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Infectious disease forecasting for ongoing epidemics has been traditionallyperformed, communicated, and evaluated as numerical targets - 1, 2, 3, and 4week ahead cases, deaths, and hospitalizations. While there is great value inpredicting these numerical targets to assess the burden of the disease, weargue that there is also value in communicating the future trend (descriptionof the shape) of the epidemic -- for instance, if the cases will remain flat ora surge is expected. To ensure what is being communicated is useful we need tobe able to evaluate how well the predicted shape matches with the ground truthshape. Instead of treating this as a classification problem (one out of $n$shapes), we define a transformation of the numerical forecasts into a``shapelet''-space representation. In this representation, each dimensioncorresponds to the similarity of the shape with one of the shapes of interest(a shapelet). We prove that this representation satisfies the property that twoshapes that one would consider similar are mapped close to each other, and viceversa. We demonstrate that our representation is able to reasonably capture thetrends in COVID-19 cases and deaths time-series. With this representation, wedefine an evaluation measure and a measure of agreement among multiple models.We also define the shapelet-space ensemble of multiple models as the mean oftheir shapelet-space representations. We show that this ensemble is able toaccurately predict the shape of the future trend for COVID-19 cases and trends.We also show that the agreement between models can provide a good indicator ofthe reliability of the forecast.

Shape-based Evaluation of Epidemic Forecasts