论文标题
“回到未来”对19卷的预测
"Back to the future" projections for COVID-19 surges
论文作者
论文摘要
我们认为，从较早的Covid-19-19，可以使用来自较早的Covid-19的国家的信息来告知另一个国家的当前模型，然后产生我们所谓的回到现实（BTF）预测。我们表明，这些投影可用于在每日感染曲线的拐点之前准确预测未来的Covid-19潮流。我们显示，在世界各地所有人口稠密的大陆的12个不同国家中，我们的方法通常可以预测传统方法总是不会预测未来的浪潮的情况下的未来激增。但是，正如预期的那样，由于新变体的出现，BTF预测无法准确预测激增。为了产生BTF预测，我们利用了与响应教练SIR模型相结合的异步时间序列的匹配方案。
We argue that information from countries who had earlier COVID-19 surges can be used to inform another country's current model, then generating what we call back-to-the-future (BTF) projections. We show that these projections can be used to accurately predict future COVID-19 surges prior to an inflection point of the daily infection curve. We show, across 12 different countries from all populated continents around the world, that our method can often predict future surges in scenarios where the traditional approaches would always predict no future surges. However, as expected, BTF projections cannot accurately predict a surge due to the emergence of a new variant. To generate BTF projections, we make use of a matching scheme for asynchronous time series combined with a response coaching SIR model.
