The Limits to Learning an SIR Process: Granular Forecasting for Covid-19
The Limits to Learning an SIR Process: Granular Forecasting for Covid-19
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学习 SIR 流程的局限性：Covid-19 的精细预测
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
A. Zheng
中科院分区：
文献类型：
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作者：
Jackie Baek;V. Farias;Andreea Georgescu;R. Levi;Tianyi Peng;Deeksha Sinha;Joshua Wilde;A. Zheng
已经开展了多种预测工作，以支持对正在进行的新冠肺炎疫情的管理。这些努力通常依赖于SIR进程的一种变体，并表明在流行病的早期阶段建立有效的预测是具有挑战性的。这可能令人惊讶，因为这些模型依赖于少量的参数，通常提供了对疾病演变的极好的回顾拟合。出于这种动机，我们分析了估计SIR过程的限制。我们表明，没有一个公正的估计者希望了解这一过程，直到对疫情进行了足够的观察，使人们接近达到新感染高峰的三分之二。我们的分析提供了对正规化战略的洞察，该战略允许在同时和异步演变的流行病中进行有效学习。这一策略已被用来对新冠肺炎疫情进行准确、精细的预测，并在美国一个大州得到了大规模的实际应用。
A multitude of forecasting efforts have arisen to support management of the ongoing COVID-19 epidemic. These efforts typically rely on a variant of the SIR process and have illustrated that building effective forecasts for an epidemic in its early stages is challenging. This is perhaps surprising since these models rely on a small number of parameters and typically provide an excellent retrospective fit to the evolution of a disease. So motivated, we provide an analysis of the limits to estimating an SIR process. We show that no unbiased estimator can hope to learn this process until observing enough of the epidemic so that one is approximately two-thirds of the way to reaching the peak for new infections. Our analysis provides insight into a regularization strategy that permits effective learning across simultaneously and asynchronously evolving epidemics. This strategy has been used to produce accurate, granular predictions for the COVID-19 epidemic that has found large-scale practical application in a large US state.