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Forecasting incidence of hemorrhagic fever with renal syndrome in China using ARIMA model
Qiyong Liu1, Xiaodong Liu, Baofa Jiang
1National Institute for Communicable Disease Control and Prevention, China CDC, Beijing, 102206, PR China.
Insights
Hemorrhagic fever with renal syndrome (HFRS) is a growing concern in China. This study successfully used an autoregressive integrated moving average (ARIMA) model to monitor and forecast HFRS incidence, predicting a rise in cases.
Area of Science:
- Epidemiology
- Biostatistics
- Public Health
Background:
- China faces a severe burden of hemorrhagic fever with renal syndrome (HFRS), accounting for 90% of global cases.
- Increasing HFRS incidence and expanding natural foci necessitate improved monitoring and prediction strategies.
- Effective HFRS control requires robust surveillance and forecasting methods.
Purpose of the Study:
- To apply a stochastic autoregressive integrated moving average (ARIMA) model for monitoring HFRS incidence in China.
- To forecast short-term HFRS incidence to aid in disease control efforts.
- To evaluate the efficacy of the ARIMA model in predicting HFRS trends.
Main Methods:
- Utilized Chinese HFRS data spanning from 1975 to 2008 for ARIMA model fitting.
- Employed Akaike Information Criterion (AIC) and Ljung-Box test for model evaluation.
- Assessed model validity using Mean Absolute Percentage Error (MAPE) and forecasted future incidence.
Main Results:
- The optimal ARIMA (0,3,1) model demonstrated good fit with non-significant residual autocorrelations (Ljung-Box Q = 5.95, P = 0.3113).
- Fitted HFRS incidence values (1978-2008) closely matched observed data, with a MAPE of 12.20%.
- Forecasted HFRS incidence for 2009-2011 indicated a rise to 0.69, 0.86, and 1.21 per 100,000 population, respectively.
Conclusions:
- ARIMA models are valuable tools for HFRS surveillance and incidence monitoring in China.
- Accurate short-term forecasting of HFRS incidence is achievable using ARIMA modeling.
- The study predicts a potential increase in HFRS incidence in China based on model forecasts.
Background:
China is a country that is most seriously affected by hemorrhagic fever with renal syndrome (HFRS) with 90% of HFRS cases reported globally. At present, HFRS is getting worse with increasing cases and natural foci in China. Therefore, there is an urgent need for monitoring and predicting HFRS incidence to make the control of HFRS more effective. In this study, we applied a stochastic autoregressive integrated moving average (ARIMA) model with the objective of monitoring and short-term forecasting HFRS incidence in China.
Methods:
Chinese HFRS data from 1975 to 2008 were used to fit ARIMA model. Akaike Information Criterion (AIC) and Ljung-Box test were used to evaluate the constructed models. Subsequently, the fitted ARIMA model was applied to obtain the fitted HFRS incidence from 1978 to 2008 and contrast with corresponding observed values. To assess the validity of the proposed model, the mean absolute percentage error (MAPE) between the observed and fitted HFRS incidence (1978-2008) was calculated. Finally, the fitted ARIMA model was used to forecast the incidence of HFRS of the years 2009 to 2011. All analyses were performed using SAS9.1 with a significant level of p < 0.05.
Results:
The goodness-of-fit test of the optimum ARIMA (0,3,1) model showed non-significant autocorrelations in the residuals of the model (Ljung-Box Q statistic = 5.95,P = 0.3113). The fitted values made by ARIMA (0,3,1) model for years 1978-2008 closely followed the observed values for the same years, with a mean absolute percentage error (MAPE) of 12.20%. The forecast values from 2009 to 2011 were 0.69, 0.86, and 1.21per 100,000 population, respectively.
Conclusion:
ARIMA models applied to historical HFRS incidence data are an important tool for HFRS surveillance in China. This study shows that accurate forecasting of the HFRS incidence is possible using an ARIMA model. If predicted values from this study are accurate, China can expect a rise in HFRS incidence.
