  Infectious disease forecasting for ongoing epidemics has been traditionally
performed, communicated, and evaluated as numerical targets - 1, 2, 3, and 4
week ahead cases, deaths, and hospitalizations. While there is great value in
predicting these numerical targets to assess the burden of the disease, we
argue that there is also value in communicating the future trend (description
of the shape) of the epidemic -- for instance, if the cases will remain flat or
a surge is expected. To ensure what is being communicated is useful we need to
be able to evaluate how well the predicted shape matches with the ground truth
shape. 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 dimension
corresponds to the similarity of the shape with one of the shapes of interest
(a shapelet). We prove that this representation satisfies the property that two
shapes that one would consider similar are mapped close to each other, and vice
versa. We demonstrate that our representation is able to reasonably capture the
trends in COVID-19 cases and deaths time-series. With this representation, we
define an evaluation measure and a measure of agreement among multiple models.
We also define the shapelet-space ensemble of multiple models as the mean of
their shapelet-space representations. We show that this ensemble is able to
accurately 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 of
the reliability of the forecast.
