Data Science Advancements in Pandemic and Outbreak Management

Data Science Advancements in Pandemic and Outbreak Management
  • eBook:
    Data Science Advancements in Pandemic and Outbreak Management
  • Author:
    Eleana Asimakopoulou, Nik Bessis
  • Edition:
    1 edition
  • Categories:
  • Data:
    April 9, 2021
  • ISBN:
  • ISBN-13:
  • Language:
  • Pages:
    255 pages
  • Format:
    PDF, ePUB

Book Description
Pandemics are disruptive. Thus, there is a need to prepare and plan actions in advance for identifying, assessing, and responding to such events to manage uncertainty and support sustainable livelihood and wellbeing. A detailed assessment of a continuously evolving situation needs to take place, and several aspects must be brought together and examined before the declaration of a pandemic even happens. Various health organizations; crisis management bodies; and authorities at local, national, and international levels are involved in the management of pandemics. There is no better time to revisit current approaches to cope with these new and unforeseen threats. As countries must strike a fine balance between protecting health, minimizing economic and social disruption, and respecting human rights, there has been an emerging interest in lessons learned and specifically in revisiting past and current pandemic approaches. Such approaches involve strategies and practices from several disciplines and fields including healthcare, management, IT, mathematical modeling, and data science. Using data science to advance in-situ practices and prompt future directions could help alleviate or even prevent human, financial, and environmental compromise, and loss and social interruption via state-of-the-art technologies and frameworks.
Data Science Advancements in Pandemic and Outbreak Management demonstrates how strategies and state-of-the-art IT have and/or could be applied to serve as the vehicle to advance pandemic and outbreak management. The chapters will introduce both technical and non-technical details of management strategies and advanced IT, data science, and mathematical modelling and demonstrate their applications and their potential utilization within the identification and management of pandemics and outbreaks. It also prompts revisiting and critically reviewing past and current approaches, identifying good and bad practices, and further developing the area for future adaptation. This book is ideal for data scientists, data analysts, infectious disease experts, researchers studying pandemics and outbreaks, IT, crisis and disaster management, academics, practitioners, government officials, and students interested in applicable theories and practices in data science to mitigate, prepare for, respond to, and recover from future pandemics and outbreaks.


Section 1 - Overview of Technologies for Pandemic Management
Chapter 1. A Review of Technologies in Emergency Medicine and Sophisticated Collective Decision-Making in the Era of the Fight Against the Pandemic
Chapter 2. Overview of IoT and Machine Learning for E-Healthcare in Pandemics and Health Crises
Chapter 3. Data Analytics Epidemic Modelling and Human Dynamics Approaches for Pandemic Outbreak

Section 2 - Data Science Practices in Pandemic Management
Chapter 4. The Importance of Big Data Metadata in Crisis Management
Chapter 5. Pandemic Management With Social Media Analytics
Chapter 6. Deep Learning Approaches in Pandemic and Disaster Management
Chapter 7. Information Extraction From Social Media for Epidemic Models
Chapter 8. Towards Combating Pandemic-Related Misinformation in Social Media

Section 3 - Technology-Driven Challenges and Implications in Pandemic Management
Chapter 9. Ensuring Food Supply and Security During Localised Lockdowns: An Information and Technology-Based Approach
Chapter 10. Privacy-Preserving Pandemic Monitoring
Chapter 11. Strategies for Upskilling in Data Science After the COVID 19 Pandemic

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