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Enhanced empirical likelihood estimation of incubation period of COVID-19 by integrating published information.

ABSTRACT: 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.

SUBMITTER: Jiang Z

PROVIDER: S-EPMC8242591 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Enhanced empirical likelihood estimation of incubation period of COVID-19 by integrating published information.

Jiang Zhongfeng Z Yang Baoying B Qin Jing J Zhou Yong Y

Statistics in medicine 20210511 19

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 missp ...[more]

PMID: 33973260

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Project description:Background:A novel human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified in China in December 2019. There is limited support for many of its key epidemiologic features, including the incubation period for clinical disease (coronavirus disease 2019 [COVID-19]), which has important implications for surveillance and control activities. Objective:To estimate the length of the incubation period of COVID-19 and describe its public health implications. Design:Pooled analysis of confirmed COVID-19 cases reported between 4 January 2020 and 24 February 2020. Setting:News reports and press releases from 50 provinces, regions, and countries outside Wuhan, Hubei province, China. Participants:Persons with confirmed SARS-CoV-2 infection outside Hubei province, China. Measurements:Patient demographic characteristics and dates and times of possible exposure, symptom onset, fever onset, and hospitalization. Results:There were 181 confirmed cases with identifiable exposure and symptom onset windows to estimate the incubation period of COVID-19. The median incubation period was estimated to be 5.1 days (95% CI, 4.5 to 5.8 days), and 97.5% of those who develop symptoms will do so within 11.5 days (CI, 8.2 to 15.6 days) of infection. These estimates imply that, under conservative assumptions, 101 out of every 10 000 cases (99th percentile, 482) will develop symptoms after 14 days of active monitoring or quarantine. Limitation:Publicly reported cases may overrepresent severe cases, the incubation period for which may differ from that of mild cases. Conclusion:This work provides additional evidence for a median incubation period for COVID-19 of approximately 5 days, similar to SARS. Our results support current proposals for the length of quarantine or active monitoring of persons potentially exposed to SARS-CoV-2, although longer monitoring periods might be justified in extreme cases. Primary Funding Source:U.S. Centers for Disease Control and Prevention, National Institute of Allergy and Infectious Diseases, National Institute of General Medical Sciences, and Alexander von Humboldt Foundation.

Dataset Information

Enhanced empirical likelihood estimation of incubation period of COVID-19 by integrating published information.

Publications

Enhanced empirical likelihood estimation of incubation period of COVID-19 by integrating published information.

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