Research of Combined ES-BP Model in Predicting Syphilis Incidence 1982-2020 in Mainland China
Abstract
Background: Syphilis remains a major public health concern in China. We aimed to construct an optimum model to forecast syphilis epidemic trends and provide effective precautionary measures for prevention and control.
Methods: Data on the incidence of syphilis between 1982 and 2020 were obtained from the China Health Statistics Yearbook. An exponential smoothing model (ES model) and a BP neural network model were constructed, and on this basis, the ES-BP combination model was created. The prediction performance was assessed to compare the MAE (Mean Absolute Error), MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Square Error).
Results: The optimum ES model was Brown’s linear trend model, which had the lowest MAE and MAPE values, and its residual was a white noise sequence (P=0.359). The optimum BP neural network model had three layers with the number of nodes in the input, hidden, and output layers set to 5, 11, and 1, and the mean values of MAE, MSE, and RMSE by five-fold cross-validation were 1.519, 6.894, and 1.969, respectively. The ES-BP combination model had three layers, with model nodes 1, 4, and 1. The lowest mean values of MAE, MSE, and RMSE obtained by five-fold cross-validation were 1.265, 5.739, and 2.105, respectively.
Conclusion: The ES, BP neural network, and ES-BP combination models can be used to predict syphilis incidence, but the prediction performance of the ES-BP combination model is better than that of a basic ES model and a basic BP neural network model.
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29. Zhao D, Zhang H, Cao Q, er al (2022). The research of ARIMA, GM(1,1), and LSTM models for prediction of TB cases in China. PLoS One, 17(2): e0262734.
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31. Wu W, Guo J, An S, et al (2015). Compari-son of Two Hybrid Models for Fore-casting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China. PLoS One, 10(8):e0135492.
32. Wang Y, Xu C, Wang Z, et al (2018). Time series modeling of pertussis incidence in China from 2004 to 2018 with a novel wavelet based SARIMA-NAR hybrid model. PLoS One, 13(12):e0208404.
33. Zhu Z, Zhu X, Zhan Y, et al (2022). Devel-opment and comparison of predictive models for sexually transmitted diseas-es-AIDS, gonorrhea, and syphilis in China, 2011-2021. Front Public Health, 10:966813.
2. Wan Z, Zhang H, Xu H, et al (2020). Mater-nal syphilis treatment and pregnancy outcomes: a retrospective study in Jiang-xi Province, China. BMC Pregnancy Child-birth, 20(1):648.
3. Tuddenham S, Ghanem KG (2015). Emerg-ing trends and persistent challenges in the management of adult syphilis. BMC Infect Dis, 15:351.
4. Ghanem KG, Ram S, Rice PA (2020). The Modern Epidemic of Syphilis. N Engl J Med, 382(9):845-854.
5. Peeling RW, Mabey D, Kamb ML, et al (2017). Syphilis. Nat Rev Dis Primers, 3:17073.
6. Zhang X, Zhang T, Pei J, et al (2016). Time Series Modelling of Syphilis Incidence in China from 2005 to 2012. PLoS One, 11(2):e0149401.
7. Chen X, Li G, Gan Y, et al (2019). Availa-bility of benzathine penicillin G for syphilis treatment in Shandong Prov-ince, Eastern China. BMC Health Serv Res, 19(1):188.
8. Guo X, Liu S, Wu L, et al (2014). Applica-tion of a novel grey self-memory cou-pling model to forecast the incidence rates of two notifiable diseases in China: dysentery and gonorrhea. PLoS One, 9(12):e115664.
9. Li R H, Huang J, Luo S Y (2020). Prediction of syphilis incident rate and number in China based on the GM (1, 1) grey model. Food Therapy and Health Care, 2(4): 170-175.
10. Ibáñez-Cervantes G, León-García G, Var-gas-De-León C, et al (2020). Epidemio-logical behavior and current forecast of syphilis in Mexico: increase in male population. Public Health, 185:386-393.
11. Zhang X, Zhang T, Young AA, et al (2014). Applications and comparisons of four time series models in epidemiological surveillance data. PLoS One, 9(2):e88075.
12. Guan P, Wu W, Huang D (2018). Trends of reported human brucellosis cases in mainland China from 2007 to 2017: an exponential smoothing time series anal-ysis. Environ Health Prev Med, 23(1):23.
