Since the outbreak of the new coronavirus disease (COVID-19), a large number of scientific studies and data analysis reports have been published in the International Journal of Medicine and Statistics. Taking the estimation of the incubation period as an example, we propose a low-cost method to integrate external research results and available internal data together. By using empirical likelihood method, we can effectively incorporate summarized information even if it may be derived from a misspecified model. Taking the possible uncertainty in summarized information into account, we augment a logarithm of the normal density in the log empirical likelihood. We show that the augmented log-empirical likelihood can produce enhanced estimates for the underlying parameters compared with the method without utilizing auxiliary information. Moreover, the Wilks' theorem is proved to be true. We illustrate our methodology by analyzing a COVID-19 incubation period data set retrieved from Zhejiang Province and summarized information from a similar study in Shenzhen, China.

中文翻译：

---

通过整合已发布的信息，增强对 COVID-19 潜伏期的经验可能性估计

自新型冠状病毒病（COVID-19）爆发以来，《国际医学与统计学杂志》发表了大量科学研究和数据分析报告. 以估计潜伏期为例，我们提出了一种将外部研究结果和可用内部数据整合在一起的低成本方法。通过使用经验似然法，我们可以有效地整合汇总信息，即使它可能来自错误指定的模型。考虑到汇总信息中可能存在的不确定性，我们在对数经验似然中增加了正态密度的对数。我们表明，与不使用辅助信息的方法相比，增强的对数经验似然可以产生对基础参数的增强估计。此外，威尔克斯定理被证明是正确的。