The early warning model of infectious diseases plays a key role in prevention and control. Our study aims to using seasonal autoregressive fractionally integrated moving average (SARFIMA) model to predict the incidence of hemorrhagic fever with renal syndrome (HFRS) and comparing with seasonal autoregressive integrated moving average (SARIMA) model to evaluate its prediction effect.
Data on notified HFRS cases in Weifang city, Shandong Province were supplied by the Disease Reporting Information System of the Shandong Center for Disease Control and Prevention from January 1, 2005 to December 31, 2018. The SARFIMA model considering both the short-memory and long-memory were performed to fit and predict the HFRS series. Besides, we compared accuracy of fitting and prediction between SARFIMA and SARIMA which were used widely in infectious diseases.
Both SARFIMA and SARIMA models show good fit of data. Model assessments indicated that the SARFIMA model has better goodness of fit (SARFIMA(2, 0.15, 2)(1, 0, 0) 12 : Akaike information criterion (AIC): -630.61; SARIMA(2, 0, 2)(1, 1, 0) 12 : AIC: -196.04) and better predictive ability than the SARIMA model (SARFIMA: root mean square error (RMSE): 0.067; SARIMA: RMSE: 0.111).
The SARFIMA model produces superior forecast performance than the SARIMA model for HFRS. Hence, the SARFIMA model may help us to improve the forecast of HFRS incidence.