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Data Analytics for Pandemics : A COVID-19 Case Study.

By: Contributor(s): Material type: TextTextSeries: Intelligent Signal Processing and Data Analysis SeriesPublisher: Milton : Taylor & Francis Group, 2020Copyright date: ©2021Edition: 1st edDescription: 1 online resource (85 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781000204414
Subject(s): Genre/Form: Additional physical formats: Print version:: Data Analytics for PandemicsDDC classification:
  • 616.24140727
LOC classification:
  • RA644.C67 .S556 2021
Online resources:
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.
Summary: 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.
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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.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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