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Abstract: The impact of coronavirus disease 2019 (COVID-19) is less than many other emerging infectious diseases, as its reproduction number (R) is below one, but China is currently experiencing a major outbreak. This study summarizes our knowledge of the epidemiological characteristics of COVID-19 in China. Methods: We analyzed the time-varying risk of confirmed COVID-19 cases in 61 Chinese districts from early January to the end of February 2020 using the SEIR model to explore the transition from confirmed cases to confirmed deaths in COVID-19. Results: The SEIR model provided an ability to simulate the confirmative data on daily (observable) cases and deaths well. We inferred the daily values of the unobservable states and dynamic changes of R by the method of Bayesian inference. The basic reproduction number R of COVID-19 varied from 0.45 to 0.61, well below the unit, implying that timely interventions greatly limited contact between people and effectively contained the spread of COVID-19 in Shaanxi. Conclusions: The SEIR model provides a way to detect the transmission route and a valid model to deal with the change of unobservable status from confirmed cases to confirmed deaths.Keywords: basic reproduction number; Bayesian inference; COVID-19; mathematical modelling; model selection; local transmission; importation
Abstract: The linear growth model is a parsimonious and effective modelling tool to mathematically solve the epidemic dynamics of COVID-19. In this study, we use Bayesian inference to determine the posterior distributions of parameters describing the epidemic dynamics of COVID-19 in China with the renewal equation model. Results: The posterior inference results are largely consistent with the epidemic data from the early stage of the outbreak in China, indicating that the data is more useful than estimates according to our model. However, as more data becomes available, it will be more appropriate to use other mathematical models, such as the Susceptible-Exposed-Diseased-Asymptomatic-Recovered (SEEDAR) and SEEDDAAR models. Conclusions: With data from the early stage of the outbreak, the linear growth model predicts the epidemic dynamics of COVID-19 smoothly and will become a valid and useful model for decision-makers to control the COVID-19 outbreak.
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