Estimate the incubation period of coronavirus 2019 (COVID-19).

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.

Estimate the incubation period of coronavirus 2019 (COVID-19)

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.

Knowledge, Attitudes, and Practices of Urban Residents Toward COVID-19 in Shaanxi During the Post-lockdown Period.

Background: The coronavirus disease 2019 (COVID-19) pandemic in China is essentially under control. Under global scrutiny, China has started reviving the social, cultural, and working lives of its inhabitants. However, localized outbreaks of COVID-19 are occurring, indicating that the country still needs to follow disease prevention and control measures. Previous studies have assessed the knowledge, attitudes, and behaviors of the general public in China regarding COVID-19 during the pandemic. However, little is known about knowledge, attitudes, and practices (KAP) of Chinese residents regarding COVID-19 after periods of lockdown. Therefore, this study was conducted to identify the KAP and other factors among the residents of Shaanxi Province during the post-lockdown period. Methods: A cross-sectional, network questionnaire survey was conducted in Shaanxi Province from October 1-30, 2020. A total of 1,175 urban residents were interviewed via Wen Juan Xing, an online questionnaire tool. A self-developed online KAP COVID-19 questionnaire was developed in this study. The questionnaire consisted of four parts: general information, knowledge, attitude, and practice. Descriptive statistics and binomial logistic regression analysis were used in the statistical analysis. Results: The majority of the participants were knowledgeable about COVID-19. They had optimistic attitudes and behaved appropriately toward COVID-19. Education was an associated factor for the knowledge of residents and the knowledge of COVID-19 was high among people with high academic qualifications. Attitudes were more positive in residents who lived with elderly people, women, and children. The score of practice was higher in residents with positive attitudes and high academic qualifications. There was a positive correlation between age and practice. Conclusion: We found that the KAP of residents in Shaanxi was at a relatively high level during the post-lockdown period. Although the lockdown was lifted, the epidemic is not over. Thus, it is necessary to develop targeted health education programs for residents with different demographic characteristics in Shaanxi.