We present our work on the development of a dynamic population model of Influenzae A infection in epithelial tissue. In this model we took into account diffusional effects of the virus in vivo and integrated them with a cellular automata simulation to understand the effect of localized interactions on the dynamics of an Influenza infection on a host. We tested the impact of incorporating a virus diffusion model into the cellular automata simulation and what parameters were particularly important to the simulation. Finally, we explored how the diffusion of virus can influence complications in an infection by presenting a hypothetical case study of an immuno-deficient host. This case study shows a dramatic increase in the fraction of dead cells, as it almost doubles at its maximum to 81%. In this test case, the subject does recover, although it takes much longer than the 12 days needed to clear the normal virus in a normal host to fully recover. Additionally, by the 12th day a slight increase in infected cells can be observed. This re-infection is due to the virus lingering in the mucus layer above the epithelial cells. If we ignored the diffusional aspects of the infection, and only utilized cell to cell infection mechanisms, we would have been unable to observe this sort of dynamic.