Early Warning at Scale: Prospective Validation of the Strategic Helix Platform Across 209 Sovereign States and the Iran Contagion Finding

Early Warning Systems are rarely validated prospectively at global scale against events they did not anticipate. This article presents a 62-day prospective validation of the Strategic Helix Early Warning Platform (EWP) across 209 sovereign states (January 1–March 3, 2026). The system achieved balanced accuracy (BA) of 83.3% (95% CI: 75.0%–90.3%), precision of 100%, and zero false positives — every alarm issued corresponded to a verified escalation event. A continuous-score AUROC of 0.894 (Hanley 95% CI: 0.824–0.965) enables direct comparison with probabilistic early warning systems. Of twelve false negatives, eight form a non-random cluster: all host U.S. military installations struck by Iranian-initiated attacks from 28 February 2026, revealing a structural specification gap in country-level EWS models blind to the network dynamics of military alliances and proxy warfare — a contagion mechanism without direct historical precedent at this scale. A Network Propagation Rule is proposed, formalised as a noisy-OR overlay on the continuous risk score. Applied to the observation window, its geopolitically constrained specification — the ten states hosting U.S. military bases within Iran’s ~2,500 km strike envelope — raises the empirical AUROC from 0.894 (bootstrap 95% CI: 0.815–0.963) to 0.992 (bootstrap 95% CI: 0.982–1.000; paired DeLong p = .010), recovering all eight contagion cases at a cost of two false positives (BA = 93.9%, precision = 94.1%, specificity = 98.8%). Because eight of the envelope’s ten members are observed escalation cases, this gain is reported as a quantified estimate of the rule’s potential value on the window that motivated its specification, rather than validated foresight; a structurally specified variant flagging all 28 of the roughly thirty U.S.-base host states worldwide, blind to outcomes, yields AUROC 0.929–0.973 depending on propagation weight. The augmented figure of 0.992 defines the system’s reference performance going forward, to be validated in future research. Validation was conducted through a multi-agent AI architecture incorporating an adversarial agent and continuous human supervision.