论文标题
使用Covid-19特定模型估算隐藏的无症主义者，群免疫阈值和锁定效果
Estimating Hidden Asymptomatics, Herd Immunity Threshold and Lockdown Effects using a COVID-19 Specific Model
论文作者
论文摘要
开发了一种融合隐藏无症状患者的定量COVID-19模型，并给出了参数形式的分析解决方案。该模型纳入了锁定的影响以及由于公告锁定而导致的人口空间迁移。提出了一种方法来估计实际数据中的模型参数。结果表明，当有症状的患者占我们研究的欧洲国家的人口4-6％时，感染的增加会减慢，当受感染总数为50-56 \％时。最后，提出了一种估计无症状患者数量的方法，这些方法是感染传播中关键的隐藏联系的方法。
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 lockdown and resulting spatial migration of population due to announcement of lockdown. A method is presented for estimating the model parameters from real-world data. It is shown that increase of infections slows down and herd immunity is achieved when symptomatic patients are 4-6\% of the population for the European countries we studied, when the total infected fraction is between 50-56 \%. 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.
