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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Daewoo Pak & Klaus Langohr & Jing Ning & Jordi Cortés Martínez & Guadalupe Gómez Melis & Yu Shen, 2020. "Modeling the Coronavirus Disease 2019 Incubation Period: Impact on Quarantine Policy," Mathematics, MDPI, vol. 8(9), pages 1-8, September.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:9:p:1631-:d:416639
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