tetano
Editor, Senior Moderator
Comput Biol Med
. 2023 Mar 30;158:106794.
doi: 10.1016/j.compbiomed.2023.106794. Online ahead of print.
Estimate the incubation period of coronavirus 2019 (COVID-19)
Ke Men[SUP] 1 [/SUP], Yihao Li[SUP] 2 [/SUP], Xia Wang[SUP] 3 [/SUP], Guangwei Zhang[SUP] 1 [/SUP], Jingjing Hu[SUP] 1 [/SUP], Yanyan Gao[SUP] 1 [/SUP], Ashley Han[SUP] 4 [/SUP], Wenbin Liu[SUP] 5 [/SUP], Henry Han[SUP] 6 [/SUP]
Affiliations
Abstract
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 bootstrap methods, we estimated the mean and median incubation periods as 5.84 (95% CI, 5.42-6.25 days) and 5.01 days (95% CI 4.00-6.00 days), respectively. We also found that the incubation periods of groups with ages greater than or equal to 40 years and less than 40 years demonstrated a statistically significant difference. The former group had a longer incubation period and a larger variance than the latter, suggesting the need for different quarantine times or medical intervention strategies. Our machine-learning results further demonstrated that the two age groups were linearly separable, consistent with previous statistical analyses. Additionally, our results indicated that the incubation period difference between males and females was not statistically significant.
Keywords: COVID-19; Incubation period; Machine learning; Mann-Whitney rank tests; Monte Carlo simulation; Nonparametric methods; Siegal-Tukey tests.
. 2023 Mar 30;158:106794.
doi: 10.1016/j.compbiomed.2023.106794. Online ahead of print.
Estimate the incubation period of coronavirus 2019 (COVID-19)
Ke Men[SUP] 1 [/SUP], Yihao Li[SUP] 2 [/SUP], Xia Wang[SUP] 3 [/SUP], Guangwei Zhang[SUP] 1 [/SUP], Jingjing Hu[SUP] 1 [/SUP], Yanyan Gao[SUP] 1 [/SUP], Ashley Han[SUP] 4 [/SUP], Wenbin Liu[SUP] 5 [/SUP], Henry Han[SUP] 6 [/SUP]
Affiliations
- PMID: 37044045
- DOI: 10.1016/j.compbiomed.2023.106794
Abstract
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 bootstrap methods, we estimated the mean and median incubation periods as 5.84 (95% CI, 5.42-6.25 days) and 5.01 days (95% CI 4.00-6.00 days), respectively. We also found that the incubation periods of groups with ages greater than or equal to 40 years and less than 40 years demonstrated a statistically significant difference. The former group had a longer incubation period and a larger variance than the latter, suggesting the need for different quarantine times or medical intervention strategies. Our machine-learning results further demonstrated that the two age groups were linearly separable, consistent with previous statistical analyses. Additionally, our results indicated that the incubation period difference between males and females was not statistically significant.
Keywords: COVID-19; Incubation period; Machine learning; Mann-Whitney rank tests; Monte Carlo simulation; Nonparametric methods; Siegal-Tukey tests.