In this thesis we formulate and analyze a structured population model, with infectious disease dynamics, based on a similar life-cycle as with individuals of the Hamilton-May model. Each individual is characterized by a strategy vector (state dependent dispersal), and depending on the infectious status of the individual, it will use a strategy accordingly. We begin by assuming that every individual in the population has the same strategy, and as the population equilibriates we consider a mutant, with it's own strategy, entering the population, trying to invade. We apply the theory of Adaptive dynamics to model the invasion fitness of the mutant, and to analyze the evolution of dispersal. We show that evolutionary branching is possible, and when such an event happens, the evolutionary trajectories, described by the Canonical equation of Adaptive dynamics, of two strategies evolve into the extinction of one branch. The surviving branch then evolves to the extinction of the disease.
