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Can I read Estimate the incubation period of coronavirus 2019 (COVID-19) on EtoBox?
Estimate the incubation period of coronavirus 2019 (COVID-19) by Ke Men; Yihao Li; Xia Wang; Guangwei Zhang; Jingjing Hu; Yanyan Gao; Ashley Han; Wenbin Liu; Henry Han is a Medicine article available to read on EtoBox.
What is Estimate the incubation period of coronavirus 2019 (COVID-19) about?
COVID-19 is an infectious disease that presents unprecedented challenges to society. Accurately estimating the incubation period of the coronavirus is critical for effective prevention and control. However, the exact incubation period remains unclear, as COVID-19 symptoms can appear in as little as 2 days or as long as 14 days or more after exposure. Accurate estimation requires original chain-of-infection data, which may not be fully available from the original outbreak in Wuhan, China. In this study, we estimated the incubation period of COVID-19 by leveraging well-documented and epidemiologically informative chain-of-infection data collected from 10 regions outside the original Wuhan areas prior to February 10, 2020. We employed a proposed Monte Carlo simulation approach and nonparametric methods to estimate the incubation period of COVID-19. We also utilized manifold learning and related statistical analysis to uncover incubation relationships between different age and gender groups. Our findings revealed that the incubation period of COVID-19 did not follow general distributions such as lognormal, Weibull, or Gamma. Using proposed Monte Carlo simulations and nonparametric boot
Who reads Estimate the incubation period of coronavirus 2019 (COVID-19)?
It is typically read by researchers, students, and practitioners in Medicine.
- Author
- Ke Men; Yihao Li; Xia Wang; Guangwei Zhang; Jingjing Hu; Yanyan Gao; Ashley Han; Wenbin Liu; Henry Han
- Publisher
- Elsevier BV
- Published
- 2023
- Language
- EN
- Field
- Medicine (Physical Sciences)