While mathematical models of disease transmission are widely used to inform public health decision-makers globally, the uncertainty inherent in results are often poorly communicated. We outline some potential sources of uncertainty in epidemic models, present traditional methods used to illustrate uncertainty and discuss alternative presentation formats used by modelling groups throughout the COVID-19 pandemic. Then, by drawing on the experience of our own recent modelling, we seek to contribute to the ongoing discussion of how to improve upon traditional methods used to visualise uncertainty by providing a suggestion of how this can be presented in a clear and simple manner. Highlights • Clearly communicating uncertainty to decision-makers is vital, but uncertainty is often poorly reflected in visualisations. • Traditional summary statistics often hide the nuances of individual trajectories and important features of the epidemic. • We present individual trajectories of ICU demand alongside individual metrics of magnitudes, peak timings and ICU capacity. • To engage audiences with uncertainty in epidemic forecasts, we use colour to link each epidemic trajectory to key metrics.
【저자키워드】 COVID-19, Decision-making, transmission modelling, communicating uncertainty, Data visualisation, 【초록키워드】 public health, COVID-19 pandemic, ICU, Epidemic, trajectory, group, disease transmission, Metrics, while, feature, the epidemic, IMPROVE, reflected, contribute, inherent, mathematical, engage, nuance, Traditional, traditional method, 【제목키워드】 Epidemic, Communicating,