Data Analytics for Pandemics: A COVID-19 Case Study (Intelligent Signal Processing and Data Analysis)

✍ Scribed by Gitanjali Rahul Shinde, Asmita Balasaheb Kalamkar, Parikshit N. Mahalle, Nilanjan Dey

• Publisher: CRC Press
• Year: 2020
• Tongue: English
• Leaves: 85
• Series: Intelligent Signal Processing and Data Analysis
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
Acknowledgment
Authors
Chapter 1 COVID-19 Outbreak
1.1 Introduction
1.2 Epidemic and Pandemic Overview
1.2.1 Stages of Disease
1.2.2 Pandemic Phases
1.2.2.1 Pandemic Risk Factors
1.2.2.2 Pandemic Mitigation
1.2.2.3 Situational Awareness
1.2.2.4 History of Pandemics
1.3 Novel Coronavirus
1.4 Medical Overview – Nature and Spread
1.5 Vulnerability Index
References
Chapter 2 Data Processing and Knowledge Extraction
2.1 Data Sources and Related Challenges
2.2 Data Storage: Platform
2.2.1 Storage Services
2.2.2 Big Data Analytics Services
2.2.3 Data Warehousing Services
2.3 Data Processing
2.3.1 Missing Values Imputation
2.3.2 Noise Treatment
2.4 Knowledge Extraction
2.4.1 Knowledge Extraction Based on Data Types
2.4.1.1 Knowledge Extraction from Text Data
2.4.1.2 Knowledge Extraction from Image Data
2.4.1.3 Knowledge Extraction from Audio Data
2.4.1.4 Knowledge Extraction from Video Data
2.4.2 Knowledge Extraction Techniques
References
Chapter 3 Big Data Analytics for COVID-19
3.1 All You Need to Know
3.1.1 WEB 2.0
3.1.2 Critical Thinking
3.1.3 Statistical Programming (R/Python)
3.1.4 R Programming Language
3.1.5 Python Programming Language
3.2 Data Visualization
3.2.1 Big Data Analytics and COVID-19
3.2.1.1 Statistical Parameters
3.2.1.2 Predictive Analytics
3.3 Data Models and Performance
3.3.1 Data Modeling Phases
3.3.2 Ensemble Data Model
3.3.3 Model Performance
3.4 Big Data Techniques
3.4.1 Association Rule Learning
3.4.2 Classification Tree Analysis
3.4.3 Genetic Algorithm
3.4.4 Machine Learning
3.4.5 Regression Analysis
3.4.6 Social Network Analysis
3.5 Big Data Tools and Technology
References
Chapter 4 Mitigation Strategies and Recommendations
4.1 Case Studies of COVID-19 Outbreak: Global Scenario
4.1.1 COVID-19 Spread in China
4.1.2 COVID-19 Spread in Italy
4.1.3 COVID-19 Spread in the United States
4.2 Mitigation Strategies and Discussion
4.3 Issues and Challenges
4.4 Recommendations
4.4.1 Recommendations for Citizens
4.4.2 Recommendations for COVID-19 Suspected and Infected Patients
4.4.3 Recommendations for Hospital Management: Adults
4.4.3.1 IPC Measures
4.4.4 Recommendations and Caring for Pregnant Ladies
4.4.5 Recommendations for Quarantine
4.5 Conclusions
4.6 Future Outlook
References
Index