Data Science for COVID-19 is a comprehensive book that covers data science techniques for detecting, mitigating, treating, and eliminating COVID-19. It includes chapters on Image Analysis and Data Processing, Geoprocessing and tracking, Predictive Systems, Design Cognition, mobile technology, and telemedicine solutions. It also covers Artificial Intelligence-based solutions, innovative treatment methods, and public safety, and applications of Big Data and new data models for mitigation.

Format: Paperback / softback
Length: 752 pages
Publication date: 01 April 2021
Publisher: Elsevier Science Publishing Co Inc

Data Science for COVID-19: Unlocking the Power of Data to Combat the Pandemic

COVID-19 has presented a global challenge that requires innovative solutions. Data science has emerged as a powerful tool in combating the pandemic, enabling researchers to detect, mitigate, treat, and eliminate the virus. This comprehensive book delves into the latest research on data science techniques for COVID-19, covering a wide range of applications.

In the introductory section, the book explores the historical context of pandemics and the role of data science in responding to them. It also discusses the related Coronavirus variations, such as SARS-CoV-2, and their impact on public health. The authors highlight the importance of data science in predicting and preparing for future pandemics.

The subsequent chapters delve into various data science applications related to COVID-19 research. These include image analysis and data processing, geoprocessing and tracking, predictive systems, design cognition, mobile technology, and telemedicine solutions. The authors provide detailed explanations of these applications and their potential benefits in combating the pandemic.

Artificial intelligence plays a significant role in the book, with chapters dedicated to its applications in COVID-19 research. The authors discuss machine learning algorithms, natural language processing, and computer vision techniques for analyzing medical data and identifying patterns. They also explore innovative treatment methods, such as personalized medicine and gene therapy, and their potential impact on patient outcomes.

Public safety is another critical aspect of COVID-19 response, and the book addresses this issue. It discusses the role of data science in monitoring and predicting disease outbreaks, developing early warning systems, and implementing effective public health interventions. The authors also explore the applications of big data and new data models for mitigation, such as data visualization and predictive analytics.

The book concludes with a discussion of the future of data science in combating pandemics. The authors emphasize the need for interdisciplinary collaboration, data sharing, and continuous research to enhance our understanding of the virus and develop effective strategies for its control.

Data Science for COVID-19 is a valuable resource for researchers, healthcare professionals, policymakers, and anyone interested in understanding the role of data science in combating the pandemic. It provides a comprehensive overview of the latest research and applications, highlighting the potential of data science to make a significant impact in the fight against COVID-19 and future pandemics.

Data Science for COVID-19: Unlocking the Power of Data to Combat the Pandemic

COVID-19 has presented a global challenge that requires innovative solutions. Data science has emerged as a powerful tool in combating the pandemic, enabling researchers to detect, mitigate, treat, and eliminate the virus. This comprehensive book delves into the latest research on data science techniques for COVID-19, covering a wide range of applications.

In the introductory section, the book explores the historical context of pandemics and the role of data science in responding to them. It also discusses the related Coronavirus variations, such as SARS-CoV-2, and their impact on public health. The authors highlight the importance of data science in predicting and preparing for future pandemics.

The subsequent chapters delve into various data science applications related to COVID-19 research. These include image analysis and data processing, geoprocessing and tracking, predictive systems, design cognition, mobile technology, and telemedicine solutions. The authors provide detailed explanations of these applications and their potential benefits in combating the pandemic.

Artificial intelligence plays a significant role in the book, with chapters dedicated to its applications in COVID-19 research. The authors discuss machine learning algorithms, natural language processing, and computer vision techniques for analyzing medical data and identifying patterns. They also explore innovative treatment methods, such as personalized medicine and gene therapy, and their potential impact on patient outcomes.

Public safety is another critical aspect of COVID-19 response, and the book addresses this issue. It discusses the role of data science in monitoring and predicting disease outbreaks, developing early warning systems, and implementing effective public health interventions. The authors also explore the applications of big data and new data models for mitigation, such as data visualization and predictive analytics.

The book concludes with a discussion of the future of data science in combating pandemics. The authors emphasize the need for interdisciplinary collaboration, data sharing, and continuous research to enhance our understanding of the virus and develop effective strategies for its control.

Data Science for COVID-19 is a valuable resource for researchers, healthcare professionals, policymakers, and anyone interested in understanding the role of data science in combating the pandemic. It provides a comprehensive overview of the latest research and applications, highlighting the potential of data science to make a significant impact in the fight against COVID-19 and future pandemics.

Dimension: 235 x 191 (mm)
ISBN-13: 9780128245361