Mathematical Modeling of Dengue Viral Infection

In recent years, dengue viral infection has become one of the most widely-spread mosquito-borne diseases in the world, with an estimated 50-100 million cases annually, resulting in 500,000 hospitalizations. Due to the nature of the immune response to each of the four serotypes of dengue virus, secondary infections of dengue put patients at higher risk for more severe infection as opposed to primary infections. The current hypothesis for this phenomenon is antibody-dependent enhancement, where strain-specific antibodies from the primary infection enhance infection by a heterologous serotype. To determine the mechanisms responsible for the increase in disease severity, we develop mathematical models of within-host virus-cell interaction, epidemiological models of virus transmission, and a combination of the within-host and between-host models. The main results of this thesis focus on the within-host model. We model the effects of antibody responses against primary and secondary virus strains. We find that secondary infections lead to a reduction of virus removal. This is slightly different than the current antibody-dependent enhancement hypothesis, which suggests that the rate of virus infectivity is higher during secondary infections due to antibody failure to neutralize the virus. We use the results from the within-host model in an epidemiological multi-scale model. We start by constructing a two-strain SIR model and vary the parameters to account for the effect of antibody-dependent enhancement.