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E-raamat: How Data Can Manage Global Health Pandemics: Analyzing and Understanding COVID-19 [Taylor & Francis e-raamat]

  • Formaat: 178 pages, 33 Line drawings, black and white; 33 Illustrations, black and white
  • Sari: HIMSS Book Series
  • Ilmumisaeg: 09-May-2022
  • Kirjastus: Productivity Press
  • ISBN-13: 9781003270911
Teised raamatud teemal:
  • Taylor & Francis e-raamat
  • Hind: 152,33 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Tavahind: 217,62 €
  • Säästad 30%
How Data Can Manage Global Health Pandemics: Analyzing and Understanding COVID-19Suurem pilt
  • Formaat: 178 pages, 33 Line drawings, black and white; 33 Illustrations, black and white
  • Sari: HIMSS Book Series
  • Ilmumisaeg: 09-May-2022
  • Kirjastus: Productivity Press
  • ISBN-13: 9781003270911
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9781003270911
"This book bridges the fields of health care and data to clarify how to use data to manage pandemics. Written while COVID-19 was raging, it identifies both effective practices and misfires, and is grounded in clear, research-based explanations of pandemics and data strategy.The author has written an essential book for students and professionals in both health care and data. While serving the needs of academics and experts, the book is accessible for the general reader."

Eileen Forrester, CEO of Forrester Leadership Group, Author of CMMI for Services, Guidelines for Superior Service

"Rupa Mahanti explores the connections between data and the human response to the spread of disease in her new book,... She recognizes the value of data and the kind of insight it can bring, while at the same time recognizing that using data to solve problems requires not just technology, but also leadership and courage. This is a book for people who want to better understand the role of data and people in solving human problems."

-- Laura Sebastian-Coleman, Author of Meeting the Challenges of Data Quality Management

In contrast to the 1918 Spanish flu pandemic which occurred in a non-digital age, the timing of the COVID-19 pandemic intersects with the digital age, characterized by the collection of large amounts of data and sophisticated technologies. Data and technology are being used to combat this digital age pandemic in ways that were not possible in the pre-digital age.

Given the adverse impacts of pandemics in general and the COVID-19 pandemic in particular, it is imperative that people understand the meaning, origin of pandemics, related terms, trajectory of a new disease, butterfly effect of contagious diseases, factors governing the pandemic potential of a disease, strategies to combat a pandemic, role of data, data sharing, data strategy, data governance, analytics, and data visualization in managing pandemics, pandemic myths, critical success factors in managing pandemics, and lessons learned. How Data Can Manage Global Health Pandemics: Analyzing and Understanding COVID-19 discusses these elements with special reference to COVID-19.

