Modeling Control And Drug Development For Covid 19 Outbreak Prevention

This book is well-structured book which consists of 31 full chapters. The book chapters' deal with the recent research problems in the areas of modeling, control and drug development, and it presents various techniques of COVID-19 outbreak prevention modeling. The book also concentrates on computational simulations that may help speed up the development of drugs to counter the novel coronavirus responsible for COVID-19. This is an open access book.

This book covers recent research on the COVID-19 pandemic. It includes the analysis, implementation, usage, and proposed ideas and models with architecture to handle the COVID-19 outbreak. Using advanced technologies such as artificial intelligence (AI) and machine learning (ML), techniques for data analysis, this book will be helpful to mitigate exposure and ensure public health. We know prevention is better than cure, so by using several ML techniques, researchers can try to predict the disease in its early stage and develop more effective medications and treatments. Computational technologies in areas like AI, ML, Internet of Things (IoT), and drone technologies underlie a range of applications that can be developed and utilized for this purpose. Because in most cases there is no one solution to stop the spreading of pandemic diseases, and the integration of several tools and tactics are needed. Many successful applications of AI, ML, IoT, and drone technologies already exist, including systems that analyze past data to predict and conclude some useful information for controlling the spread of COVID-19 infections using minimum resources. The AI and ML approach can be helpful to design different models to give a predictive solution for mitigating infection and preventing larger outbreaks. This book: Examines the use of artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), and drone technologies as a helpful predictive solution for controlling infection of COVID-19 Covers recent research related to the COVID-19 pandemic and includes the analysis, implementation, usage, and proposed ideas and models with architecture to handle a pandemic outbreak Examines the performance, implementation, architecture, and techniques of different analytical and statistical models related to COVID-19 Includes different case studies on COVID-19 Dr. Chhabi Rani Panigrahi is Assistant Professor in the Department of Computer Science at Rama Devi Women’s University, Bhubaneswar, India. Dr. Bibudhendu Pati is Associate Professor and Head of the Department of Computer Science at Rama Devi Women’s University, Bhubaneswar, India. Dr. Mamata Rath is Assistant Professor in the School of Management (Information Technology) at Birla Global University, Bhubaneswar, India. Prof. Rajkumar Buyya is a Redmond Barry Distinguished Professor and Director of the Cloud Computing and Distributed Systems (CLOUDS) Laboratory at the University of Melbourne, Australia.

Disasters such as earthquakes, cyclones, floods, heat waves, nuclear accidents, and large scale pollution incidents take lives and cause exceptionally large health problems. The majority of large-scale disasters affect the most vulnerable populations, which are often comprised of people of extreme ages, in remote living areas, with endemic poverty, and with low literacy. Health-related emergency disaster risk management (Health-EDRM) [1] refers to the systematic analysis and management of health risks surrounding emergencies and disasters; it plays an important role in reducing hazards and vulnerability along with extending preparedness, response, and recovery measures. This concept encompasses risk analyses and interventions, such as accessible early warning systems, timely deployment of relief workers, and the provision of suitable drugs and medical equipment, to decrease the impact of disaster on people before, during, and after disaster events. Disaster risk profiling and interventions can be at the personal/household, community, and system/political levels; they can be targeted at specific health risks including respiratory issues caused by indoor burning, re-emergence of infectious disease due to low vaccination coverage, and gastrointestinal problems resulting from unregulated waste management. Unfortunately, there has been a major gap in the scientific literature regarding Health-EDRM. The aim of this Special Issue of IJERPH was to present papers describing/reporting the latest disaster and health risk analyses, as well as interventions for health-related disaster risk management, in an effort to address this gap and facilitate major global policies and initiatives for disaster risk reduction.

This book presents a compilation of the most recent implementation of artificial intelligence methods for solving different problems generated by the COVID-19. The problems addressed came from different fields and not only from medicine. The information contained in the book explores different areas of machine and deep learning, advanced image processing, computational intelligence, IoT, robotics and automation, optimization, mathematical modeling, neural networks, information technology, big data, data processing, data mining, and likewise. Moreover, the chapters include the theory and methodologies used to provide an overview of applying these tools to the useful contribution to help to face the emerging disaster. The book is primarily intended for researchers, decision makers, practitioners, and readers interested in these subject matters. The book is useful also as rich case studies and project proposals for postgraduate courses in those specializations.

