SEIHRS_gv model——predicting the influenza-like illness epidemic trend based on short term data
Objective To develop a prediction model for the epidemic trend of influenza-like ill-ness using monitoring data from Tianjin City and quantitatively evaluate the impact of epidemic prevention and control measures on the medical burden caused by influenza-like illness.Methods The data from November 6,2023 to November 15,2023 were used for fitting the SEIHRS_gv model,and the data from November 15,2023 to March 31,2024 were using for validating.Root mean square error(RMSE),mean absolute error(MAE),and coefficient of determination r-square(R2)were used to evaluate the predictive ability of the model.Results The SEIHRS_gv model could predict the trend,peak,and cru-cial point of influenza-like illness epidemics.Using 10 days of data for prediction,with an R2 of 0.85 and an RMSE of 949.5.Increasing the intensity of epidemic prevention and control measures could reduce the number of patients seeking medical treatment.Conclusions The SEIHRS_gv model required a few days of data for prediction in this round of influenza-like illness epidemic prediction and had high accuracy in prediction results,which could serve as an efficient predictive model to evaluate the pressure of hospital visits and guide the implementation intensity of epidemic control measures.
Influenza-like illnessSEIR modelEpidemic prevention and control
NETL
NSTL
万方数据
