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.
翻译:对当前流行病的传染病预测历来是作为数字指标来进行、传播和评价的。虽然预测这些数字指标对于评估该疾病的负担很有价值,但我们认为,传播该流行病的未来趋势(形状说明)也有价值,例如,如果病例保持平缓或预计会激增,为了确保所传播的信息有用,我们需要能够评估所预测的形状与地面真相形状的匹配程度。我们不是将这一问题作为分类问题处理(以美元为单位),而是将数字预测转化为“shapelet”-空间代表。在这个表述中,每个方面与该流行病未来趋势的相似性(形状说明) -- -- 例如,如果病例将保持平缓,或将出现剧增。我们还需要能够评估所预测的形状与地面真相形状相匹配的程度如何。我们的代表能够合理地了解COVID-19案件和死亡时间序列中的趋势。我们通过这种表述,我们也可以确定一个具有多重空间分布的模型,我们也可以确定一个衡量和预测模型的多重比例。我们也可以确定一个衡量和预测模型的多重比例。我们也可以确定一个衡量和模型的模型。我们确定一个显示一个稳定的模型。