Massively Parallel Simulations of Spread of Infectious Diseases over Realistic Social Networks

Controlling the spread of infectious diseases in large populations is an important societal challenge. Mathematically, the problem is best captured as a certain class of reaction-diffusion processes (referred to as contagion processes) over appropriate synthesized interaction networks. Agent-based modelshave been successfully used in the recent past to study such contagion processes. We describe EpiSimdemics, a highly scalable, parallelcode written in Charm++ that uses agent-based modelingto simulate disease spreads over large, realistic, co-evolving interaction networks. We present a new parallel implementation of EpiSimdemics that achieves unprecedented strong and weak scaling on different architectures --- Blue Waters, Coriand Mira. EpiSimdemics achieves five times greater speedup than the second fastest parallel code in this field. This unprecedented scaling is an important step to support the long term vision of realtime epidemic science. Finally, we demonstrate the capabilities of EpiSimdemics by simulating the spread of influenza over a realistic synthetic social contact network spanning the continental United States (~280 million nodes and 5.8 billion social contacts).