| Abstract | The most common viral dynamics models for analyzing viral infections as- sume even spatial distribution between virus particles and uninfected target cells. However, throughout an infection, the spatial distribution of virus and cells changes. Initially, virus and infected cells are localized so that a target cell in an area with lower virus presence will be less likely to be infected than a cell close to a location of viral production. A density-dependent infection rate has the potential to improve models that treat cellular infection probability as constant. Saturated Incidence, Beddington-DeAngelis, and Crowley Martin models were used to understand how density dependent parameters could impact the severity of an influenza infection. Parameter values were varied to understand impli- cations of density constraints. For low density dependence, a steeper increase in virus and greater viral peak was predicted. Initial localization of infected cells likely slows the progression of infection. The model demonstrates that ac- counting for density dependence when analyzing influenza infection severity can result in an altered expectation for viral progression. A density-dependent infec- tion rate provides a more complete view of the interaction between infected and uninfected cells. |
