Original Article

Prediction of Mumps Incidence Trend in China Based on Difference Grey Model and Artificial Neural Network Learning

Abstract

Background: We aimed to compare the prediction efficiency of back propagation (BP) network and grey model (GM) (1.1) for mumps infectious diseases and compare the application effect of the two models.

Methods: By calculating the average incidence rate of mumps in January 2014 -2016, we conducted the modeling of the BP time series, GM (1,1) grey model and the combination models of them, and predicted the incidence rate in June 2016 in comparison with the actual one. We compared the quarterly incidence rate to test the two prediction models, and compared the advantages and disadvantages of these models.

Results: R value of BP model was 68.45%, for GM (1,1) was 58.49%, and for combined forecasting model was 86.95%. We used the principal component analysis clustering method to control the samples, and found that the samples were close to the population mean. We found that the GM (1.1) model was more suitable for the prediction of mumps infection mode. We carried out dimension reduction analysis on the model data, and the accuracy of the data after dimension reduction is within the range of Da. For the discrete degree of the data in the combined model, matlab pipeline was used to verify the reliability of the data and results. By calculation after manifold optimization small error probability was P=0.875 and semi mean relative error 2.43%.

Conclusion: BP, GM (1,1) is a better method for modeling the epidemic trend of mumps in China, but the efficiency of prediction is not as high as the combination of them.

1. Chen ZL, Wang XF (2018). [Nondestructive detection of total nitrogen content in leaves of Santalum album based on ST-PCA-BP neural network.]. Ying Yong Sheng Tai Xue Bao, 29(5):1551-1558. [In Chi-nese].
2. Huang L, Huang J, Wang W (2018). The Sustainable Development Assessment of Reservoir Resettlement Based on a BP Neural Network. Int J Environ Res Public Health,15(1): 146.
3. Liang W, Sun F (2019). Competing Endog-enous RNA Network Analysis Reveals Pivotal ceRNAs in Adrenocortical Carci-noma. Front Endocrinol (Lausanne),10:301.
4. Liu L, Zhao T, Ma M, et al (2016). A new gene regulatory network model based on BP algorithm for interrogating differen-tially expressed genes of Sea Urchin. Springerplus, 5(1):1911.
5. Lyu J, Zhang J (2019). BP neural network prediction model for suicide attempt among Chinese rural residents. J Affect Disord,246:465-473.
6. Shi Y, Li Y, Cai M, et al (2019). A Lung Sound Category Recognition Method Based on Wavelet Decomposition and BP Neural Network. Int J Biol Sci, 15(1):195-207.
7. Wang L, Zhou W, Xing Y, et al (2018). A Novel Neural Network Model for Blood Pressure Estimation Using Photo-plethesmography without Electrocardio-gram. J Healthc Eng, 2018:7804243.
8. Wen H, Xie W, Pei J (2016). A Structure-Adaptive Hybrid RBF-BP Classifier with an Optimized Learning Strategy. PLoS One,11(10):e0164719.
9. Belyaletdinova IK, Mitrofanova IV, Ko-zlovskaya LI, et al (2020). Cases of aseptic meningitis after vaccination against mumps in Russia (2009-2019). Public Health, 186:8-11.
10. Hamidi H, Zarrineh M, Es-Haghi A, et al (2020). Rapid and sensitive determination of neomycin and kanamycin in measles, mumps, and rubella vaccine via high-performance liquid chromatography-tandem mass spectrometry using modi-fied super-paramagnetic Fe3O4 nano-spheres. J Chromatogr A, 1625:461343.
11. Wang F, Chen R, Jiang Q, et al (2020). Roles of Sialic Acid, AXL, and MER Receptor Tyrosine Kinases in Mumps Virus Infec-tion of Mouse Sertoli and Leydig Cells. Front Microbiol,11:1292.
12. McPherson TD, Ramirez E, Ringness M, et al (2020). Mumps Cases Disproportion-ately Affecting Persons Living with HIV Infection and Men Who Have Sex with Men - Chicago, Illinois, 2018. MMWR Morb Mortal Wkly Rep, 69(28):909-912.
13. Filippi F, Patrizi A, Sabattini E, et al (2020). Pityriasis lichenoides triggered by mea-sles-mumps-rubella vaccine injection. J Dtsch Dermatol Ges, 18(7):758-760.
14. Yang L, Grenfell BT, Mina MJ (2020). Wan-ing immunity and re-emergence of mea-sles and mumps in the vaccine era. Curr Opin Virol, 40:48-54.
15. Salehzadeh O, Vachon M, Sabourin N, et al (2020). Vertically tapered waveguide spot size converters fabricated via a linewidth controlled grey tone lithography for InP-based photonic integrated circuits. Opt Express, 28(16):23523-23533.
16. Qiu JS, Luo L, Jin RG (2020). [Grey model prediction of occupational diseases in Changsha]. Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi,38(7):508-511.
17. Lawson B, Suppiah S, Rota PA, et al (2020). In vitro inhibition of mumps virus repli-cation by favipiravir (T-705). Antiviral Res,180:104849.
18. Zhou S, Liu N, Shen C, et al (2019). An adaptive Kalman filtering algorithm based on back-propagation (BP) neural network applied for simultaneously detection of exhaled CO and N2O. Spectrochim Acta A Mol Biomol Spectrosc, 223:117332.
19. Zhu Q, Ding J, Ren W, et al (2019). [Study on Risk Assessment Model of in Vitro Diagnostic Reagent Adverse Events Based on BP Neural Network]. Zhongguo Yi Liao Qi Xie Za Zhi,43(2):136-139. [In Chinese].
20. Zha WT, Li WT, Zhou N, et al (2020). Ef-fects of meteorological factors on the in-cidence of mumps and models for pre-diction, China. BMC Infect Dis,20(1):468.
21. Hassan J, Kelly J, De Gascun C (2020). Presence of low mumps-specific IgG in oral fluids is associated with high mumps viral loads. J Clin Virol, 129:104517.
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IssueVol 50 No 7 (2021) QRcode
SectionOriginal Article(s)
DOI https://doi.org/10.18502/ijph.v50i7.6630
Keywords
Mumps Back propagation network Grey model China
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
1.
Jia J, Liu M, Xue Z, Wang Z, Pan Y. Prediction of Mumps Incidence Trend in China Based on Difference Grey Model and Artificial Neural Network Learning. Iran J Public Health. 2021;50(7):1405-1415.