Modeling the Coronavirus Disease 2019 Incubation Period: Impact on Quarantine Policy

Daewoo Pak, Klaus Langohr, Jing Ning, Jordi Cortés Martínez, Guadalupe Gómez Melis and Yu Shen
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Daewoo Pak: Department of Information & Statistics, Yonsei University, Wonju 26493, Korea
Klaus Langohr: Departament d’Estadística i Investigació Operativa, Universitat Politècnica de Catalunya-BarcelonaTECH, 08034 Barcelona, Spain
Jing Ning: Department of Biostatistics, The University of Texas, MD Anderson Cancer Center, Houston, TX 77030, USA
Jordi Cortés Martínez: Departament d’Estadística i Investigació Operativa, Universitat Politècnica de Catalunya-BarcelonaTECH, 08034 Barcelona, Spain
Guadalupe Gómez Melis: Departament d’Estadística i Investigació Operativa, Universitat Politècnica de Catalunya-BarcelonaTECH, 08034 Barcelona, Spain
Yu Shen: Department of Biostatistics, The University of Texas, MD Anderson Cancer Center, Houston, TX 77030, USA

Mathematics, 2020, vol. 8, issue 9, 1-8

Abstract: The incubation period of coronavirus disease 2019 (COVID-19) is not always observed exactly due to uncertain onset times of infection and disease symptoms. In this paper, we demonstrate how to estimate the distribution of incubation and its association with patient demographic factors when the exact dates of infection and symptoms’ onset may not be observed. The findings from analysis of the confirmed COVID-19 cases indicate that age could be associated with the incubation period, and an age-specific quarantine policy might be more efficient than a unified one in confining COVID-19.

Keywords: COVID-19; generalized odds-rate class of models; incubation period; infectious disease; interval censoring; left censoring; quarantine policy (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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