A connectivity approach to stochastically simulate physical distancing and to make more accurate predictions of its effectiveness to reduce the spread of the COVID-19 outbreak

一种连接方法，可随机模拟物理距离并更准确地预测其减少 COVID-19 爆发传播的有效性

• 批准号: 554430-2020
• 负责人: Elmo, Davide
• 金额: $ 3.22万
• 依托单位: University of British Columbia
• 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=705471
• 关键词: connectivity; approach; stochastically; simulate; physical

Physical distancing (or social distancing) is the paradigm presented by public health experts and public officials as the best, albeit indirect, tool to slow the spread of the Covid-19 outbreak. We consider physical distancing an indirect tool since only a vaccine would directly target the virus responsible for the outbreak and render people immune to it. Around the world countries have imposed strict lockdown procedures in an effort to maximise physical distancing. Public health experts and elected officials know that those solutions are temporary and not economically viable in the long-term. As we begin to consider relaxing lockdown measures there is the need to effectively simulate physical distancing at the scale of large and densely populated urban areas. The principal investigator has actively worked with the Partner Organisation, researching discrete fracture network (DFN) models and connectivity of natural fracture networks. Physical distancing can ultimately be considered a problem of connectivity between objects (people): by replacing fractures with people, the center of every fracture in the network represents an individual, and the size of the fracture becomes a measure of physical distancing. Fractures (people) could also be assigned different properties representing, for example, positive or asymptomatic conditions. The result would be a connectivity path between people with different underlying conditions. Combined with a robust stochastic (probabilistic) framework, DFN models are therefore well suited to simulate scenarios of physical distancing applied to large scale areas, while at the same time accounting for variations in population density. We believe the proposed research program will significantly enhance Canada's body of knowledge and expertise in the use of discrete simulations applied to random (stochastic) scenarios, and provide a new way to measure and thus implement physical distancing in the context of highly populated areas, and to make more accurate predictions of its effectiveness to reduce the spread of the outbreak.

物理距离（或社交距离）是公共卫生专家和公职人员提出的范例，是减缓新冠肺炎疫情传播的最佳（尽管是间接的）工具。我们认为物理距离是一种间接的工具，因为只有疫苗才能直接针对导致疫情的病毒，使人们对其产生免疫力。世界各地的国家都实施了严格的封锁程序，以尽量扩大物理距离。公共卫生专家和民选官员知道，这些解决办法是暂时的，从长远来看在经济上不可行。当我们开始考虑放宽封锁措施时，有必要在人口密集的大型城市地区有效地模拟物理距离。首席研究员积极与合作伙伴组织合作，研究离散裂缝网络（DFN）模型和天然裂缝网络的连通性。物理距离最终可以被认为是对象（人）之间的连接问题：通过将裂缝替换为人，网络中每个裂缝的中心代表一个个体，裂缝的大小成为物理距离的度量。骨折（人）也可以分配不同的属性，例如，积极或无症状的条件。其结果将是具有不同基础条件的人之间的连接路径。结合一个强大的随机（概率）框架，DFN模型，因此非常适合模拟场景的物理距离适用于大规模的地区，而在同一时间占人口密度的变化。我们相信，拟议的研究计划将大大提高加拿大在使用离散模拟应用于随机（随机）情景方面的知识和专业知识，并提供一种新的方法来测量，从而在人口稠密地区实施物理距离，并对其有效性进行更准确的预测，以减少疫情的传播。