With an increasingly urbanized and mobile population, the likelihood of a worldwide pandemic is increasing. With rising input sizes and strict deadlines for simulation results, e.g., for real-time planning during the outbreak of an epidemic, we must expand the use of high performance computing (HPC) approaches and, in particular, push the boundaries of scalability for this application area. EpiSimdemics simulates epidemic diffusion in extremely large and realistic social contact networks. It captures dynamics among co-evolving entities. Such applications typically involve large-scale, irregular graph processing, which makes them difficult to scale due to irregular communication, load imbalance, and the evolutionary nature of their workload.