Application of an Autoregressive Integrated Moving Average Model for Predicting the Incidence of Hemorrhagic Fever with Renal Syndrome

Abstract

Abstract. The Box-Jenkins approach was used to fit an autoregressive integrated moving average (ARIMA) model to the incidence of hemorrhagic fever with renal Syndrome (HFRS) in China during 1986-2009. The ARIMA (0, 1, 1) + (2, 1, 0) 12 models fitted exactly with the number of cases during January 1986-December 2009. The fitted model was then used to predict HFRS incidence during 2010, and the number of cases during January-December 2010 fell within the model's confidence interval for the predicted number of cases in 2010. This finding suggests that the ARIMA model fits the fluctuations in HFRS frequency and it can be used for future forecasting when applied to HFRS prevention and control.

Characteristics and Prediction of Lung Cancer Mortality in China from 1991 to 2013

“…Many researchers have applied different time series models to forecasting epidemic incidence or mortality in previous studies (Li et al, 2012;Spaeder and Fackler, 2012). The autoregressive integrated moving average (ARIMA) models are almost the most widely used methods, considering both the historical values and residuals, and can effectively transforms the non-stationary data into a stationary one .…”

Using an Autoregressive Integrated Moving Average Model to Predict the Incidence of Hemorrhagic Fever with Renal Syndrome in Zibo, China, 2004–2014

“…Such selection of models is usually based on the Akaike information criterion (AIC) and Schwarz Bayesian criterion (SBC). Smaller AIC and SBC statistics indicate the better fitting model (14).…”

“…In response to the spread of HFRS in China, the Chinese Center for Disease Control and Prevention (CDC) established the National Notifiable Disease Surveillance System in 2004, which made the surveillance data for HFRS more accurate and comprehensive. Surveillance and early warning are essential for controlling or reducing the risk of outbreaks (14). Early warnings of infectious diseases should be provided based on the analysis of surveillance information.…”

Forecasting on the long-term sustainability of the employees provident fund in Malaysia via the Box-Jenkins’ ARIMA model

“…Among others, Promprou, Jaroensutasinee and Jaroensutasinee (2006), who fitted the ARIMA models on the monthly data of dengue hemorrhagic fever cases in southern Thailand, revealed that the forecast curves were comparably close to the trend of actual figures and disclosed that the autocorrelation function (ACF) was insignificantly different compared to zero. Likewise, Li, Guo, Han, Zhang, Qi, Xu, Wei, Han and Liu (2012) concluded that the ARIMA models adequately describe the changes in hemorrhagic fever with renal syndrome (HFRS) frequency and these models can also be used for future forecasting that is tied to HFRS preventive control. In a similar vein, Moosazadeh, Nasehi, Bahrampour, Khanjani, Sharafi and Ahmadi (2014) proved that the Box-Jenkins and seasonal ARIMA (SARIMA) models are the appropriate tools to predict a growing incidence of tuberculosis cases in Iran.…”