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.
翻译:我们争论说,早期COVID-19激增的国家提供的信息可以用来为另一个国家目前的模型提供参考,然后产生我们所谓的回向未来预测。我们表明,这些预测可以用来准确预测未来的COVID-19激增,然后是每天感染曲线的切换点。我们从全世界所有人口稠密的大陆的12个不同国家看,我们的方法往往可以预测未来潮流,而传统方法总是预测不会发生未来的潮流。然而,正如预期的那样,BTF的预测无法准确预测新变异的出现导致的潮流。为了生成BTF的预测,我们利用一个匹配计划,将无节制的时间序列与反应辅导SIR模型结合起来。