A novel Multi-Agent Model with Adversarial Mobility Learning for Epidemic Simulation at the Community Level

在这一年里，我们首先对基于代理的疾病传播模拟器的模型进行了调查，了解了国内外对新冠肺炎疾病的不同模拟方法，在此基础上，我们设计并实现了第一个版本的社区级流行病模拟器，并将其命名为口豆。这个模拟器再现了筑波大学及其附近的校园。它代表的是工作人员和学生，他们在城市中进行日常活动。该模拟跟踪了新冠肺炎等空气传播疾病在室内和室外的传播情况，并考虑了口罩使用情况的变化。该模拟还再现了大地震后的疏散过程，并基于疏散中心的聚集来衡量疾病传播的差异。我们还开发了一个“智能城市规划模型(ICPM)”。ICPM使用机器学习来理解和整合人口统计和地理数据、土地使用和交通(LUTI)，以生成虚拟城市。我们证明了ICPM可以从真实世界的城市数据中学习，并根据模拟主体的需求重新创建逼真的城市设计。这是在模拟中学习高级代理移动性模型的第一步。以上两个工作都被接受并作为开源项目提供给公众。

In this year, first we performed a survey on agent-based models of disease spread simulator, understanding the different approaches for the simulation of COVID-19 disease around the world.Based on this survey, we have designed and implemented a first version of the Community Level Epidemic Simulator (CES), and named it Koudou. This simulator reproduces the campus of the University of Tsukuba and its neighborhood. It represents agents as workers and students who go around the city for their day-to-day activities. The simulation tracks the spread of an airborne disease like COVID-19 indoors and outdoors, and takes into account variation on mask usage. The simulation also reproduces the evacuation from a large earthquake, and measures the differences in disease spread based on gatherings in evacuation centers.We have also developed an "Intelligent City Planning Model (ICPM)". The ICPM uses uses Machine Learning to understand and integrate demographic and geographical data, Land Use and Transport (LUTI), to generate a virtual city. We demonstrated that the ICPM can learn from real world urban data and re-create realistic designs of cities based on the needs of simulated agents. This is a first step for learning advanced mobility models for agents in simulation.Both works above were accepted for presentation at the International ALIFE 2023 conference, and have been made available to the public as open-source projects.