Advanced Computational Techniques for SARS-CoV-2 and COVID-19 Analysis

Authored by S. Prabha, this book presents a comprehensive overview of new computational techniques and methodologies for the analysis of the clinical, epidemiological, and public health aspects of SARS-CoV-2 and the COVID-19 pandemic. It delves into the use of soft computing techniques, such as machine learning algorithms, for the analysis of the epidemiological aspects of SARS-CoV-2.

Computational Image Processing for COVID-19 Detection

The book provides a detailed explanation of novel computational image processing algorithms for the detection of COVID-19 lesions in lung CT and X-ray images. These algorithms can potentially aid in mass screening of SARS-CoV-2 infected cases, significantly improving the speed and accuracy of diagnosis.

Computational Methods for SARS-CoV-2 Infection Analysis

It explores various computational methods for computerized analysis of the SARS-CoV-2 infection, including severity assessment. This section of the book is particularly useful for researchers and medical scientists working on identifying and tracking infectious diseases.

Epidemiological Models and Machine Learning Techniques

The book also explains conventional epidemiological models and machine learning techniques for the prediction of the course of the COVID-19 epidemic. It includes real-life examples through case studies, making it an invaluable resource for biomedical engineers, mathematicians, postgraduate students, and researchers.

Overall, this book serves as a comprehensive guide for those interested in understanding the computational aspects of the SARS-CoV-2 pandemic and its implications for public health.