Using the SEIQR model with epidemic amplifier effect to predict the final outbreak size of the COVID-19 in Dalian, Liaoning province, China. - Qingyu An; Jun Wu; Wen Hui Chen (PLoS ONE)
Objectives Early in the outbreak, to predict the final size of the COVID-19 outbreak in Dalian, Liaoning province, China, the finding can be used to provide a scientific reference for timely adjustment of prevention and control strategies. Methods Data from COVID-19 patients were collected from August 26 to September 14 2022. Early in the outbreak, a Susceptible-Exposed-Infectious-Quarantine-Recovered (SEIQR) dynamics model with an epidemic amplifier effect, based on the basic model, was developed to fit the data and predict the final size of the COVID-19 outbreak in Dalian, Liaoning province, China. The mean absolute relative error(MARE), root mean squared error(RMSE) and mean absolute error(MAE) were used to assess the predictive capacity of the model. Results From 26 August to 14 September 2022, 1132 confirmed cases and infected asymptomatic cases of COVID-19 (558 males and 574 females) were reported in Dalian. There were two epidemic amplifiers in this outbreak, namely, T Market and H Hotel. The outbreak size predicted by the combined application of the SEIQR model with these two amplifiers is 1168.34 cases, and MARE, RMSE and MAE compared to the actual value from September 1 to 14 is 1.894%, 21.473 and 17.492 respectively According to the fitting results of the basic SEIQR model, if there was no epidemic amplifier in this outbreak, the final outbreak size was 349.96 cases, which means that the T Market and H Hotel increased 822 infections through amplification. Conclusions Early in the outbreak, it was effective and reliable to use the SEIQR transmission dynamics model with the amplifier effect to predict the final size of the COVID-19 outbreak in Dalian, Liaoning province, China, and the result can provide a theoretical basis for the early closing of the COVID-19 epidemic amplifier sites. Furthermore, the epidemic amplifier effect added to the model can solve the homogeneous mixing hypothesis problem that does not match the actual spread of infectious diseases but commonly used by researchers in the construction process of the dynamic model.