Trend analysis and prediction of gonorrhea in mainland China based on a hybrid time series model.

ABSTRACT:

Background

Gonorrhea has long been a serious public health problem in mainland China that requires attention, modeling to describe and predict its prevalence patterns can help the government to develop more scientific interventions.

Methods

Time series (TS) data of the gonorrhea incidence in China from January 2004 to August 2022 were collected, with the incidence data from September 2021 to August 2022 as the validation. The seasonal autoregressive integrated moving average (SARIMA) model, long short-term memory network (LSTM) model, and hybrid SARIMA-LSTM model were used to simulate the data respectively, the model performance were evaluated by calculating the mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) of the trainin

SUBMITTER: Wang Z 

PROVIDER: S-EPMC10802032 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature