We present a robust data-driven machine learning analysis of the COVID-19 pandemic from its early infection dynamics, specifically infection counts over time. The goal is to extract actionable public health insights. These insights include the infectious force, the rate of a mild infection becoming serious, estimates for asymtomatic infections and predictions of new infections over time. We focus on USA data starting from the first confirmed infection on January 20 2020. Our methods reveal significant asymptomatic (hidden) infection, a lag of about 10 days, and we quantitatively confirm that the infectious force is strong with about a 0.14% transition from mild to serious infection. Our methods are efficient, robust and general, being agnostic to the specific virus and applicable to different populations or cohorts.

中文翻译：

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从早期感染动力学机器学习 COVID-19 的现象学

我们从 COVID-19 大流行的早期感染动态，特别是随时间推移的感染计数，对 COVID-19 大流行进行了强大的数据驱动机器学习分析。目标是提取可操作的公共卫生见解。这些见解包括传染力、轻度感染变得严重的速度、对无症状感染的估计以及随着时间的推移对新感染的预测。我们关注从 2020 年 1 月 20 日第一次确诊感染开始的美国数据。我们的方法揭示了显着的无症状（隐藏）感染，大约有 10 天的滞后，我们从数量上确认传染力很强，从轻度至重度感染。我们的方法高效、稳健且通用，与特定病毒无关，适用于不同的人群或队列。