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

新冠肺炎疫情的发展对管理和控制受影响人数的最佳政策设计提出了重要问题。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数量。特别是，像多伦多这样的大城市的城市空间是这种疾病可能传播的一个地区。 该项目的目标是创建一个基于代理的框架，用于构建城市地区的虚拟模型。这一框架将被用作测试各种情景及其对流行病发展的影响的虚拟实验室。为了使结论可靠，模型(在文献中被称为合成人口模型或数字双胞胎)必须达到规模，代理人的数量与城市人口相当。这进而要求实施准备好在云中的大规模分布式计算环境中运行(例如，使用Amazon Web Services-AWS的Elastic Compute Cloud-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新冠肺炎缓解政策设计。这包括可能的决定，比如减少戴口罩的倾向，关闭一些不必要的、高接触的社交网络节点(例如理发师)，限制同时举行社交聚会的人数，或者通过推广#stayathome等行动完全减少街道上的人数。

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.