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Comparison of autoregressive integrated moving average model and generalised regression neural network model for prediction of haemorrhagic fever with renal syndrome in China: a time-series study.
BMJ Open. 2019 06 16; 9(6):e025773.BO

Abstract

OBJECTIVES

Haemorrhagic fever with renal syndrome (HFRS) is a serious threat to public health in China, accounting for almost 90% cases reported globally. Infectious disease prediction may help in disease prevention despite some uncontrollable influence factors. This study conducted a comparison between a hybrid model and two single models in forecasting the monthly incidence of HFRS in China.

DESIGN

Time-series study.

SETTING

The People's Republic of China.

METHODS

Autoregressive integrated moving average (ARIMA) model, generalised regression neural network (GRNN) model and hybrid ARIMA-GRNN model were constructed by R V.3.4.3 software. The monthly reported incidence of HFRS from January 2011 to May 2018 were adopted to evaluate models' performance. Root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were adopted to evaluate these models' effectiveness. Spatial stratified heterogeneity of the time series was tested by month and another GRNN model was built with a new series.

RESULTS

The monthly incidence of HFRS in the past several years showed a slight downtrend and obvious seasonal variation. A total of four plausible ARIMA models were built and ARIMA(2,1,1) (2,1,1)12 model was selected as the optimal model in HFRS fitting. The smooth factors of the basic GRNN model and the hybrid model were 0.027 and 0.043, respectively. The single ARIMA model was the best in fitting part (MAPE=9.1154, MAE=89.0302, RMSE=138.8356) while the hybrid model was the best in prediction (MAPE=17.8335, MAE=152.3013, RMSE=196.4682). GRNN model was revised by building model with new series and the forecasting performance of revised model (MAPE=17.6095, MAE=163.8000, RMSE=169.4751) was better than original GRNN model (MAPE=19.2029, MAE=177.0356, RMSE=202.1684).

CONCLUSIONS

The hybrid ARIMA-GRNN model was better than single ARIMA and basic GRNN model in forecasting monthly incidence of HFRS in China. It could be considered as a decision-making tool in HFRS prevention and control.

Authors+Show Affiliations

School of Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.School of Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.School of Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Pub Type(s)

Comparative Study
Journal Article

Language

eng

PubMed ID

31209084

Citation

Wang, Ya-Wen, et al. "Comparison of Autoregressive Integrated Moving Average Model and Generalised Regression Neural Network Model for Prediction of Haemorrhagic Fever With Renal Syndrome in China: a Time-series Study." BMJ Open, vol. 9, no. 6, 2019, pp. e025773.
Wang YW, Shen ZZ, Jiang Y. Comparison of autoregressive integrated moving average model and generalised regression neural network model for prediction of haemorrhagic fever with renal syndrome in China: a time-series study. BMJ Open. 2019;9(6):e025773.
Wang, Y. W., Shen, Z. Z., & Jiang, Y. (2019). Comparison of autoregressive integrated moving average model and generalised regression neural network model for prediction of haemorrhagic fever with renal syndrome in China: a time-series study. BMJ Open, 9(6), e025773. https://doi.org/10.1136/bmjopen-2018-025773
Wang YW, Shen ZZ, Jiang Y. Comparison of Autoregressive Integrated Moving Average Model and Generalised Regression Neural Network Model for Prediction of Haemorrhagic Fever With Renal Syndrome in China: a Time-series Study. BMJ Open. 2019 06 16;9(6):e025773. PubMed PMID: 31209084.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Comparison of autoregressive integrated moving average model and generalised regression neural network model for prediction of haemorrhagic fever with renal syndrome in China: a time-series study. AU - Wang,Ya-Wen, AU - Shen,Zhong-Zhou, AU - Jiang,Yu, Y1 - 2019/06/16/ PY - 2019/6/19/entrez PY - 2019/6/19/pubmed PY - 2020/7/2/medline KW - autoregressive integrated moving average KW - generalized regression neural network KW - hemorrhagic fever with renal syndrome KW - prediction SP - e025773 EP - e025773 JF - BMJ open JO - BMJ Open VL - 9 IS - 6 N2 - OBJECTIVES: Haemorrhagic fever with renal syndrome (HFRS) is a serious threat to public health in China, accounting for almost 90% cases reported globally. Infectious disease prediction may help in disease prevention despite some uncontrollable influence factors. This study conducted a comparison between a hybrid model and two single models in forecasting the monthly incidence of HFRS in China. DESIGN: Time-series study. SETTING: The People's Republic of China. METHODS: Autoregressive integrated moving average (ARIMA) model, generalised regression neural network (GRNN) model and hybrid ARIMA-GRNN model were constructed by R V.3.4.3 software. The monthly reported incidence of HFRS from January 2011 to May 2018 were adopted to evaluate models' performance. Root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were adopted to evaluate these models' effectiveness. Spatial stratified heterogeneity of the time series was tested by month and another GRNN model was built with a new series. RESULTS: The monthly incidence of HFRS in the past several years showed a slight downtrend and obvious seasonal variation. A total of four plausible ARIMA models were built and ARIMA(2,1,1) (2,1,1)12 model was selected as the optimal model in HFRS fitting. The smooth factors of the basic GRNN model and the hybrid model were 0.027 and 0.043, respectively. The single ARIMA model was the best in fitting part (MAPE=9.1154, MAE=89.0302, RMSE=138.8356) while the hybrid model was the best in prediction (MAPE=17.8335, MAE=152.3013, RMSE=196.4682). GRNN model was revised by building model with new series and the forecasting performance of revised model (MAPE=17.6095, MAE=163.8000, RMSE=169.4751) was better than original GRNN model (MAPE=19.2029, MAE=177.0356, RMSE=202.1684). CONCLUSIONS: The hybrid ARIMA-GRNN model was better than single ARIMA and basic GRNN model in forecasting monthly incidence of HFRS in China. It could be considered as a decision-making tool in HFRS prevention and control. SN - 2044-6055 UR - https://www.unboundmedicine.com/medline/citation/31209084/Comparison_of_autoregressive_integrated_moving_average_model_and_generalised_regression_neural_network_model_for_prediction_of_haemorrhagic_fever_with_renal_syndrome_in_China:_a_time_series_study_ L2 - http://bmjopen.bmj.com/cgi/pmidlookup?view=long&pmid=31209084 DB - PRIME DP - Unbound Medicine ER -