Description
This book summarises the research findings of the COVID-19 AI & Simulation Project in Japan. The COVID-19 pandemic presented unprecedented challenges to public health systems and socioeconomic stability worldwide, necessitating rapid, computational and evidence-based decision-making under extreme uncertainty. The project exemplifies the integration of advanced computational modeling with public policy formation. The project developed a comprehensive framework for pandemic response that bridged the gap between scientific analysis and practical policy implementation by deploying artificial intelligence, complex network analysis, multi-agent simulations, fluid simulation, and laser optics.
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.
Table of Contents
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.
