Numerous models exist with the goal of modeling the propagation of COVID-19 and other epidemics or pandemics. These models include the SEIR (Susceptible-Exposed-Infected-Removed) model, Agent Based Models (ABM), and continuum models of reaction diffusion. Each of these modelling approaches contain multiple and sometimes intractable variables, resulting in large uncertainties in outcomes, thus restricting their utility in guiding local, national, and international governmental decisions for managing and controlling pandemics. There exists a need for a simple, fast, deterministic, scalable, and accurate model that captures the dominant physics of pandemic propagation. Here we propose such a model by adapting a physical earthquake/aftershock model to the COVID19 problem. The aftershock model revealed the physical basis for the Epidemic Type Aftershock Sequence (ETAS) model as a highly non-linear diffusion process, thus permitting a grafting of the underlying physical equations into a formulation for calculating infection pressure propagation in a pandemic-type model. Model results show excellent correlations with observed infection rates for all cases studied to date. In alphabetical order, these include Austria, Belgium, Brazil, France, Germany, Italy, Melbourne (AU), New Zealand, Spain, Sweden, Switzerland, UK, and the USA. Importantly, the model is predominantly controlled by one parameter α, which modulates societal compliance to governmental actions. We find that differing societal compliance between countries results in dramatically different outcomes given similar infection sources. These results provide an intuition-based approach to designing and implementing mitigation measures, with predictive capabilities for various mitigation scenarios.