ABSTRACT
Objective: To study the incubation period of the infection with 2019-nCoV Omicron variant BA.5.1.3. Methods: Based on the epidemiological survey data of 315 COVID-19 cases and the characteristics of interval censored data structure, log-normal distribution and Gamma distribution were used to estimate the incubation. Bayes estimation was performed for the parameters of each distribution function using discrete time Markov chain Monte Carlo algorithm. Results: The mean age of the 315 COVID-19 cases was (42.01±16.54) years, and men accounted for 30.16%. A total of 156 cases with mean age of (41.65±16.32) years reported the times when symptoms occurred. The log-normal distribution and Gamma distribution indicated that the M (Q1, Q3) of the incubation period from exposure to symptom onset was 2.53 (1.86, 3.44) days and 2.64 (1.91, 3.52) days, respectively, and the M (Q1, Q3) of the incubation period from exposure to the first positive nucleic acid detection was 2.45 (1.76, 3.40) days and 2.57 (1.81, 3.52) days, respectively. Conclusions: The incubation period by Bayes estimation based on log-normal distribution and Gamma distribution, respectively, was similar to each other, and the best distribution of incubation period was Gamma distribution, the difference between the incubation period from exposure to the first positive nucleic acid detection and the incubation period from exposure to symptom onset was small. The median of incubation period of infection caused by Omicron variant BA.5.1.3 was shorter than those of previous Omicron variants.
Subject(s)
COVID-19 , Nucleic Acids , Male , Humans , Adult , Middle Aged , SARS-CoV-2 , Bayes Theorem , Infectious Disease Incubation PeriodABSTRACT
Social distancing is currently the most effective known countermeasure against the rapid proliferation of the virus that causes COVID-19. This project aims to encourage mindfulness about maintaining interpersonal distance in shared public spaces through a multi-user virtual reality experience that simulates shopping in a grocery store. The virtual environment is populated with non-player characters that navigate through the store and also supports up to 20 concurrent live users represented as avatars. Real-time feedback is implemented using a dynamic visual effect that reacts to physical proximity, and comparative performance metrics are also provided for users to reflect on after the task is completed.