SARFIMA model prediction for infectious diseases: application to hemorrhagic fever with renal syndrome and comparing wit
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(2020) 20:243
RESEARCH ARTICLE
Open Access
SARFIMA model prediction for infectious diseases: application to hemorrhagic fever with renal syndrome and comparing with SARIMA Chang Qi1, Dandan Zhang1, Yuchen Zhu1, Lili Liu1, Chunyu Li1, Zhiqiang Wang2 and Xiujun Li1*
Abstract Background: The early warning model of infectious diseases plays a key role in prevention and control. This 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. Methods: Data on notified HFRS cases in Weifang city, Shandong Province were collected from the official website and Shandong Center for Disease Control and Prevention between January 1, 2005 and December 31, 2018. The SARFIMA model considering both the short memory and long memory was performed to fit and predict the HFRS series. Besides, we compared accuracy of fit and prediction between SARFIMA and SARIMA which was used widely in infectious diseases. Results: Model assessments indicated that the SARFIMA model has better goodness of fit (SARFIMA (1, 0.11, 2)(1, 0, 1)12: Akaike information criterion (AIC):-631.31; SARIMA (1, 0, 2)(1, 1, 1)12: AIC: − 227.32) and better predictive ability than the SARIMA model (SARFIMA: root mean square error (RMSE):0.058; SARIMA: RMSE: 0.090). Conclusions: The SARFIMA model produces superior forecast performance than the SARIMA model for HFRS. Hence, the SARFIMA model may help to improve the forecast of monthly HFRS incidence based on a long-range dataset. Keywords: Seasonal autoregressive fractionally integrated moving average model, Seasonal autoregressive integrated moving average model, Hemorrhagic fever with renal syndrome, Goodness of fit, Prediction
Background The incidence of infectious diseases is subject to many factors, and there are intricate connections between the influencing factors. In recent years, many studies have explored the relationship between meteorological factors and infectious diseases [1–4]. However, the impact of * Correspondence: [email protected] 1 Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China Full list of author information is available at the end of the article
meteorological factors account for only a small proportion on infectious diseases [1], because there are many potential unknown factors. It is especially important to establish a dynamic model of time series according to its own variation to predict and warn infectious diseases. Time series analysis and modeling is widely used for studying temporal changes in the incidence of infectious diseases to forecast future trends [2, 5, 6]. Seasonal autoregressive integrated moving average (SARIMA) model has been used to fit and predict epidemics of many
© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International
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