We present a  data-driven machine learning analysis of  COVID-19 from itsearly infection dynamics, with the goal of extracting actionable public health insights.  We focus on the transmission dynamics in the USA starting from the first confirmed infection on January 21 2020. We find that COVID-19 has a strong infectious force if left unchecked, with a doubling time of under 3 days. However it is not particularly virulent. Our methods may be of generalinterest .
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 .
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robust
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the
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from its
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early
pandemic from its early
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with the goal of extracting
specifically infection counts over time. The goal is to extract
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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.
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the transmission dynamics in the USA
USA data
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21
20
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We find that COVID-19 has a strong infectious force if left unchecked, with a doubling time of under 3 days. However it is not particularly virulent. Our methods may be of generalinterest
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