Computational Modelling And Imaging For Sars Cov 2 And Covid 19

The aim of this book is to present new computational techniques and methodologies for the analysis of the clinical, epidemiological and public health aspects of SARS-CoV-2 and COVID-19 pandemic. The book presents the use of soft computing techniques such as machine learning algorithms for analysis of the epidemiological aspects of the SARS-CoV-2. This book clearly explains novel computational image processing algorithms for the detection of COVID-19 lesions in lung CT and X-ray images. It explores various computational methods for computerized analysis of the SARS-CoV-2 infection including severity assessment. The book provides a detailed description of the algorithms which can potentially aid in mass screening of SARS-CoV-2 infected cases. Finally the book also explains the conventional epidemiological models and machine learning techniques for the prediction of the course of the COVID-19 epidemic. It also provides real life examples through case studies. The book is intended for biomedical engineers, mathematicians, postgraduate students; researchers; medical scientists working on identifying and tracking infectious diseases.

This innovative textbook brings together modern concepts in mathematical epidemiology, computational modeling, physics-based simulation, data science, and machine learning to understand one of the most significant problems of our current time, the outbreak dynamics and outbreak control of COVID-19. It teaches the relevant tools to model and simulate nonlinear dynamic systems in view of a global pandemic that is acutely relevant to human health. If you are a student, educator, basic scientist, or medical researcher in the natural or social sciences, or someone passionate about big data and human health: This book is for you! It serves as a textbook for undergraduates and graduate students, and a monograph for researchers and scientists. It can be used in the mathematical life sciences suitable for courses in applied mathematics, biomedical engineering, biostatistics, computer science, data science, epidemiology, health sciences, machine learning, mathematical biology, numerical methods, and probabilistic programming. This book is a personal reflection on the role of data-driven modeling during the COVID-19 pandemic, motivated by the curiosity to understand it.

Intelligent Modeling, Prediction, and Diagnosis from Epidemiological Data: COVID-19 and Beyond is a handy treatise to elicit and elaborate possible intelligent mechanisms for modeling, prediction, diagnosis, and early detection of diseases arising from outbreaks of different epidemics with special reference to COVID-19. Starting with a formal introduction of the human immune systems, this book focuses on the epidemiological aspects with due cognizance to modeling, prevention, and diagnosis of epidemics. In addition, it also deals with evolving decisions on post-pandemic socio-economic structure. The book offers a comprehensive coverage of the most essential topics, including: A general overview of pandemics and their outbreak behavior A detailed overview of CI techniques Intelligent modeling, prediction, and diagnostic measures for pandemics Prognostic models Post-pandemic socio-economic structure The accompanying case studies are based on available real-world data sets. While other books may deal with this COVID-19 pandemic, none features topics covering the human immune system as well as influences on the environmental disorder due to the ongoing pandemic. The book is primarily intended to benefit medical professionals and healthcare workers as well as the virologists who are essentially the frontline fighters of this pandemic. In addition, it also serves as a vital resource for relevant researchers in this interdisciplinary field as well as for tutors and postgraduate and undergraduate students of information sciences.

The book is intended for readers who are interested in learning about the use of computer-based modelling of the COVID-19 disease. It provides a basic introduction to a five-ordinary differential equation (ODE) model by providing a complete statement of the model, including a detailed discussion of the ODEs, initial conditions and parameters, followed by a line-by-line explanation of a set of R routines (R is a quality, scientific programming system readily available from the Internet). The reader can access and execute these routines without having to first study numerical algorithms and computer coding (programming) and can perform numerical experimentation with the model on modest computers.

The aim of this book is to present new computational techniques and methodologies for the analysis of the clinical, epidemiological and public health aspects of SARS-CoV-2 and COVID-19 pandemic. The book presents the use of soft computing techniques such as machine learning algorithms for analysis of the epidemiological aspects of the SARS-CoV-2. This book clearly explains novel computational image processing algorithms for the detection of COVID-19 lesions in lung CT and X-ray images. It explores various computational methods for computerized analysis of the SARS-CoV-2 infection including severity assessment. The book provides a detailed description of the algorithms which can potentially aid in mass screening of SARS-CoV-2 infected cases. Finally the book also explains the conventional epidemiological models and machine learning techniques for the prediction of the course of the COVID-19 epidemic. It also provides real life examples through case studies. The book is intended for biomedical engineers, mathematicians, postgraduate students; researchers; medical scientists working on identifying and tracking infectious diseases.

Computational Approaches for Novel Therapeutic and Diagnostic Designing to Mitigate SARS-CoV2 Infection: Revolutionary Strategies to Combat Pandemics compiles information about various computational bioinformatic approaches that can help combat viral infection. The book includes working knowledge of various molecular docking and molecular dynamic simulation approaches that have been exploited for drug repurposing and drug designing purpose. In addition, it sheds light on reverse vaccinomics and immunoinformatic approaches for vaccine designing against SARS-CoV2 infection. This book is an essential resource for researchers, bioinformaticians, computational biologists, computational chemists and pharmaceutical companies who are working on the development of effective and specific therapeutic interventions and point-of-care diagnostic devices using various computational approaches. Covers computational based approaches for designing and repurposing drugs Discusses immunoinformatic and reverse vaccinomic approaches for effective vaccine design Categorizes information about artificial intelligence-based drug screening and diagnostic tools

The novel coronavirus disease 2019 (COVID-19) pandemic has posed a major threat to human life and health. This book is beneficial for interdisciplinary students, researchers, and professionals to understand COVID-19 and how computational intelligence can be used for the purpose of surveillance, control, prevention, prediction, diagnosis, and potential treatment of the disease. The book contains different aspects of COVID-19 that includes fundamental knowledge, epidemic forecast models, surveillance and tracking systems, IoT- and IoMT-based integrated systems for COVID-19, social network analysis systems for COVID-19, radiological images (CT, X-ray) based diagnosis system, and computational intelligence and in silico drug design and drug repurposing methods against COVID-19 patients. The contributing authors of this volume are experts in their fields and they are from various reputed universities and institutions across the world. This volume is a valuable and comprehensive resource for computer and data scientists, epidemiologists, radiologists, doctors, clinicians, pharmaceutical professionals, along with graduate and research students of interdisciplinary and multidisciplinary sciences.

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.

The coronavirus disease 2019 (COVID-19) outbreak has been designated as a worldwide pandemic by World Health Organization (WHO) and raised an international call for global health emergency. In this regard, recent advancements of technologies in the field of artificial intelligence and machine learning provide opportunities for researchers and scientists to step in this battlefield and convert the related data into a meaningful knowledge through computational-based models, for the task of containment the virus, diagnosis and providing treatment. In this study, we will provide recent developments and practical implementations of artificial intelligence modeling and machine learning algorithms proposed by researchers and practitioners during the pandemic period which suggest serious potential in compliant solutions for investigating diagnosis and decision making using computerized tomography (CT) scan imaging. We will review the modern algorithms in CT scan imaging modeling that may be used for detection, quantification, and tracking of Coronavirus and study how they can differentiate Coronavirus patients from those who do not have the disease.

In this study, five different deep convolutional neural network-based models (AlexNet, VGGNet16, VGGNet19, GoogleNet, and ResNet50) have been selected for the investigation to detect the coronavirus infected patient using chest CT radiographs digital images. The classical data augmentations along with CGAN improve the performance of classification in all selected deep transfer models. The Outcomes show that ResNet50 is the most appropriate classifier to detect the COVID-19 from chest CT dataset using the classical data augmentation and CGAN with testing accuracy of 82.91%.