As the culminating volume in the DCP3 series, volume 9 will provide an overview of DCP3 findings and methods, a summary of messages and substantive lessons to be taken from DCP3, and a further discussion of cross-cutting and synthesizing topics across the first eight volumes. The introductory chapters (1-3) in this volume take as their starting point the elements of the Essential Packages presented in the overview chapters of each volume. First, the chapter on intersectoral policy priorities for health includes fiscal and intersectoral policies and assembles a subset of the population policies and applies strict criteria for a low-income setting in order to propose a "highest-priority" essential package. Second, the chapter on packages of care and delivery platforms for universal health coverage (UHC) includes health sector interventions, primarily clinical and public health services, and uses the same approach to propose a highest priority package of interventions and policies that meet similar criteria, provides cost estimates, and describes a pathway to UHC.

Biomedical Innovations to Combat COVID-19 provides an updated overview on the development of vaccines, antiviral drugs and nanomaterials, and diagnostic methods for the fight against COVID-19. Perspectives on such technologies are identified, discussed, and enriched with figures for easy understanding and applicability. Furthermore, it contains basic aspects of virology, immunology, and antiviral drugs that are needed to fully appreciate these innovations. This book is split into four sections: introduction, presenting basic virologic and epidemiological aspects of COVID-19; vaccines against COVID-19, discussing their different types and applications used to develop them; diagnostic approaches for SARS-CoV-2, encompassing advanced sensing and microfluidic-based biosensors; and drug development and delivery, where antivirals based on nanomaterials or drugs are presented. It is a valuable source for virologists, biotechnologists, and members of biomedical field interested in learning more about how novel technologies can be applied to fasten the eradication of the COVID-19 and similar pandemics. Presents updated literature coverage summarizing the most relevant information on COVID-19 Written by experts from diverse scientific domains in order to provide readers with a thorough view on the subject Encompasses tables, figures and information trees especially developed for the book in order to condense and highlight key points for quick reference

This compendium represents a set of guides to understanding the challenging scientific, epidemiological, clinical, social, and economic phenomenon that is represented by the COVID-19 pandemic. The book explains the mathematical modeling of COVID-19 infection, with emphasis on traditional epidemiological principles. It represents a rigorous, comprehensive and multidisciplinary approach to a complex phenomenon. The chapters take into account the knowledge arising from different disciplines (epidemiology, pathophysiology, immunology, medicine, biology, vaccine development, etc.). It also covers COVID-19 data analysis, giving the reader a perspective of statistics and data science, and includes a discussion about social and economic issues of the pandemic. Each chapter is devoted to a specific topic, and is contributed by experts in epidemiology. Because of its multidisciplinary nature, this book is intended as a reference on mathematical models and basic immunotherapy for COVID-19 for a broad community of readers, from scholars who have scientific training, to general readers who have an interest in the disease.

This book introduces the most recent research and innovative developments regarding the new strains of COVID-19. While medical and natural sciences have been working instantly on deriving solutions and trying to protect humankind against such virus types, there is also a great focus on technological developments for improving the mechanism – momentum of science for effective and efficient solutions. At this point, computational intelligence is the most powerful tools for researchers to fight against COVID-19. Thanks to instant data-analyze and predictive techniques by computational intelligence, it is possible to get positive results and introduce revolutionary solutions against related medical diseases. By running capabilities – resources for rising the computational intelligence, technological fields like Artificial Intelligence (with Machine / Deep Learning), Data Mining, Applied Mathematics are essential components for processing data, recognizing patterns, modelling new techniques and improving the advantages of the computational intelligence more. Nowadays, there is a great interest in the application potentials of computational intelligence to be an effective approach for taking humankind more step away, after COVID-19 and before pandemics similar to the COVID-19 many appear.