requirements. In order to achieve this, blockchain has been deployed to enhance the security, confidentially, trust, and integrity of sensitive data being transmitted over insecure channels as a result of its cryptographic characteristics. Blockchain is regarded as a protected and distributed structure of data, which enhances security, simplifies the tracking, and analyses the stored medical information independently. Additionally, blockchain allows the transmission and exchange of messages between two parties in a network configuration, independent of sole trusted authority. Blockchain technology is verifiably safe against an attacker who mismanages the scheme and compromises the central controller. Furthermore, blockchain technology has been deployed to handle distributed medical data in standard medical laboratories. The decentralized and immutability features of blockchain have facilitated the rapid development of the beyond 5G wireless services for health-related data sharing and processing. However, the adoption of blockchain technology in medical data handling is still in its infancy, and the need for a rigorous study in this domain cannot be overemphasized. Toward this end, this chapter first highlights how blockchain technology has developed in recent times and how it is applied in the medical data-handling space. Further to this, the chapter examines the potential benefits, key challenges, and prospects in blockchain technology. Additionally, the chapter highlights research efforts in blockchain in healthcare, especially as it relates to the dreaded COVID-19 pandemic. The chapter also discusses the application of blockchain technology in healthcare data-handling practices that can build trust with automated tracking of integrity and responsible credential verification. Finally, practical COVID-19 pandemic data were analyzed and presented to motivate the chapter further.
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7 Social distancing technologies for COVID-19
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Because of the COVID-19 scenario, the viral infection rate in Thailand has skyrocketed as people commute to work and go about their everyday lives. Social divides have a role in illness prevention. As a result, Thailand is focusing on adopting different technologies to assist us in managing the gap and support everyone's ability to work. Adopting this technology has resulted in it being the New Normal in use as a substitute for older systems. It also plays a role in instilling everyone a sense of social responsibility and caution to avoid near individuals from becoming infected; it has become an accepted part of daily life. This chapter offers a template analysis of stakeholder interviews conducted with the aid of technology to accomplish social distancing, which is divided into three categories: educational, public health, and social distancing, as well as manufacturing employees. A software usage pattern has been developed due to data analysis utilizing the content analysis to help create social distancing for job planning and everyday use. It can also be used to plan future smart cities.
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8 Social health protection in touristic destinations during COVID-19
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COVID-19 pandemic has caused huge economic losses in almost all industries. Thus, the economic crisis caused by this pandemic has modeled huge challenges for leisure industry. Travel bans, closed borders, mandatory quarantine after border crossing, reduced contacts are just some of the problems that citizens around the world have encountered. Observed from the point of view of tourism, a special problem was curfew and quarantine closure of cities and areas. All these significantly disrupted global tourism, which have been in full upswing in the years before pandemic. The research described in this chapter aims to give a description of the proposed software solution intended for better control and records of infected persons. The straightforward use of the proposed software solution aims to reduce the potential for the virus to spread among tourists. In order to reduce the potential for the spread of the virus, the proposed software solution needs to enable a better control of the movement of infected persons. Proper control of the movement of infected persons reduces the pressure on the tourism sector and opens the possibility of reoperation of tourist facilities for persons who are not infected. The proposed system is based on mutual cooperation of several actors. Thus, at the local and state level, the healthcare system can be singled out. Actors acting only at the local level are tourism organizations and local governments, while the actor acting only at the state level is border control. When it comes to the process of collecting personal data of patients, the bearer of this task within the system is the local health organization, which is certainly the first in contact with infected patients. In this way, the role of the healthcare organization is the proper registration of infected patients, and their entry into the proposed system. This part of the system also provides data on vaccinated and revaccinated patients. In order not to further burden the healthcare system, proposed software solution will use existing data and will not require additional tests. Based on provided data from all actors, the integrated management system will create a database with vaccinated or tested positive persons. This database will be obtained by merging several databases into one so-called centralized register. However, each of the actors in the system has the right to use only that part of the data that is essential for their work. In this way, the system is able to protect personal patient data from unauthorized access. The system will allow one to check vaccination or home isolation status of an individual and, based on the results, approve or deny reservations. The edge/cloud architecture at the same time will provide data availability, and data access control.
