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

COVID-19 Research Area(s): Epidemiology & Public Health, Social Impacts

The research aims to address a problem that has deep and immediate societal and economic ramifications. Our society and economy are global; however, they rest on a foundation of finite spaces and densely populated urban and working environments. At the same time, world-scale processes and global expansion provide a means for increased connectivity between national entities. Connectivity therefore exists at the small scale as well as the large scale: from cities, to nations, to our World as a global entity. Pandemics disrupt connectivity and therefore weaken the socio-economical foundation upon which the modern World is founded. Digital connectivity integrates with and supports many aspects of our society and economy, but recent events have demonstrated that digital connectivity cannot replace physical connectivity, and in person interactions are required to sustain the economy at the national and world scale.

Public health and elected officials have been urging people to keep at a distance of at least 2 metres to help minimise contact with others. Physical distancing is thus inversely proportional to social connectivity. In this context, we believe it is possible to develop an alternative method to measure the impact of physical distancing based on the connectivity principles generally used to solve rock engineering problems and the application of a discrete modelling method, such as the discrete fracture network (DFN) approach. Connectivity is a well know phenomenon in rock engineering; natural fracture connectivity defines the strength, deformability, and fluid conductivity of a rock mass; the larger the connectivity, the lower the resistance of a rock mass to sustain larger induced stresses, and the larger its fluid conductivity. DFN models can be used as a stand-alone tool or integrated within more complex geomechanical simulations to create realistic virtual rock masses (digital twins) under static or dynamic conditions. We believe that the same principles that govern DNF modelling could be extended and applied to study public health problems to provide risk-based scenarios in which key parameters are varied either according to specific functions or randomly.

The proposed research has two key components and objectives, namely:

1. Development of DFN based risk maps to represent conditions of potentially safe or unsafe physical distancing, and development of a quantitative classification system of physical distancing using connectivity principles
2. Integrate the results of the DFN analysis with virtual/mixed reality (VR/MR) tools to provide an immersive experience when visualizing and comparing datasets. The DFN analysis results could potentially be integrated with solid structures extracted and / or imported into 3D mapping software