论文标题
机器从早期感染动力学中学习Covid-19的现象学
Machine Learning the Phenomenology of COVID-19 From Early Infection Dynamics
论文作者
论文摘要
我们从其早期感染动力学中提出了COVID-19大流行的强大数据驱动的机器学习分析，特别是随着时间的推移感染计数。目的是提取可行的公共卫生见解。这些见解包括感染力，轻度感染的速度变得严重，对随着时间的推移的新感染的估计以及对新感染的预测。我们关注从2020年1月20日首次确认感染开始的美国数据。我们的方法显示出明显的无症状（隐藏）感染，滞后约10天，我们进行了定量确认，感染力很强，从轻度感染到严重感染约为0.14％。我们的方法是有效，健壮和一般的，对特定病毒不可知，适用于不同的人群或同伙。
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.
