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. Around the world countries have imposed strict lockdown procedures in an effort to maximize physical distancing. However, 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.
Fracture networks are highly developed models of connectivity between fractures. Thus if fractures are used to represent people, physical distancing can be considered a problem of connectivity between fracture objects (people), where 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 probabilistic framework, connectivity 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.