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Time series analysis of human brucellosis in mainland China by using Elman and Jordan recurrent neural networks.
BMC Infect Dis. 2019 May 14; 19(1):414.BI

Abstract

BACKGROUND

Establishing epidemiological models and conducting predictions seems to be useful for the prevention and control of human brucellosis. Autoregressive integrated moving average (ARIMA) models can capture the long-term trends and the periodic variations in time series. However, these models cannot handle the nonlinear trends correctly. Recurrent neural networks can address problems that involve nonlinear time series data. In this study, we intended to build prediction models for human brucellosis in mainland China with Elman and Jordan neural networks. The fitting and forecasting accuracy of the neural networks were compared with a traditional seasonal ARIMA model.

METHODS

The reported human brucellosis cases were obtained from the website of the National Health and Family Planning Commission of China. The human brucellosis cases from January 2004 to December 2017 were assembled as monthly counts. The training set observed from January 2004 to December 2016 was used to build the seasonal ARIMA model, Elman and Jordan neural networks. The test set from January 2017 to December 2017 was used to test the forecast results. The root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to assess the fitting and forecasting accuracy of the three models.

RESULTS

There were 52,868 cases of human brucellosis in Mainland China from January 2004 to December 2017. We observed a long-term upward trend and seasonal variance in the original time series. In the training set, the RMSE and MAE of Elman and Jordan neural networks were lower than those in the ARIMA model, whereas the MAPE of Elman and Jordan neural networks was slightly higher than that in the ARIMA model. In the test set, the RMSE, MAE and MAPE of Elman and Jordan neural networks were far lower than those in the ARIMA model.

CONCLUSIONS

The Elman and Jordan recurrent neural networks achieved much higher forecasting accuracy. These models are more suitable for forecasting nonlinear time series data, such as human brucellosis than the traditional ARIMA model.

Authors+Show Affiliations

Department of Epidemiology, School of Public Health, China Medical University, Shenyang, Liaoning, China.Liaoning Provincial Center for Disease Control and Prevention, Shenyang, Liaoning, China.Department of Epidemiology, School of Public Health, China Medical University, Shenyang, Liaoning, China.Department of Mathematics, School of Fundamental Sciences, China Medical University, Shenyang, Liaoning, China.Department of Epidemiology, School of Public Health, China Medical University, Shenyang, Liaoning, China. bszhou@cmu.edu.cn.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31088391

Citation

Wu, Wei, et al. "Time Series Analysis of Human Brucellosis in Mainland China By Using Elman and Jordan Recurrent Neural Networks." BMC Infectious Diseases, vol. 19, no. 1, 2019, p. 414.
Wu W, An SY, Guan P, et al. Time series analysis of human brucellosis in mainland China by using Elman and Jordan recurrent neural networks. BMC Infect Dis. 2019;19(1):414.
Wu, W., An, S. Y., Guan, P., Huang, D. S., & Zhou, B. S. (2019). Time series analysis of human brucellosis in mainland China by using Elman and Jordan recurrent neural networks. BMC Infectious Diseases, 19(1), 414. https://doi.org/10.1186/s12879-019-4028-x
Wu W, et al. Time Series Analysis of Human Brucellosis in Mainland China By Using Elman and Jordan Recurrent Neural Networks. BMC Infect Dis. 2019 May 14;19(1):414. PubMed PMID: 31088391.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Time series analysis of human brucellosis in mainland China by using Elman and Jordan recurrent neural networks. AU - Wu,Wei, AU - An,Shu-Yi, AU - Guan,Peng, AU - Huang,De-Sheng, AU - Zhou,Bao-Sen, Y1 - 2019/05/14/ PY - 2018/08/27/received PY - 2019/04/26/accepted PY - 2019/5/16/entrez PY - 2019/5/16/pubmed PY - 2019/7/10/medline KW - Human brucellosis KW - Recurrent neural network KW - Time series analysis SP - 414 EP - 414 JF - BMC infectious diseases JO - BMC Infect. Dis. VL - 19 IS - 1 N2 - BACKGROUND: Establishing epidemiological models and conducting predictions seems to be useful for the prevention and control of human brucellosis. Autoregressive integrated moving average (ARIMA) models can capture the long-term trends and the periodic variations in time series. However, these models cannot handle the nonlinear trends correctly. Recurrent neural networks can address problems that involve nonlinear time series data. In this study, we intended to build prediction models for human brucellosis in mainland China with Elman and Jordan neural networks. The fitting and forecasting accuracy of the neural networks were compared with a traditional seasonal ARIMA model. METHODS: The reported human brucellosis cases were obtained from the website of the National Health and Family Planning Commission of China. The human brucellosis cases from January 2004 to December 2017 were assembled as monthly counts. The training set observed from January 2004 to December 2016 was used to build the seasonal ARIMA model, Elman and Jordan neural networks. The test set from January 2017 to December 2017 was used to test the forecast results. The root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to assess the fitting and forecasting accuracy of the three models. RESULTS: There were 52,868 cases of human brucellosis in Mainland China from January 2004 to December 2017. We observed a long-term upward trend and seasonal variance in the original time series. In the training set, the RMSE and MAE of Elman and Jordan neural networks were lower than those in the ARIMA model, whereas the MAPE of Elman and Jordan neural networks was slightly higher than that in the ARIMA model. In the test set, the RMSE, MAE and MAPE of Elman and Jordan neural networks were far lower than those in the ARIMA model. CONCLUSIONS: The Elman and Jordan recurrent neural networks achieved much higher forecasting accuracy. These models are more suitable for forecasting nonlinear time series data, such as human brucellosis than the traditional ARIMA model. SN - 1471-2334 UR - https://www.unboundmedicine.com/medline/citation/31088391/Time_series_analysis_of_human_brucellosis_in_mainland_China_by_using_Elman_and_Jordan_recurrent_neural_networks_ L2 - https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-019-4028-x DB - PRIME DP - Unbound Medicine ER -