COVID-19: Agent-based framework for modelling pandemics in urban environment

COVID-19：基于代理的城市环境流行病建模框架

• 批准号: 555131-2020
• 负责人: Pralat, Pawel
• 金额: $ 3.64万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=705755
• 关键词: COVID; 19; Agent; based; framework

The development of COVID-19 pandemic raises important questions on optimal policy design for managing and controlling the number of people affected. In order to answer these questions, one needs to better understand determinants of pandemic dynamics. Indeed, the development of epidemics depends on various factors including the intensity and frequency of social contacts and the amount of care and protection applied during those contacts. In particular, one area where the disease can be transmitted is the urban space of a large city such as Toronto. The goal of the project is to create an agent-based framework for building virtual models of an urban area. This framework will be used as a virtual laboratory for testing various scenarios and their implications for the development of pandemics. In order for conclusions to be reliable, the models (known in the literature as synthetic population models or digital twins) have to be up to scale, with the number of agents comparable with the population of the city. This, in turn, requires implementations ready to be run in a large scale distributed computing environment in the cloud (for example, using the Elastic Compute Cloud - EC2 service at Amazon Web Services - AWS) as the algorithms behind the engine need high performance computing power. The framework will allow us to evaluate different COVID-19 mitigation policy designs. This includes possible decisions such as decreasing proneness to wearing masks, closing down some non-essential, high-contact, social network nodes (for example, hairdressers), limiting the number of people having simultaneous social gatherings or reducing the number of people on streets altogether via promoting actions such as #stayathome.

COVID-19的发展大流行提出了有关管理和控制受影响人数的最佳政策设计的重要问题。为了回答这些问题，人们需要更好地了解大流行动态的决定因素。实际上，流行病的发展取决于各种因素，包括社会接触的强度和频率以及这些接触期间应用的护理和保护量。特别是，可以传播该疾病的一个地区是多伦多大城市的城市空间。 该项目的目的是创建一个基于代理的框架，以建立城市区域的虚拟模型。该框架将用作测试各种场景及其对流浪者发展的影响的虚拟实验室。为了使结论可靠，这些模型（文献中称为合成人口模型或数字双胞胎）必须达到缩放，代理的数量与城市的人口相当。反过来，这需要在云中大规模分布式计算环境中准备就绪（例如，使用弹性计算云-EC2服务在Amazon Web服务 - AWS）作为发动机背后的算法需要高性能计算能力。 该框架将使我们能够评估不同的COVID-19缓解政策设计。这包括可能的决定，例如戴口罩的倾斜度，关闭一些非必需的，高接触的，社交网络节点（例如，美发师），限制了同时社交聚会的人数，或通过诸如#StayAthateome等促销活动完全减少街道上的人数。