基于SEAIR模型推断COVID-19的潜伏期分布
Inferring incubation period distribution of COVID-19 based on SEAIR Model
摘要：
为了减少传统的基于调查的方法的偏差，本文提出了一种基于流行病模型的方法，利用公开报告的确诊病例数来推断COVID-19的潜伏期分布。我们构建了一个流行病模型，即SEAIR，并利用SEAIR描述的动态传播过程，估计在8个受影响的国家暴露个人每天的发病概率。基于该等估计，COVID-19的一般潜伏概率分布已被揭示。该方法可以避免传统的基于调查的方法的一些偏见。然而，由于该方法基于几何模型的性质，推断结果对参数的设置有些敏感。因此，应在对所研究的流行病有一定了解的基础上，合理运用该方法。
英文摘要：
To reduce the biases of traditional survey-based methods, this paper proposes an epidemic model-based approach to inference the incubation period distribution of COVID-19 utilizing the publicly reported confirmed case number. We construct an epidemic model, namely SEAIR, and take advantage of the dynamic transmission process depicted by SEAIR to estimate the onset probability in each day of exposed individuals in eight impacted countries. Based on these estimations, the general incubation probability distribution of COVID-19 has been revealed. The proposed method can avoid several biases of traditional survey-based methods. However, due to the mathematical-model-based nature of this method, the inference results are somewhat sensitive to the setting of parameters. Therefore, this method should be practiced reasonably on the basis of a certain understanding of the studied epidemic.
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