ABSTRACT
This study aimed to predict the transmission trajectory of the 2019 Corona Virus Disease (COVID-19). The particle swarm optimization (PSO) algorithm was combined with the traditional susceptible exposed infected recovered (SEIR) infectious disease prediction model to propose a SEIR-PSO prediction model on the COVID-19. In addition, the domestic epidemic data from February 25, 2020 to March 20, 2020 in China were selected as the training set for analysis. The results showed that when the conversion rate, recovery rate, and mortality rate of the SEIR-PSO model were 1/5, 1/15, and 1/13, its predictive effect on the number of people diagnosed with COVID-19 was the closest to the real data; and the SEIR-PSO model showed a mean-square errors (MSE) value of 1304.35 and mean absolute error (MAE) value of 1069.18, showing the best prediction effect compared with the susceptible infectious susceptible (SIS) model and the SEIR model. In contrary to the standard particle swarm optimization (SPSO) and linear weighted particle swarm optimization (LPSO), which were two classical improved PSO algorithms, the reliability and diversity of the SEIR-PSO model were higher. In summary, the SEIR-PSO model showed excellent performance in predicting the time series of COVID-19 epidemic data, and showed reliable application value for the prevention and control of COVID-19 epidemic.
ABSTRACT
COVID-19 has become a global pandemic. However, the impact of the public health interventions in China needs to be evaluated. We established a SEIRD model to simulate the transmission trend of China. In addition, the reduction of the reproductive number was estimated under the current forty public health interventions policies. Furthermore, the infection curve, daily transmission replication curve, and the trend of cumulative confirmed cases were used to evaluate the effects of the public health interventions. Our results showed that the SEIRD curve model we established had a good fit and the basic reproductive number is 3.38 (95% CI, 3.25-3.48). The SEIRD curve show a small difference between the simulated number of cases and the actual number; the correlation index (H2) is 0.934, and the reproductive number (R) has been reduced from 3.38 to 0.5 under the current forty public health interventions policies of China. The actual growth curve of new cases, the virus infection curve, and the daily transmission replication curve were significantly going down under the current public health interventions. Our results suggest that the current public health interventions of China are effective and should be maintained until COVID-19 is no longer considered a global threat.