Uncertainty Quantification (UQ) is the subject devoted to quantifying all of the uncertainties when you attempt to combine complex scientific models and data to learn about the real world. We know that neglecting some of these uncertainties can bias inference, throw off predictions and is particularly problematic in decision making contexts. The UQ community has been around since the early 1980's and a number of core methods and tools exist for doing it. Fast forward to February and March 2020 and using models to inform decision makers entered the public conciousness like never before as we used the best tools we had to understand and predict the trajectory of COVID-19 in the UK. The UQ community waved the flag for the importance of proper uncertainty quantification, yet despite a number of projects to synthesise UQ for COVID-19 modelling getting off the ground through RAMP and later EPSRC, UQ for COVID-19 never really broke through into SPI-M/SAGE. In this talk I will overview some of the key ideas in UQ and why they proved (and still prove) so difficult to implement for (our) COVID-19 models. I'll view the problem through the lens of how we might be ready to include UQ right from the start of the next pandemic to help modellers better calibrate their models and report more accurate uncertainties to feed into policy support.