There are no results for define quantitative risk models The COVID$-19$ crisis has highlighted the role of quantitative models in decision making, particularly during the pandemic. Models provide information that can present a framework for understanding a situation but are not crystal balls that state with certainty what will happen. They help decision makers navigate uncertainty by structuring data by restricting variables to those that matter for a particular question. Models are well suited to exposing sensitivities$,$ showing how even small changes in key assumptions can produce large variations in outcomes, helping decision makers establish priorities. By establishing sensitivities$,$ models pinpoint areas for investment of effort or money to reduce uncertainty. Models can be broken down into three main components: raw data, assumptions that define what the model does with the data, and final output. The relative importance of assumptions and data varies by model, and failure to do so can lead to poorly informed decisions. A model is only as good as its underlying data, and in a time of extreme uncertainty, such as a global pandemic, poor data leads to poor output from a model. When using models to make decisions or interpreting their outputs, key questions to ask include how has this model simplified the world, what inputs does the model require, how knowable, certain, and stable those inputs are, what the outputs tell us$,$ and how users have engaged with this model in the process of making decisions. Satisfactory answers to these questions will foster a better understanding of potential future scenarios and better decisions in an evolving and uncertain situation. As highlighted in the case study, making decisions in the face of uncertainty is challenging, particularly during a pandemic. Examine how quantitative risk models can be of significance to planners. Make use of relevant examples.