Effective use of models in intelligence-to-decision workflows within and across One Health sectors

Abstract Decision-makers in public service face uncertainty. Operational management or policy decisions need to be made about system-level ecological and sociological processes that are complex, poorly understood, and change over time. Relying on intuition, evidence, and experience for robust decision-making is challenging without a formal assimilation of these elements (a model), especially when the decision needs to consider potential impacts if an action is or is not taken. Models can provide assistance to this challenge, but effective use of modeling tools in decision-making can be difficult due to lack of trust, expertise, and transparency and consistency in modeling methods and results. We conducted 41 semi-structured interviews of researchers, operational managers, and policy decision-makers with direct experience in intelligence-to-decision workflows involving models within and across human health, animal health, or environmental sectors (One Health sectors). Qualitative analysis of the interview data reveals important ingredients for effective development and use of quantitative models in informing management or policy decisions in One Health sectors. Two of the priorities we identified for implementing improved workflows include establishing different standards for development of modeling intelligence before or after decisions are made and investment in knowledge brokers with modeling expertise working in teams with decision-makers. These and other priorities we identified are important considerations for developers and users of modeling intelligence in a broad range of institutional contexts.