Social Simulation of COVID-19 with AI in Japan
Springer Nature Switzerland AG (Verlag)
978-981-96-8065-8 (ISBN)
In the project, implementing deep learning technologies has enabled access to extensive infection spread data, allowing for machine learning-based predictions. Additionally, agent-based simulation was extensively utilized in this project. Agent-based simulation involves recreating a virtual real-world environment where numerous human-like agents interact dynamically. This approach facilitates the reproduction of complex societal problems and the exploration of potential solutions, which can be fed back into real-world problem-solving.
This book serves as a valuable record of how AI and simulation technologies were applied in response to the unprecedent crisis posed by the COVID-19 in Japan. The insights gained from this endeavor will contribute to preparedness for the next inevitable pandemic.
Satoshi Kurihara (Ph.D.) Professor of Faculty of Science and Technology, Keio Univ. Director of Center of Advanced Research for Human-AI Symbiosis Society, Keio Univ. President of Japanese Society for Artificial Intelligence (JSAI). After working at NTT Basic Research Laboratories, Osaka University and the University of Electro-Communications, he has been in her current position since 2018. Research areas include multi-agent, complex network science and computational social science. Kaoru Endo, Satoshi Kurihara, Takashi Kamihigashi, Fujio Toriumi (eds.), Reconstruction of the Public Sphere in the Socially Mediated Age, springer, 2017., Akira Namatame, Satoshi Kurihara, Hideyuki Nakashima(eat.), Emergent Intelligence of Networked Agents, Springer, 2007., etc.
Chapter 1 Leveraging Artificial Intelligence and Complex Systems Simulation for Computational Pandemic Response: The Japanese Government's COVID-19 AI & Simulation Project.- Chapter 2 Projection of COVID-19 positive cases considering new viral variants and vaccination effectiveness models: Deep learning approach.- Chapter 3 Projections of COVID-19 Severity and Case Fatality Rates in Tokyo: Real-Time Analysis and Ex-post Assessment.- Chapter 4 Prefecture-Level Projections for COVID-19 Hospital Bed Demand in Japan.- Chapter 5 Driven by Models, Data, and People toward Lessons from “Stay with Your Community” .- Chapter 6 Risk of COVID-19 infection at home and in the office.- Chapter 7 Individual-based epidemic simulation with one million agents.- Chapter 8 Effect of small-world network on infection diffusion: A multi-agent simulation reflecting human travel network.- Chapter 9 A multi-layered AI simulation for assessing the impact of infection control measures.- Chapter 10 The ｐEffects of Reopening the Border on Covid-19 Infection: A Simulation Study for Japan in the Summer of 2022.- Chapter 11 Forecasting COVID-19 Infection with Model Averaging: A Real-Time Evaluation 1.
| Erscheinungsdatum | 13.06.2025 |
|---|---|
| Zusatzinfo | 92 Illustrations, color; 10 Illustrations, black and white |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Office Programme ► Outlook |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Medizin / Pharmazie ► Allgemeines / Lexika | |
| Sozialwissenschaften ► Soziologie ► Empirische Sozialforschung | |
| Schlagworte | complex network • Covid-19 • Deep learning • epidemic simulation • multiagent simulation • multi-layered AI simulation • Real-time Monitoring • social simulation • Vaccination |
| ISBN-10 | 981-96-8065-4 / 9819680654 |
| ISBN-13 | 978-981-96-8065-8 / 9789819680658 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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