利用 COVID-19 特异性模型计算无症状感染者以估计群体免疫阈值。

Jawaharlal Nehru Centre for Advanced Scientific Research, Jakkur, Bangalore, India.

Indian Institute of Science, Bengaluru, India.

PLoS One. 2020 Dec 16;15(12):e0242132. doi: 10.1371/journal.pone.0242132. eCollection 2020.

A quantitative COVID-19 model that incorporates hidden asymptomatic patients is developed, and an analytic solution in parametric form is given. The model incorporates the impact of lock-down and resulting spatial migration of population due to announcement of lock-down. A method is presented for estimating the model parameters from real-world data, and it is shown that the various phases in the observed epidemiological data are captured well. It is shown that increase of infections slows down and herd immunity is achieved when active symptomatic patients are 10-25% of the population for the four countries we studied. Finally, a method for estimating the number of asymptomatic patients, who have been the key hidden link in the spread of the infections, is presented.

我们建立了一个包含隐性无症状患者的定量 COVID-19 模型，并给出了参数形式的解析解。该模型纳入了封锁措施的影响以及因封锁措施宣布而导致的人口的空间迁移。提出了一种从实际数据中估计模型参数的方法，并表明该方法能够很好地捕捉到观察到的流行病学数据中的各个阶段。结果表明，当四个国家中活跃的有症状患者占人口的 10-25%时，感染的增长率会减缓，并实现群体免疫。最后，提出了一种估计无症状患者数量的方法，无症状患者一直是感染传播的关键隐性环节。