双区间删失下新冠病毒肺炎潜伏期分布的参数估计

Parametric Estimation for the Incubation Period Distribution of COVID-19 under Doubly Interval Censoring

潜伏期的取值范围对流行病学病例的定义是至关重要的,并可以用来确定合适的隔离时间.此外,对潜伏期的了解有助于评估入境筛查和接触者追踪的有效性.本研究中我们所使用的是根据中国省级疾病控制中心等机构公开报道的信息整理而来的流行病学数据,在双重区间删失的数据结构下,对新型冠状病毒肺炎(COVID-19)的潜伏期分布进行参数估计.根据我们的计算,潜伏期中位数的估计约为5.5天,在对数正态假设下潜伏期以95%可能性落在[2.01,15.36].本文的方法可以充分利用新冠肺炎流行病学调查的数据结构,可为疫情防控政策制定提供可靠的依据.

The range of incubation period is crucial to the definition of epidemiological cases and can be used to determine the appropriate isolation time.In addition,understanding of the incubation period helps assess the effectiveness of immigration screening and contact tracing.In this study,we used the epidemiological data compiled according to the information publicly reported by the Chinese provincial centers for disease control and other institutions,and estimated the parameter distribution of the incubation period of novel coronavirus under the data structure of doubly interval censoring.According to our calculation,the median incubation period is estimated to be about 5.5 days,and under the lognormal distribution hypothesis,the incubation period falls at [2.01,15.36] with with 95% confidence.The method in this paper can make full use of the data structure of the epidemiological investigation of COVID-19 and provide a reliable basis for the formulation of epidemic prevention and control policies.