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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.
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