AI Versus Epidemics (eBook)

James Hughes, Sheridan Houghten, Michael Dubé, Daniel Ashlock, Joseph Alexander Brown, Wendy Ashlock, Matthew Stoodley (Autoren)

eBook Download: PDF

2024
XI, 97 Seiten
Springer International Publishing (Verlag)
978-3-031-64373-6 (ISBN)

Infos

This book presents algorithms and tools that are designed to model and extract information from personal contact networks, which represent which individuals in a population are physically in contact with one another. The authors developed these tools based on research they conducted during the COVID-19 pandemic, with the goal of improving responses to epidemics in the future. The book provides methods for modelling the transmission of infection across a population. The authors explain how an epidemic model can be used to strategically distribute vaccines and minimize the spread of a virus. The book shows how evolutionary computation, graph compression, and network induction can be utilized to manage issues that arise from an epidemic. 

Produkt-Details

• Erscheint lt. Verlag: 24.9.2024
• Reihe/Serie: Synthesis Lectures on Learning, Networks, and Algorithms
• Zusatzinfo: XI, 97 p. 57 illus., 35 illus. in color.
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Schlagworte: Artificial Intelligence • Covid-19 • Epidemics • pandemics • Personal Contact Networks • SARS-CoV-2
• ISBN-10: 3-031-64373-9 / 3031643739
• ISBN-13: 978-3-031-64373-6 / 9783031643736