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大流行的发展提出了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。事实上，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，以及在这些接触期间提供的护理和保护的程度。特别地，疾病可以传播的一个区域是诸如多伦多的大城市的城市空间。 该项目的目标是创建一个基于代理的框架，用于构建城市区域的虚拟模型。该框架将用作虚拟实验室，用于测试各种场景及其对流行病发展的影响。为了使结论可靠，模型（在文献中称为合成人口模型或数字孪生模型）必须达到规模，代理人的数量与城市人口相当。这反过来又需要准备好在云中的大规模分布式计算环境中运行的实现（例如，使用Amazon Web Services - AWS的弹性计算云- EC2服务），因为引擎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，例如减少戴口罩的倾向，关闭一些非必要的，高接触的社交网络节点（例如，理发师），限制同时进行社交聚会的人数，或者通过#stayathome等促进行动减少街道上的人数。