Data analytics for pandemics : a COVID-19 case study / Gitanjali Rahul Shinde, Asmita Balasaheb Kalamkar, Parikshit N. Mahalle, Nilanjan Dey.

Format:

Book

Author/Creator:

Shinde, Gitanjali Rahul, 1983- author.

Kalamkar, Asmita Balasaheb, author.

Mahalle, Parikshit N., author.

Dey, Nilanjan, 1984- author.

Series:

CRC focus series

Intelligent signal processing and data analysis

Language:

English

Subjects (All):

COVID-19 (Disease)--Statistical methods.

COVID-19 (Disease).

Physical Description:

1 online resource (xv, 68 pages) : illustrations

Edition:

First edition.

Place of Publication:

Boca Raton, FL : CRC Press, [2021].

Summary:

"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"-- Provided by publisher.

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

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

Index.

Notes:

Description based on print version record.

ISBN:

9781003095415

1003095410

9781000204414

1000204413

OCLC:

1154861973