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
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.