Graduate School of Data Science, Shiga University, Hikone, Shiga, Japan.
The Center for Data Science Education and Research, Shiga University, Hikone, Shiga, Japan.
PLoS One. 2020 Oct 28;15(10):e0241170. doi: 10.1371/journal.pone.0241170. eCollection 2020.
Estimating the percentages of undiagnosed and asymptomatic patients is essential for controlling the outbreak of SARS-CoV-2, and for assessing any strategy for controlling the disease. In this paper, we propose a novel analysis based on the birth-death process with recursive full tracing. We estimated the numbers of undiagnosed symptomatic patients and the lower bound of the number of total infected individuals per diagnosed patient before and after the declaration of the state of emergency in Hokkaido, Japan. The median of the estimated number of undiagnosed symptomatic patients per diagnosed patient decreased from 1.7 to 0.77 after the declaration, and the median of the estimated lower bound of the number of total infected individuals per diagnosed patient decreased from 4.2 to 2.4. We will discuss the limitations and possible expansions of the model.
估算未确诊和无症状患者的比例对于控制 SARS-CoV-2 的爆发以及评估任何疾病控制策略至关重要。在本文中,我们提出了一种基于具有递归完全跟踪的生死过程的新分析方法。我们估计了日本北海道宣布紧急状态前后未确诊的有症状患者数量以及每位确诊患者的总感染人数下限。宣布紧急状态后,每位确诊患者的未确诊有症状患者数量的中位数从 1.7 下降到 0.77,每位确诊患者的总感染人数下限的中位数从 4.2 下降到 2.4。我们将讨论模型的局限性和可能的扩展。