13. Wang XY, Yu Y, Hu Y, et al (2020). COVID-19 analysis and forecast based on Ex-ponential Smoothing Model in Hubei Province. J Public Health Pre Med, (6): 1-4. (in Chinese)
14. Rostami M, Jalilian A, Poorolajal J, et al (2019). Time Series Analysis of Monthly Suicide Rates in West of Iran, 2006-2013. Int J Prev Med, 10:78.
15. Zhang YQ, Li XX, Li WB, et al (2020). Analysis and predication of tuberculosis registration rates in Henan Province, China: an exponential smoothing model study. Infect Dis Poverty, 9(1):123.
16. Shang Y, Zhang TT, Wang ZF, et al (2022). Spatial epidemiological characteristics and exponential smoothing model ap-plication of tuberculosis in Qinghai Plateau, China. Epidemiol Infect, 150:e37.
17. Liu T, Zou G (2021). Evaluation of Mechan-ical Properties of Materials Based on Genetic Algorithm Optimizing BP Neu-ral Network. Comput Intell Neurosci, 2021:2115653.
18. Fan K, Gu X (2021). Image Quality Evalua-tion of Sanda Sports Video Based on BP Neural Network Perception. Comput Intell Neurosci, 2021:5904400.
19. Liu W, Bao C, Zhou Y, er al (2019). Fore-casting incidence of hand, foot and mouth disease using BP neural networks in Jiangsu province, China. BMC Infect Dis, 19(1):828.
20. Alim M, Ye GH, Guan P (2020). Compari-son of ARIMA model and XGBoost model for prediction of human brucel-losis in mainland China: a time-series study. BMJ Open, 10(12):e039676.
21. Li Z, Li Y (2020). A comparative study on the prediction of the BP artificial neural network model and the ARIMA model in the incidence of AIDS. BMC Med In-form Decis Mak, 20(1):143.
22. Wu L, Liu S, Yang Y (2016). Grey double exponential smoothing model and its application on pig price forecasting in China. Applied Soft Computing, 39: 117-123.
23. Chen L, Jagota V, Kumar A (2021). Re-search on optimization of scientific re-search performance management based on BP neural network. Int J Syst Assur Eng Manag , 1-10.
24. Lyu J, Zhang J (2019). BP neural network prediction model for suicide attempt among Chinese rural residents. J Affect Disord, 246:465-473.
25. Lu S (2021). Research on GDP Forecast Analysis Combining BP Neural Network and ARIMA Model. Comput Intell Neurosci, 2021:1026978.
26. Feng W, Feng F (2022). Research on the Multimodal Digital Teaching Quality Data Evaluation Model Based on Fuzzy BP Neural Network. Comput Intell Neuro-sci, 2022:7893792.
27. Jia J, Liu M, Xue Z, et al (2021). Prediction of Mumps Incidence Trend in China Based on Difference Grey Model and Artificial Neural Network Learning. Iran J Public Health, 50(7):1405-1415.
28. Zhou H, Zhang H, Yang C (2020). Hybrid-model-based intelligent optimization of ironmaking process. IEEE Transactions on Industrial Electronics, 67(3):2469-2479.
29. Zhao D, Zhang H, Cao Q, er al (2022). The research of ARIMA, GM(1,1), and LSTM models for prediction of TB cases in China. PLoS One, 17(2): e0262734.
30. Wang Y, Xu C, Zhang S, et al (2019). Tem-poral trends analysis of tuberculosis morbidity in mainland China from 1997 to 2025 using a new SARIMA-NARNNX hybrid model. BMJ Open, 9(7):e024409.
31. Wu W, Guo J, An S, et al (2015). Compari-son of Two Hybrid Models for Fore-casting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China. PLoS One, 10(8):e0135492.
32. Wang Y, Xu C, Wang Z, et al (2018). Time series modeling of pertussis incidence in China from 2004 to 2018 with a novel wavelet based SARIMA-NAR hybrid model. PLoS One, 13(12):e0208404.
33. Zhu Z, Zhu X, Zhan Y, et al (2022). Devel-opment and comparison of predictive models for sexually transmitted diseas-es-AIDS, gonorrhea, and syphilis in China, 2011-2021. Front Public Health, 10:966813.
| Files | ||
| Issue | Vol 52 No 10 (2023) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/ijph.v52i10.13844 | |
| Keywords | ||
| Syphilis Exponential smoothing BP neural network Incidence China | ||
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How to Cite
1.
Zhao D. Research of Combined ES-BP Model in Predicting Syphilis Incidence 1982-2020 in Mainland China. Iran J Public Health. 2023;52(10):2063-2072.