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9 Analysis of Artificial Intelligence and Internet of Things in biomedical imaging and sequential data for COVID-19
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Biomedical data analysis is an exceedingly broad field. It includes array data analysis, biomedical image analysis, integrated or hybrid data analysis, and patient data analysis using machine learning (ML) and artificial intelligence (AI). Array data analysis can be further classified into RNA-sequence, single-cell RNA-sequence, microarray, and ChiP-seq data analysis. Biomedical imaging encompasses different parameters like gathering of biomedical signal, formation of an image, processing of an image, display of image, and the medical diagnosis that are built on the features obtained from various images. AI was mainly used to break down healthcare data and used to track and screen patients while Internet of Things was used mainly for monitoring a patient remotely. There are different radiological imaging processes that include the radiography, ultrasound, thermography, magnetic resonance imaging, nuclear medicine and computed tomography. We, in this book chapter, provide a comprehensive survey (road map) on various array-based sequence data analyses and biomedical imaging along with their integrated studies for different tissue-specific dreadful diseases (such as cancer). We included the integrated studies of biomedical imaging and array-based data analysis for the same set of patients (samples) that covered the problem of combinatorial gene signature detection as well as disease subtype image classifications while specific multimodal data from well-known data repository (e.g., TCGA, ICGC) had been provided. Finally, our book chapter covers the maximum area of biomedical imaging as well as array-based sequence data analysis along with the contribution of AI and ML in order to build a smart healthcare system, and provide a new dimension to the interested biomedical researchers.
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10 Review of medical imaging with machine learning and deep learning-based approaches for COVID-19
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COVID-19 came with unprecedented challenges that disrupted activities of government, economies, and societies all around the world. It is caused due to severe acute respiratory syndrome coronavirus 2. We are witnessing how governments and administrations have taken proactive steps to limit the spread of disease. Even though many countries have enforced strict lockdown to curtail its transmission, mortality rates continue to rise. An early detection of COVID-19 is inevitable as it helps in seeking early medical intervention and to minimize community spread. COVID-19 can be diagnosed with the help of gold standard reverse transcription polymerase chain reaction (RT-PCR) tests, quick antigen and antibody tests supplemented with computerized tomography (CT) scan and X-ray imaging. Considering the increase in the number of cases worldwide, there is a need for economical and viable ways to detect COVID-19. RT-PCR tests continue to be dominant; however, there are few concerns related to the sensitivity associated with the test results. Thus, medical community underlines the importance of medical imaging tools like CT scans and chest X-rays (CXRs) and the abnormalities revealed in CXRs and CT scan can help in detecting COVID-19.
Medical imaging and analytic tools that use machine learning and deep learning algorithms can enhance the diagnosis and prediction of COVID-19. There are many effective and proven image recognition techniques in deep learning like convolutional neural networks and transfer learning that can be used to design promising applications for COVID-19 detection. These models can be enhanced using image segmentation and edge detection techniques. In the proposed book chapter, we have reviewed the impact of the COVID-19 pandemic on the global community, the need for reliable, quick, and economical ways to detect it. We have surveyed the existing mechanisms for COVID-19 detection using machine learning and deep learning algorithms and presented a critical review of the major shortcomings in existing mechanism that could open further research in this area.
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11 Machine-based drug design to inhibit SARS-CoV-2 virus
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Researchers worldwide are striving hard to design the best anti-coronavirus drug to overcome the current pandemic situation. Due to the lack of animal and human trials data, the process is still complicating and causing more death worldwide. Since biological testing costs more money and time-consuming, the combination of computerized programs-based evaluation like molecular docking, virtual screening (AutoDock, HEX, Schrodinger, Gaussian, and Glide), and molecular dynamics study serves as a hopeful way in drug developing and studying their effect over severe acute respiratory syndrome (SARS)-coronavirus-causing components like spike (S) protein present around the SARS-coronavirus-2 (SARS-CoV-2), main protease (M-pro), and ribonucleic acid cooperate. The keen analysis of the amino acid sequences in the coronavirus-19 infection-causing proteins will give very important information about the virus transformation and replication cycle. The amino acid sequences and their active sites provided in the 3D crystalline structure of M-pro (PDB ID; 6LU7) afford valuable data to the researchers about the type of inhibitors that corresponds to the SARS-CoV-2 inhibition. Compared to all other deadly viruses like flu, human immunodeficiency virus, and SARS, novel coronavirus SARS-CoV-2 shows superior binding affinity over a human transmembrane protein christened angiotensin-converting enzyme 2, found in human lungs. Since the target is very clear, every scientist aims to design a new drug or check the available prodrug activity through a computer program to defeat the COVID-19 disease. Nowadays, many synthetic and natural drugs have been tested for their suitability against M-pro. Since the coronavirus-19 infection spreads more vigorously, the traditional real-time PCR test will need more time for infection confirmation, so machine-based imaging studies like MRI, computerized tomography (CT), and X-ray are needed. The MRI and CT need contrast agents (CAs) to give more precise images. The development of image contrast-enhancing agents will give more appropriate outcome image in detecting COVID-19 infection in the early stages. The designing of perfect multimodal CAs is the current research among MRI researchers. It will behave as both targeting and coronavirus-19 killing drug for the current pandemic situation. All the above-said applications can be accomplished only by designing the drug and then studying their binding studies through a computer application.
