论文标题
基于Seair模型的CoVID-19的孵育周期分布
Inferring incubation period distribution of COVID-19 based on SEAIR Model
论文作者
论文摘要
为了减少传统基于调查的方法的偏见，本文提出了一种基于流行病模型的方法来推断使用公开报道的确认案例编号，以推理Covid-19的孵育周期分布。我们构建了一个流行病模型，即Seair，并利用Seair描绘的动态传播过程来估计八个受影响国家暴露个人的每一天的发作概率。基于这些估计，已经揭示了Covid-19的一般孵育概率分布。提出的方法可以避免传统基于调查的方法的几种偏见。但是，由于该方法基于数学模型的性质，推断结果对参数的设置有些敏感。因此，应根据对所研究流行病的一定理解来合理实践该方法。
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.
