Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.
Department of Mathematics and Statistics, The University of North Carolina at Charlotte, Fretwell 335L, 9201 University City Blvd., Charlotte, NC, 28223, USA.
Lifetime Data Anal. 2023 Jan;29(1):87-114. doi: 10.1007/s10985-022-09567-3. Epub 2022 Jul 13.
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。在新发传染病的爆发中，准确评估潜伏期分布对于设计有效的预防和控制措施至关重要。由于删失和截断，基于对感染病例的回顾性检查获得的有限信息来估计潜伏期分布极具挑战性。在本文中，我们考虑了潜伏期的半参数回归模型，并针对报告病例的症状出现时间、旅行史和基本人口统计学数据，提出了一种基于筛的最大似然估计方法。该方法适当考虑了大流行的增长和数据收集的选择偏差。我们还开发了一种有效的计算方法，并建立了所提出估计量的渐近性质。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，并提供了一个针对 COVID-19 爆发数据集的应用案例。