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12 Stress detection for cognitive rehabilitation in COVID-19 scenario
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Due to the current demand for emerging technologies like the Internet of Things integrated with machine learning in industry and academics, brain-computer interface tools like electroencephalogram in healthcare have drawn worldwide attention. As has been noticed that during recent times, mobile phone exposure to people increased in at least 2-fold way, so games have been used as stimuli for detecting how our brain becomes overburdened with increased exposure. After the data acquisition from 14 channels of an electroencephalogram, the activated regions were identified. Features were extracted from the most activated ten electrode channels using discrete wavelet transform. To reduce the dimensions of the feature space for enhancing the performance, principal component analysis was used. The mental state classification was performed using a support vector machine based on the detected stress. The proposed system has outperformed the existing ones for its effectiveness and efficiency in a broad application area of cognitive rehabilitation. Classification accuracy was obtained as 92.79% and different other metrics proved that the combination of channel selection, feature extraction, and classification methods in our proposed approach has outperformed the others. Privacy is maintained, and it is flexible to the user as per his/her convenient time.
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13 Arduino-based robot for purification of COVID-19 using far UVC light
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The proposed work elaborates the necessity of sterilisation especially during COVID-19 period. In this chapter, we will discuss about the development and construction of an 'Arduino-based robot for the purification of COVID-19 using far UVC light'. Throughout history, the outgrowing technology has solved major problems. Technology has made everything possible, from making knowledge more available to more people and bringing group of people associated, to making our planet cleaner and even saving lives. But the current circumstance of COVID-19 is threat to human lives as there are high chances of getting sick in no time, so in order to fight with this novel virus. There is necessity to keep our surroundings clean and tidy. Cleanliness is the most important factor to keep ourselves away from diseases. The objective of the current work is to discuss various inventions made during the pandemic scenario and to contribute to the battle against COVID-19 propagation, a novel human corona virus in hospitals, public transports, airlines, and any enclosed areas with an approach, which is a basically a bot named 'Arduino-based robot' which uses ultraviolet (UV) sterilisation method to kill and eliminate all the germs and viruses present in the surroundings and sanitises the entire surroundings by cleaning and disinfecting the environment. Far-UVC (ultraviolet C) radiation has a wavelength range of 207-222 nm. The decontaminating efficiency of far UVC light was previously tested by exposing bacteria that were irradiated on a surface or in suspension. We have developed a UV-based sterilisation method that uses single-wavelength far-UVC light produced by filtered exclaims to selectively inactivate microorganisms while causing no biological damage to exposed cells and tissues. The method is based on biophysical principles, as far-UVC light can penetrate and thus inactivate bacteria and viruses with dimensions in micrometre or smaller, while far-UVC light cannot penetrate even the outer dead-cell layers of human skin, nor the outer tear layer of an eye, due to its high absorbance in biological materials. The far-UVC lamp plays a major role in a disinfection robot. This bot will disinfect the virus and clean the areas; it will travel autonomously and sterilise the areas without the need for human intervention. In the discussion section, a demonstration of the bot's implementation and presentation was shown. This robot can also be used as a vacuum cleaner, eliminating the need for and expense of cleaning by humans.
