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

Background Hand-foot-and-mouth disease_(HFMD) is one of the most typical diseases in children that associated with high morbidity. Reliable forecasting crucial for prevention and control. Recently, hybrid models have become popular, wavelet analysis has been widely performed. Better prediction accuracy may be achieved using wavelet-based models. Thus, our aim to forecast number HFMD cases Materials methods We fitted a seasonal autoregressive integrated moving average (SARIMA)–neural network nonlinear (NNAR) model weekly from 2009 2016 Zhengzhou, China. Additionally, single SARIMA model, simplex NNAR pure SARIMA–NNAR were established comparison estimation. Results The demonstrates excellent performance whether fitting or compared other Its time series are similar actual observed series. Conclusions this study suitable cases. Hence, it will facilitate control HFMD.