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（或社会疏远）是公共卫生专家和公职人员提出的范式，尽管是间接的，但可以放缓Covid-19-19-19-19的爆发。我们考虑物理距离间接工具，因为只有疫苗才能直接针对导致爆发的病毒，并使人们对其免疫。在世界各地，已实施严格的锁定程序，以最大程度地提高身体距离。公共卫生专家和民选官员知道，这些解决方案是暂时的，并且在长期内不可行。当我们开始考虑放松锁定措施时，有必要在人口稠密的城市地区有效地模拟物理距离。首席研究者已经与合作伙伴组织积极合作，研究了自然断裂网络的离散断裂网络（DFN）模型和连通性。最终可以将物理距离视为对象之间的连通性问题（人）：通过用人代替裂缝，网络中每个骨折的中心代表一个人，而骨折的大小变成了物理距离的量度。裂缝（人）也可以分配不同的特性，例如阳性或无症状条件。结果将是具有不同潜在条件的人之间的连通性路径。因此，与强大的随机（概率）框架相结合，DFN模型非常适合模拟用于大规模区域的物理距离的场景，同时又考虑了人口密度的变化。我们认为，拟议的研究计划将大大增强加拿大在使用适用于随机（随机）场景的离散模拟时的知识和专业知识，并提供了一种新的方法来衡量并在人口稠密领域的背景下实施物理距离，并且为了更准确地预测其有效性，以减少暴发的传播。