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.

保持身体距离（或社交距离）是公共卫生专家和政府官员提出的范例，是减缓Covid-19疫情传播的最佳工具，尽管是间接的。我们认为保持物理距离是一种间接工具，因为只有疫苗才能直接针对导致疫情爆发的病毒，并使人们对其免疫。世界各国都实施了严格的封锁程序，以最大限度地保持身体距离。公共卫生专家和民选官员知道，这些解决方案是暂时的，从长远来看在经济上是不可行的。随着我们开始考虑放松封锁措施，有必要在人口密集的大城市地区有效地模拟身体距离。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物体（人）之间的连通性问题：通过用人代替裂缝，网络中每条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）也可以被赋予不同的属性，例如，阳性或无症状状态。结果将是具有不同潜在条件的人之间的连接路径。因此，结合稳健的随机（概率）框架，DFN模型非常适合模拟应用于大尺度地区的物理距离情景，同时考虑人口密度的变化。我们认为，拟议的研究计划将大大提高加拿大在将离散模拟应用于随机（随机）情景方面的知识和专业知识体系，并提供一种新的方法来测量并因此在人口稠密地区实施物理距离，并对其减少疫情传播的有效性做出更准确的预测。