COVID-19 Modelling Group, Hong Kong Baptist University, Kowloon, Hong Kong SAR, China.
Department of Physics and Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon, Hong Kong SAR, China.
Nat Commun. 2021 Feb 19;12(1):1147. doi: 10.1038/s41467-021-21385-z.
Within a short period of time, COVID-19 grew into a world-wide pandemic. Transmission by pre-symptomatic and asymptomatic viral carriers rendered intervention and containment of the disease extremely challenging. Based on reported infection case studies, we construct an epidemiological model that focuses on transmission around the symptom onset. The model is calibrated against incubation period and pairwise transmission statistics during the initial outbreaks of the pandemic outside Wuhan with minimal non-pharmaceutical interventions. Mathematical treatment of the model yields explicit expressions for the size of latent and pre-symptomatic subpopulations during the exponential growth phase, with the local epidemic growth rate as input. We then explore reduction of the basic reproduction number R through specific transmission control measures such as contact tracing, testing, social distancing, wearing masks and sheltering in place. When these measures are implemented in combination, their effects on R multiply. We also compare our model behaviour to the first wave of the COVID-19 spreading in various affected regions and highlight generic and less generic features of the pandemic development.
在短时间内,COVID-19 迅速发展成为全球性大流行病。由于存在症状前和无症状的病毒携带者,使得该疾病的干预和控制极具挑战性。基于已报告的感染案例研究,我们构建了一个侧重于症状出现前后传播的流行病学模型。该模型针对武汉以外地区大流行初期的潜伏期和人际传播统计数据进行了校准,干预措施最少。通过对模型的数学处理,得到了在指数增长阶段潜隐和症状前亚群的大小的显式表达式,以局部流行病增长率作为输入。然后,我们通过接触者追踪、检测、社交隔离、戴口罩和就地避难等特定的传播控制措施来探讨基本再生数 R 的降低。当这些措施联合实施时,它们对 R 的影响会倍增。我们还将模型行为与 COVID-19 在不同受影响地区的第一波传播进行了比较,并突出了大流行发展的通用和非通用特征。