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14 Effect of COVID-19 pandemic on waste management system and infection control
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Late within the year of 2019, the entire world roused by a reality of an epidemic of coronavirus disease (COVID-19). It has become very vital and important to take all possible precaution till the world finds a definite treatment against this disease as there is least information about its behavior and origin. World Health Organization (WHO) informed that proper guidelines need to be followed for COVID-19 waste management as it is extremely infectious and contaminated. Personal protective equipment has become a very vital element to guard from exposure of any infectious materials. It is usually used in healthcare or hospital settings during any outbreak. These enormous challenges are with all stakeholders for avoiding spread through waste. Additionally, to WHO, each country was adopted different safety measures and developed guidelines to control the contamination and manage the waste. The previously mentioned guidelines are very useful for managing the infectious waste and providing protection and security of waste handlers. Not only the spread of COVID-19 is reduced by practicing appropriate technologies to handle wastes, but it generates worth through increasing the recyclability chance of waste. Due to COVID-19, there were lots of social and economic changes happened in the world, which leads to reduction in manufacturing units and various business processes. All these have significantly affected waste management that is a very vivacious step for the health outcomes of the patients as well as caregivers, especially during the COVID-19 pandemic. Among all the issues created by COVID-19, the problem that will create major mess is, not handling biomedical waste properly. If the spreading needs to be controlled, it requires a strict monitoring of the complete cycle starting from the point of generation. However, due to the invaluable service of waste management sector only, it is possible to avoid spread of the COVID-19 and ensure that the weird tons of waste will not be gathered that increases health risks and the spread of disease.
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15 Natural adjunctive therapies options other than COVID-19 antiviral therapies
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The coronavirus disease 2019 (COVID-19) pandemic is the largest health problem worldwide. Unfortunately, the lack of an effective and clear treatment causes it to be a major health problem. There are currently no effective antiviral drugs or vaccines. The symptoms and course of the disease differ individually. Symptoms vary from asymptomatic to intensive care, even death. The individual variation of this symptom pattern is related to viral load, individual's current comorbid conditions, age, gender, and most importantly, immune status. Considering the course differences of the disease in all these individual, familial, and demographic distributions, it suggests that genetic and environmental factors play an important role. There is a systemic inflammatory response in COVID-19. High levels of chemokine and proinflammatory cytokines are detected in patients. Nutrition is one of the most important factors for health. With the support of the immune system, people can be protected from COVID-19 and make the process easy when suffering from disease. Apart from the current treatments, some herbal and natural products are used as adjunctive therapy. Key dietary components such as vitamins C, D, E, zinc, selenium, nonflavonoids, flavonoids, polyphenols, and curcumin have been shown to have immunomodulatory properties, which can help with infectious illnesses. Most of these nutrients also have been demonstrated to be useful in the treatment of COVID-19. The importance of such dietary elements in immunity and their particular consequences in COVID-19 patients are discussed in this review.
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16 Risk assessment and spread of COVID-19
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COVID-19 has brought tremendous changes in everyone's lifestyle. It also brought awareness among us on how analysis and prediction of situations play a crucial role. These kinds of situations and risk assessments are considered critical factors in reducing the seriousness of the situations. Due to a lack of risk assessment, proper preventive measures cannot be taken. Generally, if an epidemic occurs throughout the world and shows its impact on more people, it is declared a pandemic. In a pandemic situation, the greatest weapon one can use to fight against it is risk assessment and taking measures. The word risk assessment refers to the procedure of identifying, evaluating the factors that cause harm to the environment, and living beings. It also involves making decisions on how to put an end to it. For example, if we consider COVID-19 pandemic, to manage the risk of spreading, analysis was done on understanding the situations that increase the risk of transmission of the virus, identifying the majorly affected people in that situation, coming up with some solution to stop this from happening or to control the situation. In COVID-19, the government provided many practical measures such as wearing masks, sanitizing our surroundings repeatedly, and maintaining a physical distance. This book chapter will have clear discussions on the steps taken to assess the risk and stop the spread of pandemics like COVID-19. We believe that prevention is always better than cure. The chapter presents an analysis of prevention using prediction techniques. Using sentiment analysis in machine learning, weighted density ensembles, forecasting models, and risk assessment can be done. Further, a bot named SAUCHA is proposed for automatic sanitization.
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Back Matter
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