The incubation period is a key characteristic of an infectious disease. In the outbreak of a novel infectious disease, accurate evaluation of the incubation period distribution is critical for designing effective prevention and control measures . Estimation of the incubation period distribution based on limited information from retrospective inspection of infected cases is highly challenging due to censoring and truncation. In this paper, we consider a semiparametric regression model for the incubation period and propose a sieve maximum likelihood approach for estimation based on the symptom onset time, travel history, and basic demographics of reported cases. The approach properly accounts for the pandemic growth and selection bias in data collection. We also develop an efficient computation method and establish the asymptotic properties of the proposed estimators. We demonstrate the feasibility and advantages of the proposed methods through extensive simulation studies and provide an application to a dataset on the outbreak of COVID-19.

潜伏期是传染病的一个关键特征。在新型传染病暴发中，准确评估潜伏期分布对于设计有效的防控措施至关重要。由于审查和截断，基于感染病例回顾性检查的有限信息估计潜伏期分布非常具有挑战性。在本文中，我们考虑了潜伏期的半参数回归模型，并提出了一种基于症状发作时间、旅行历史和报告病例的基本人口统计数据的筛分最大似然估计方法。该方法适当地解释了数据收集中的流行病增长和选择偏差。我们还开发了一种有效的计算方法，并建立了所提出的估计量的渐近特性。我们通过广泛的模拟研究证明了所提出方法的可行性和优势，并提供了对 COVID-19 爆发数据集的应用。