We study a prescriptive model for end-to-end design of a supply chain for medical countermeasures (MCM) to defend against bioattacks. We model the defender's MCM inventory prepositioning and dispensing capacity installation decisions, attacker's move, and defender's adjustable shipment decisions, so as to minimize inventory and life loss costs, subject to population survivability targets. We explicitly account for the strategic interaction between defender's and attacker's actions, assuming information transparency. We consider the Affinely Adjustable Robust Counterpart (AARC) to our problem, which enables us to deal with realistic networks comprising millions of nodes. We provide theoretical backing to the AARC performance by proving its optimality under certain conditions. We conduct a high-fidelity case study on the design of a MCM supply chain with millions of nodes to guard against anthrax attacks in the United States. We calibrate our model using data from a wide variety of sources, including literature and field experiments. We produce policy insights that have been long sought after but elusive up until now.