In the early stages of pandemics, mathematical models can provide invaluable insights into transmission dynamics, help predict disease spread, and evaluate control measures. However models are only valid within the limits of the parameters examined. As reliable parameter estimates are rarely available early in a new pandemic, best-guess estimates are used, which need to be constantly reviewed as new real-world data emerge. Estimating how sensitive the model is to changes in its parameters can provide useful information about validity when parameters are uncertain. Interpreting models without considering these factors can lead to flawed inferences, which can have far reaching effects when they inform public health policy. We illustrate this, here, using an example from the Hellewell et al. model published in Lancet Global Health, 2020. This model suggested that case detection and contact tracing was unlikely to be an effective strategy for pandemic control, and is likely to have informed UK government strategy to cease testing and contact tracing on the 12th March 2020. We show that this model is very sensitive to the parameter of delay between case detection and isolation. We demonstrate that when the delay scenario parameter is changed to a median of 1 day, which is very plausible in the context of current rapid testing, this model predicts a >80% probability of controlling the epidemic within 12 weeks, with relatively modest contact tracing. These results suggest that rapid testing, contact tracing and isolation could be effective strategies to control transmission
