Data Analytics For Pandemics

Epidemic trend analysis, timeline progression, prediction, and recommendation are critical for initiating effective public health control strategies, and AI and data analytics play an important role in epidemiology, diagnostic, and clinical fronts. The focus of this book is data analytics for COVID-19, which includes an overview of COVID-19 in terms of epidemic/pandemic, data processing and knowledge extraction. Data sources, storage and platforms are discussed along with discussions on data models, their performance, different big data techniques, tools and technologies. This book also addresses the challenges in applying analytics to pandemic scenarios, case studies and control strategies. Aimed at Data Analysts, Epidemiologists and associated researchers, this book: discusses challenges of AI model for big data analytics in pandemic scenarios; explains how different big data analytics techniques can be implemented; provides a set of recommendations to minimize infection rate of COVID-19; summarizes various techniques of data processing and knowledge extraction; enables users to understand big data analytics techniques required for prediction purposes.

Epidemic trend analysis, timeline progression, prediction, and recommendation are critical for initiating effective public health control strategies, and AI and data analytics play an important role in epidemiology, diagnostic, and clinical fronts. The focus of this book is data analytics for COVID-19, which includes an overview of COVID-19 in terms of epidemic/pandemic, data processing and knowledge extraction. Data sources, storage and platforms are discussed along with discussions on data models, their performance, different big data techniques, tools and technologies. This book also addresses the challenges in applying analytics to pandemic scenarios, case studies and control strategies. Aimed at Data Analysts, Epidemiologists and associated researchers, this book: discusses challenges of AI model for big data analytics in pandemic scenarios; explains how different big data analytics techniques can be implemented; provides a set of recommendations to minimize infection rate of COVID-19; summarizes various techniques of data processing and knowledge extraction; enables users to understand big data analytics techniques required for prediction purposes.

There has been a multitude of studies focused on the COVID-19 pandemic across fields and disciplines as all sectors of life have had to adjust the way things are done and adapt to the constantly shifting environment. These studies are crucial as they provide support and perspectives on how things are changing and what needs to be done to stay afloat. Connecting COVID-19-related studies and big data analytics is crucial for the advancement of industrial applications and research areas. Applied Big Data Analytics and Its Role in COVID-19 Research introduces the most recent industrial applications and research topics on COVID-19 with big data analytics. Featuring coverage on a broad range of big data technologies such as data gathering, artificial intelligence, smart diagnostics, and mining mobility, this publication provides concrete examples and cases of usage of data-driven projects in COVID-19 research. This reference work is a vital resource for data scientists, technical managers, researchers, scholars, practitioners, academicians, instructors, and students.

Pandemics are disruptive. Thus, there is a need to prepare and plan actions in advance for identifying, assessing, and responding to such events to manage uncertainty and support sustainable livelihood and wellbeing. A detailed assessment of a continuously evolving situation needs to take place, and several aspects must be brought together and examined before the declaration of a pandemic even happens. Various health organizations; crisis management bodies; and authorities at local, national, and international levels are involved in the management of pandemics. There is no better time to revisit current approaches to cope with these new and unforeseen threats. As countries must strike a fine balance between protecting health, minimizing economic and social disruption, and respecting human rights, there has been an emerging interest in lessons learned and specifically in revisiting past and current pandemic approaches. Such approaches involve strategies and practices from several disciplines and fields including healthcare, management, IT, mathematical modeling, and data science. Using data science to advance in-situ practices and prompt future directions could help alleviate or even prevent human, financial, and environmental compromise, and loss and social interruption via state-of-the-art technologies and frameworks. Data Science Advancements in Pandemic and Outbreak Management demonstrates how strategies and state-of-the-art IT have and/or could be applied to serve as the vehicle to advance pandemic and outbreak management. The chapters will introduce both technical and non-technical details of management strategies and advanced IT, data science, and mathematical modelling and demonstrate their applications and their potential utilization within the identification and management of pandemics and outbreaks. It also prompts revisiting and critically reviewing past and current approaches, identifying good and bad practices, and further developing the area for future adaptation. This book is ideal for data scientists, data analysts, infectious disease experts, researchers studying pandemics and outbreaks, IT, crisis and disaster management, academics, practitioners, government officials, and students interested in applicable theories and practices in data science to mitigate, prepare for, respond to, and recover from future pandemics and outbreaks.

This book includes research articles and expository papers on the applications of artificial intelligence and big data analytics to battle the pandemic. In the context of COVID-19, this book focuses on how big data analytic and artificial intelligence help fight COVID-19. The book is divided into four parts. The first part discusses the forecasting and visualization of the COVID-19 data. The second part describes applications of artificial intelligence in the COVID-19 diagnosis of chest X-Ray imaging. The third part discusses the insights of artificial intelligence to stop spread of COVID-19, while the last part presents deep learning and big data analytics which help fight the COVID-19.

This book presents intelligent data analysis as a tool to fight against COVID-19 pandemic. The intelligent data analysis includes machine learning, natural language processing, and computer vision applications to teach computers to use big data-based models for pattern recognition, explanation, and prediction. These functions are discussed in detail in the book to recognize (diagnose), predict, and explain (treat) COVID-19 infections, and help manage socio-economic impacts. It also discusses primary warnings and alerts; tracking and prediction; data dashboards; diagnosis and prognosis; treatments and cures; and social control by the use of intelligent data analysis. It provides analysis reports, solutions using real-time data, and solution through web applications details.

"Epidemic trend analysis, timeline progression, prediction and recommendation are critical for initiating effective public health control strategies and AI and data analytics play an important role in epidemiology, diagnostic and clinical fronts. The focus of this book is data analytics for COVID-19 which includes an overview of COVID-19 in terms of epidemic/pandemic, data processing and knowledge extraction. Data sources, storage and platforms are discussed along with discussion on data models, their performance, different Big data techniques, tools and technologies. This book also addresses the challenges in applying analytics to pandemic scenarios, case studies and control strategies"--

"Epidemic trend analysis, timeline progression, prediction and recommendation are critical for initiating effective public health control strategies and AI and data analytics play an important role in epidemiology, diagnostic and clinical fronts. The focus of this book is data analytics for COVID-19 which includes an overview of COVID-19 in terms of epidemic/pandemic, data processing and knowledge extraction. Data sources, storage and platforms are discussed along with discussion on data models, their performance, different Big data techniques, tools and technologies. This book also addresses the challenges in applying analytics to pandemic scenarios, case studies and control strategies"--