Modeling the coronavirus disease 2019 incubation period: Impact on quarantine policy

Daewoo Pak; Klaus Langohr; Jing Ning; Jordi Cortés Martínez; Guadalupe Gómez Melis; Yu Shen

doi:10.3390/math8091631

Modeling the coronavirus disease 2019 incubation period: Impact on quarantine policy

Daewoo Pak, Klaus Langohr, Jing Ning, Jordi Cortés Martínez, Guadalupe Gómez Melis, Yu Shen

Biostatistics

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

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.

Original language	English (US)
Article number	1631
Journal	Mathematics
Volume	8
Issue number	9
DOIs	https://doi.org/10.3390/math8091631
State	Published - Sep 2020

Keywords

COVID-19
Generalized odds-rate class of models
Incubation period
Infectious disease
Interval censoring
Left censoring
Quarantine policy

ASJC Scopus subject areas

General Mathematics

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10.3390/math8091631

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title = "Modeling the coronavirus disease 2019 incubation period: Impact on quarantine policy",

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.",

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AU - Pak, Daewoo

AU - Langohr, Klaus

AU - Ning, Jing

AU - Martínez, Jordi Cortés

AU - Melis, Guadalupe Gómez

AU - Shen, Yu

PY - 2020/9

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N2 - 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.

AB - 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.

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KW - Generalized odds-rate class of models

KW - Incubation period

KW - Infectious disease

KW - Interval censoring

KW - Left censoring

KW - Quarantine policy

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Modeling the coronavirus disease 2019 incubation period: Impact on quarantine policy

Abstract

Keywords

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