Dr. Rupa Mahanti is a business and data consultant and has expertise in different data management disciplines, business process improvement, regulatory reporting, quality management, and more. She is the author of Data Quality (ASQ Quality Press) and the series Data Governance: The Way Forward (Springer).
List of Figures
xi
List of Tables
xiii
Foreword xv
Preface xvii
Acknowledgments xxi
About the Author xxiii
1 Pandemic--An Introduction
1(16)
Introduction
1(1)
What Is a Pandemic?
2(1)
Pandemic--Definitions
2(1)
Characteristics of Pandemics
3(1)
Large Geographic Spread
3(1)
Person-to-Person Spread
4(1)
High Transmission Rates and Explosiveness
4(1)
Novelty
4(1)
Minimal Population Immunity
4(1)
Infectiousness
5(1)
Severity
5(1)
Origin of Pandemic
5(3)
Notable Pandemics in History
8(3)
Pandemics, COVID-19, Technology, Data, and Analytics in the 21st Century
11(2)
Concluding Thoughts
13(1)
References
13(4)
2 Data--Management, Strategy, Quality, Governance, and Analytics
17(14)
Delving into the Definition of Data
18(1)
Varieties of Data
18(1)
Structured Data
18(1)
Unstructured Data
19(1)
Semi-Structured Data
19(1)
Big Data versus Traditional Data
19(1)
Volume
19(1)
Velocity
19(1)
Variety
20(1)
Organization of Data
20(1)
Data Management
21(1)
Data Strategy
21(1)
Data Quality
22(1)
Data Quality Dimensions
23(2)
Data Quality Management
25(1)
Data Governance
25(1)
Data Analytics
26(1)
Descriptive Analytics
26(1)
Diagnostic Analytics
26(1)
Predictive Analytics
26(2)
Prescriptive Analytics
28(1)
Concluding Thoughts
29(1)
References
29(2)
3 Trajectory and Stages of a New Disease
31(24)
Introduction
31(1)
New Disease Trajectory--From Darkness to Light
32(1)
Contagious Disease and Butterfly Effect
33(1)
Contact Tracing and Disease Transmission
33(2)
Epidemic, Pandemic, Outbreak, and Endemic
35(2)
Index Case, Primary Case, Secondary Case, Patient Zero, and Super Spreaders
37(1)
Index Case
37(1)
Primary Case
38(1)
Secondary Case
38(1)
Patient Zero
38(2)
Super Spreaders
40(1)
Epidemiological Parameters
41(4)
Stages of a Disease and Pandemic Status
45(1)
Disease Stages as Defined by Centers for Disease Control and Prevention
46(1)
Disease Stages as Defined by WHO
47(1)
Stages of Spread of a Pandemic as Defined by Yigitcanlar et al. (2020), Bharat (2020)
48(1)
Flattening the Curve
49(2)
Concluding Thoughts
51(1)
References
51(4)
4 COVID-19--A Pandemic in the Digital Age
55(12)
Introduction
55(1)
COVID-19 Pandemic Predictions
56(1)
Co ronavi ruses
57(2)
What Is COVID-19?
59(1)
Who Was Patient Zero in COVID-19?
60(1)
COVID-19--From a Localized Outbreak into a Global Pandemic
60(1)
COVID-19 Features
60(1)
Spread and Impact of COVID-19--What Does the Data Say
60(3)
Concluding Thoughts
63(1)
References
63(4)
5 Data and Pandemic in the Digital World
67(24)
Data, Technology, Digital World, and Pandemic
67(1)
Types of Data from a Pandemic Analytics Perspective
68(1)
Role of Data in Pandemic
69(1)
Big Data Sources and Pandemic Management
70(1)
Use of External Data and Data Sharing in a Pandemic
71(1)
Data Challenges in a Pandemic--COVID-19 as an Example
72(1)
Data Collection
72(2)
Misinformation
74(3)
Data Quality
77(3)
Data Definition and Metadata
80(1)
Data Security, Data Protection, and Data Privacy
81(3)
Concluding Thoughts
84(2)
References
86(5)
5 Data Analytics and Pandemic
91(24)
Pandemics and Data Analytics
91(2)
Data Analytics Use Cases in the Pandemic
93(2)
Predicting Virus Spread
95(1)
Medical Imaging
95(1)
New Drug Development
95(1)
Modeling Infection Severity
96(1)
Combating Misleading Information
96(1)
Vaccine Development, Management, and Distribution
97(3)
Pandemics, Analytics, and Retail
100(1)
Pandemics, Analytics, and Finance
100(1)
COVID-19--Examples of Data Analytics Application in Different Industry Sectors
101(1)
Data Visualization and Its Role in a Pandemic
102(8)
Concluding Thoughts
110(2)
References
112(3)
7 Disease and Pandemic Potential
115(8)
Pathogens and Pandemic Potential
116(2)
Factors Determining Pandemic Potential
118(2)
Concluding Thoughts
120(1)
References
120(3)
8 Pandemic and Critical Success Factors
123(14)
An Introduction to Pandemic Myths and Critical Success Factors
123(1)
Pandemic Myths
123(1)
Myth 1 Pandemics Are Public Health Problem Only
124(1)
Myth 2 Pandemics Are Extremely Rare and Have Short-Term Impacts
124(1)
Myth 3 Doctors Are Aware of All the Infectious Diseases
125(1)
Myth 4 Infrastructure, Resources, and Capacity Are There to Detect and Effectively Respond to Pandemics
125(1)
Myth 5 Disease Emergence Is Unavoidable, and No One Can Do Anything About It
125(1)
Critical Success Factors to Manage a Pandemic
126(1)
Government and Leadership
127(1)
Capacity to Trace, Test, and Treat
128(1)
Education and Training
129(1)
Pandemic Preparedness and Strategies
130(1)
Communication
131(1)
Technology and Data
131(1)
Collaboration, Coordination, and Global Solidarity
132(1)
Concluding Thoughts
133(1)
References
134(3)
9 Pandemic Preparedness and Strategies
137(12)
Preparing for a Pandemic
138(2)
Pandemic and Strategies
140(4)
Data Strategy and the Pandemic
144(1)
Concluding Thoughts
145(1)
References
146(3)
10 Pandemic--Lessons Learned and Future Ahead
149(18)
Introduction
149(1)
Lessons from Past Pandemics--With Special Reference to 1918
Spanish Flu
150(1)
COVID-19 Pandemic--Specific Lessons
151(16)
1 Save Nature
152(1)
2 Government and Leadership Lessons
153(1)
3 Transparency, Effective Governance, and Timely Release of Relevant Information
154(2)
4 Non-Pharmaceutical Interventions and Pharmaceutical Precautions
156(1)
5 Robust Health Surveillance System
157(1)
6 Testing Responsiveness and Resilience of Health Systems and Action Plan to Balloon Healthcare Infrastructure
157(2)
7 Funding Research and Development (R&D)
159(1)
8 Opportunities to Use Novel Approaches and Technologies
159(1)
9 Leverage Data
160(1)
10 Communication and Collaboration
160(1)
Concluding Thoughts
161(1)
References
162(5)
Appendix A Abbreviations and Acronyms 167(2)
Appendix B Glossary of Terms 169(1)
References 170(1)
Index 171
Rupa Mahanti is a Business and Information Management consultant with extensive and diversified consulting experience in different solution environments, industry sectors, and geographies (United States, United Kingdom, India, and Australia). She has expertise in different information management disciplines, business process improvement, regulatory reporting, and more. Her research interests include quality management, information management, software engineering, empirical study, environmental management, simulation and modeling and more. With a work experience that spans industry, academics, and research, Rupa has guided a doctoral dissertation, published a large number of research articles, and is the author of the booksData Quality, Data Governance and Compliance, Data Governance and Data Management, Data Governance Success, Data Humour, and Thoughts. She is an Associate Editor with the journal Software Quality Professional and a reviewer for several international journals.
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