A Hierarchical Consilient Algorithm for Epidemic Risk Prediction within the One Health Framework

Abstract

Emerging and re-emerging infectious diseases increasingly arise from the interaction of ecological disruption, human and animal health, social vulnerability, infrastructure fragility, and collective behavior. Although the One Health paradigm has strengthened interdisciplinary thinking, many operational frameworks still aggregate heterogeneous risk factors without clearly distinguishing their causal position. This article proposes a hierarchical consilient algorithm for epidemic risk prediction within the One Health framework. The model differentiates primary structural drivers, secondary mediators and amplifiers, and epidemiological outcomes, thereby translating complex multidimensional risk into a more interpretable and actionable structure. The proposed framework is intended to support early warning, preparedness, prioritization of interventions, and adaptive surveillance, particularly in fragile, conflict-affected, and environmentally stressed settings.