Abstract
Knowledge of the incubation period of coronavirus disease 2019 (COVID-19), defined as the time from infection to symptom onset, is critical to effectively confine COVID-19. However, the incubation period of COVID-19 is not always observed exactly due to uncertain onsets of infection and disease symptom. In this paper, we demonstrate how to estimate the distribution of incubation and its association with patient demographic factors when the dates of infection and disease onset are not explicitly. We employ a sufficiently general parametric class, the generalized odds-rate class of regression models, which includes the log-logistic proportional odds model and the Weibull proportional hazards model as special cases. We base our analysis on publicly reported, clinically confirmed COVID-19 cases with potential exposure period information. The application indicates that an age-specific quarantine policy might be more efficient than a unified one in confining COVID-19.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
This work was partially supported by the Ministerio de Economia y Competitividad (Spain) [MTM2015-64465-C2-1-R (MINECO/FEDER)] and the Departament de Economia i Coneixement de la Generalitat de Catalunya (Spain) [2017 SGR 622 (GRBIO)].
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Not applicable. This paper uses publicly available data and was deemed exempt from approval from a human research ethics committee.
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The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, University of Washington, and Vrije Universiteit Amsterdam.
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