Data Analytics for Pandemics : A COVID-19 Case Study.
- 1st ed.
- 1 online resource (85 pages)
- Intelligent Signal Processing and Data Analysis Series .
- Intelligent Signal Processing and Data Analysis Series .
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.
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.