Popis: |
Background: A new human coronavirus named SARS-CoV-2 emerged during December 2019 in Wuhan, China. Cases have been exported to other Chinese cities and abroad, which may cause the global outbreak. Chang Sha is the nearest provincial capital city to Wuhan, the first case of COVID-19 in Changsha was diagnosed on January 21, 2020. Estimating the transmissibility and forecasting the trend of the outbreak of SARS-CoV-2 under the prevention and control measures in Changsha could inform evidence based decisions to policy makers. Methods: Data were collected from the Health Commission of Changsha and Hunan Center for Disease Control and Prevention. A Susceptible-exposed-infections/ asymptomatic- removed (SEIAR) model was established to simulate the transmission of SARS-CoV-2 in Changsha. Berkeley Madonna 8.3.18 were employed for the model simulation and prediction, while the curve fitting problem was solved by the Runge-Kutta fourth-order method, with a tolerance of 0.001. Results: In this study, we found that Rt was 2.05 from January 21 to 27 and reduced to 0.2 after January 27, 2020 in Changsha. The prediction results showed that when no obvious prevention and control measures were applied, the total number of patients in Changsha would reach the maximum (2.27 million) on the 79th day after the outbreak, and end in about 240 days; When measures have not been fully launched, the total number of patients would reach the maximum (1.60 million) on the 28th day after the outbreak, and end in about 110 days; When measures have been fully launched, the total number of patients would reach the maximum (234) on the 23rd day after the outbreak, and end in about 60 days. Conclusions: Outbreak of SARS-CoV-2 in Changsha is in a controllable stage under current prevention and control measures, it is predicted that the cumulative patients would reach the maximum of 234 on February 12, and the outbreak would be over on 20 March in Changsha. With the fully implementation of prevention and control measures, it could effectively reduce the peak value, short the time to peak and duration of the outbreak. |