Given the current COVID-19 public health crisis that has affected humans worldwide, we sought to model the spread of infection via random simulation of a form of Brownian motion, where people, simulated as dots, move through a crowded area past one another. If an uninfected person passes within a fixed distance of an infected person (in COVID's case, this distance could be the CDC-recommended threshold distance of 6 feet) the uninfected person is subject to a nonzero probability of infection, acquiring the disease randomly. While classical computers can simulate this randomness with pseudo-random techniques, their deterministic approach to computation does not allow them to truly generate random simulations in a way that is authentic to the real world, and this is where our quantum approach comes in. We use a fixed probability of infection (10% in the model provided here) to rotate a person's corresponding "infection vector" from |psi> = |0> (no infection) to |psi> = c1 |0> + c2 |1> where |c2|^2 represents the probability of infection. We then measure this pure state to be either |0> or |1> with their corresponding appropriate proabilities of non-infection or infection with genuine randomness, updating their infection status via a color change in the animated model (black = not infected, red = infected). In the future, we hope to expand this to more complex models of random infection, beyond the simple binomially distributed infection variable employed here to more complex models of propagation studied in modern epidemiology.