Using agent-based simulation to understand populatation dynamics and coevolution in host-pathogen relationships

The development of anti-biotic resistant strains of bacteria and the nearly annual emergence of new strains of influenza virus are evidence of the rapid adaptation of pathogens to environmental pressures. The ability to predict the outcome of a long-term host-pathogen interaction could significantly improve public health decisions. Starting from the premise that all interactions between hosts and pathogens are stochastic in nature, we developed a generic agent based simulation of a population of hosts infected by a population of pathogens. Our simulation suggests that a host population is not intrinsically stable absent negative feedback loop mechanisms. We show that even in the face of a specific pathogen pressure, the outcome of a given initial host-pathogen community can be expressed only as a set of probabilities, unlike the prediction of traditional mathematical models constructed in the language of differential calculus.