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| 001 | EBC6275346 | ||
| 003 | MiAaPQ | ||
| 005 | 20240724114433.0 | ||
| 006 | m o d | | ||
| 007 | cr cnu|||||||| | ||
| 008 | 240724s2020 xx o ||||0 eng d | ||
| 020 |
_a9781000204414 _q(electronic bk.) |
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| 020 | _z9780367558468 | ||
| 035 | _a(MiAaPQ)EBC6275346 | ||
| 035 | _a(Au-PeEL)EBL6275346 | ||
| 035 | _a(OCoLC)1182879691 | ||
| 040 |
_aMiAaPQ _beng _erda _epn _cMiAaPQ _dMiAaPQ |
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| 050 | 4 | _aRA644.C67 .S556 2021 | |
| 082 | 0 | _a616.24140727 | |
| 100 | 1 | _aShinde, Gitanjali Rahul. | |
| 245 | 1 | 0 |
_aData Analytics for Pandemics : _bA COVID-19 Case Study. |
| 250 | _a1st ed. | ||
| 264 | 1 |
_aMilton : _bTaylor & Francis Group, _c2020. |
|
| 264 | 4 | _c©2021. | |
| 300 | _a1 online resource (85 pages) | ||
| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 490 | 1 | _aIntelligent Signal Processing and Data Analysis Series | |
| 505 | 0 | _aCover -- 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. | |
| 505 | 8 | _aReferences -- 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. | |
| 520 | _aThe 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. | ||
| 588 | _aDescription based on publisher supplied metadata and other sources. | ||
| 590 | _aElectronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. | ||
| 650 | 0 | _aCOVID-19 (Disease)-Statistical methods. | |
| 655 | 4 | _aElectronic books. | |
| 700 | 1 | _aKalamkar, Asmita Balasaheb. | |
| 700 | 1 | _aMahalle, Parikshit N. | |
| 700 | 1 | _aDey, Nilanjan. | |
| 776 | 0 | 8 |
_iPrint version: _aShinde, Gitanjali Rahul _tData Analytics for Pandemics _dMilton : Taylor & Francis Group,c2020 _z9780367558468 |
| 797 | 2 | _aProQuest (Firm) | |
| 830 | 0 | _aIntelligent Signal Processing and Data Analysis Series | |
| 856 | 4 | 0 |
_uhttps://ebookcentral.proquest.com/lib/orpp/detail.action?docID=6275346 _zClick to View |
| 999 |
_c19886 _d19886 |
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