Forecasting hand-foot-and-mouth disease cases using wavelet-based SARIMA-NNAR hybrid model.

ABSTRACT:

Background

Hand-foot-and-mouth disease_(HFMD) is one of the most typical diseases in children that is associated with high morbidity. Reliable forecasting is crucial for prevention and control. Recently, hybrid models have become popular, and wavelet analysis has been widely performed. Better prediction accuracy may be achieved using wavelet-based hybrid models. Thus, our aim is to forecast number of HFMD cases with wavelet-based hybrid models.

Materials and methods

We fitted a wavelet-based seasonal autoregressive integrated moving average (SARIMA)-neural network nonlinear autoregressive (NNAR) hybrid model with HFMD weekly cases from 2009 to 2016 in Zhengzhou, China. Additionally, a single SARIMA model, simplex NNAR model, and pure SARIMA-NNAR hybrid model were established

SUBMITTER: Yu G 

PROVIDER: S-EPMC7864434 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature