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Application of a long short-term memory neural network: a burgeoning method of deep learning in forecasting HIV incidence in Guangxi, China

  • G. Wang (a1), W. Wei (a2), J. Jiang (a2), C. Ning (a1), H. Chen (a3), J. Huang (a2), B. Liang (a2), N. Zang (a1) (a2), Y. Liao (a1), R. Chen (a1), J. Lai (a1), O. Zhou (a1), J. Han (a1), H. Liang (a1) (a2) and L. Ye (a2)...

Abstract

Guangxi, a province in southwestern China, has the second highest reported number of HIV/AIDS cases in China. This study aimed to develop an accurate and effective model to describe the tendency of HIV and to predict its incidence in Guangxi. HIV incidence data of Guangxi from 2005 to 2016 were obtained from the database of the Chinese Center for Disease Control and Prevention. Long short-term memory (LSTM) neural network models, autoregressive integrated moving average (ARIMA) models, generalised regression neural network (GRNN) models and exponential smoothing (ES) were used to fit the incidence data. Data from 2015 and 2016 were used to validate the most suitable models. The model performances were evaluated by evaluating metrics, including mean square error (MSE), root mean square error, mean absolute error and mean absolute percentage error. The LSTM model had the lowest MSE when the N value (time step) was 12. The most appropriate ARIMA models for incidence in 2015 and 2016 were ARIMA (1, 1, 2) (0, 1, 2)12 and ARIMA (2, 1, 0) (1, 1, 2)12, respectively. The accuracy of GRNN and ES models in forecasting HIV incidence in Guangxi was relatively poor. Four performance metrics of the LSTM model were all lower than the ARIMA, GRNN and ES models. The LSTM model was more effective than other time-series models and is important for the monitoring and control of local HIV epidemics.

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Copyright

This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

Corresponding author

Author for correspondence: L. Ye, E-mail: yeli@gxmu.edu.cn

Footnotes

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G. Wang and W. Wei contributed equally to this paper.

L. Ye and H. Liang contributed equally to this paper.

Footnotes

References

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1.Xing, J et al. (2014) HIV/AIDS epidemic among older adults in China during 2005–2012: results from trend and spatial analysis. Clinical Infectious Diseases 59, 5360.
2.China, CDC et al. (2018) Update on the AIDS/STD epidemic in China in January, 2018. Chinese Journal of AIDS & STD 24, 219.
3.Zhang, C et al. (2014) Prevalence of HIV, syphilis, and HCV infection and associated risk factors among male clients of low-paying female sex workers in a rural county of Guangxi, China: a cross-sectional study. Sexually Transmitted Infections 90, 230236.
4.Willis, SJ et al. (2018) Chronic hepatitis C virus infection and subsequent HIV viral load among women with HIV initiating antiretroviral therapy. AIDS (London, England) 32, 653661.
5.WHOCSR (2004) WHO Recommended Surveillance Standards, 2nd Edn. WHO. Available at http://www.who.int/csr/resources/publications/surveillance/whocdscsrisr992.pdf (Accessed 17 June 2012).
6.Lyu, P (2016) Discussion of HIV control and prevention strategies. Chinese Journal of Preventive Medicine 50, 841845.
7.Xu, Q et al. (2017) Forecasting influenza in Hong Kong with Google search queries and statistical model fusion. PLoS ONE 12, e0176690.
8.Goodman, KE et al. (2016) A clinical decision tree to predict whether a bacteremic patient is infected with an extended-spectrum beta-lactamase-producing organism. Clinical Infectious Diseases 63, 896903.
9.Zeng, H et al. (2016) Convolutional neural network architectures for predicting DNA-protein binding. Bioinformatics (Oxford, England) 32, 121127.
10.Box, GE and Jenkins, GM (1976) Time series analysis: forecasting and control. Journal of Time 31, 238242.
11.Zheng, YL et al. (2015) Forecast model analysis for the morbidity of tuberculosis in Xinjiang, China. PLoS ONE 10, e0116832.
12.Zeng, Q et al. (2016) Time series analysis of temporal trends in the pertussis incidence in Mainland China from 2005 to 2016. Scientific Reports 6, 32367.
13.Wu, C et al. (2019) Modeling and estimating aboveground biomass of Dacrydium pierrei in China using machine learning with climate change. Journal of Environmental Management 234, 167179.
14.Kim, Y et al. (2018) A novel approach to predicting human ingress motion using an artificial neural network. Journal of Biomechanics 84, 2735.
15.Guan, P et al. (2018) Trends of reported human brucellosis cases in mainland China from 2007 to 2017: an exponential smoothing time series analysis. Environmental Health and Preventive Medicine 23, 23.
16.Lore, KG et al. (2018) A deep learning framework for causal shape transformation. Neural Network 98, 305317.
17.Hochreiter, S and Schmidhuber, J (1997) Long short-term memory. Neural Computation 9, 17351780.
18.Chen, L et al. (2017) Application of LSTM networks in short-term power load forecasting under the deep learning framework. Electric Power Information & Communication Technology 15, 811.
19.Donahue, J et al. (2017) Long-term recurrent convolutional networks for visual recognition and description. IEEE Transactions on Pattern Analysis & Machine Intelligence 39, 677691.
20.Vinyals, O et al. (2014) Show and tell: a neural image caption generator. IEEE Conference on Computer Vision & Pattern Recognition (version 1) 31563164.
21.Lin, Y et al. (2015) Application of an autoregressive integrated moving average model for predicting injury mortality in Xiamen, China. BMJ Open 5, e008491.
22.Dvornek, NC et al. (2017) Identifying autism from resting-state fMRI using long short-term memory networks. International Workshop on Machine Learning in Medical Imaging 10541, 362370.
23.Greff, K et al. (2017) LSTM: a search space odyssey. IEEE Transactions on Neural Networks & Learning Systems 28, 22222232.
24.Gers, FA et al. (2000) Learning to forget: continual prediction with LSTM. Neural Computation 12, 24512471.
25.Specht, DF (1991) A general regression neural network. IEEE Transactions on Neural Networks & Learning Systems 2, 568576.
26.Ghritlahre, HK and Prasad, RK (2018) Exergetic performance prediction of solar air heater using MLP, GRNN and RBF models of artificial neural network technique. Journal of Environmental Management 223, 566575.
27.Wang, H et al. (2018) Time-series analysis of tuberculosis from 2005 to 2017 in China. Epidemiology & Infection 146, 935939.
28.Singh, KP et al. (2013) Predicting acute aquatic toxicity of structurally diverse chemicals in fish using artificial intelligence approaches. Ecotoxicology & Environmental Safety 95, 221233.
29.Wei, W et al. (2016) Application of a combined model with autoregressive integrated moving average (ARIMA) and generalized regression neural network (GRNN) in forecasting hepatitis incidence in Heng County, China. PLoS ONE 11, e0156768.
30.Ke, G et al. (2016) Epidemiological analysis of hemorrhagic fever with renal syndrome in China with the seasonal-trend decomposition method and the exponential smoothing model. Scientific Reports 6, 39350.
31.Pereira, A (2004) Performance of time-series methods in forecasting the demand for red blood cell transfusion. Transfusion 44, 739746.
32.Zhang, J and Nawata, K (2017) A comparative study on predicting influenza outbreaks. Bioscience Trends 11, 533541.
33.Ordonez, FJ and Roggen, D (2016) Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors (Basel Switzerland) 16, 125.
34.Ge, X et al. (2017) Analysis on epidemiological characteristics and trends of HIV/AIDS in Guangxi during 2010–2015. Chinese Journal of AIDS & STD 24, 864866.
35.Harrison, A et al. (2015) Sustained high HIV incidence in young women in Southern Africa: social, behavioral, and structural factors and emerging intervention approaches. Current HIV/AIDS Reports 12, 207215.
36.Alvarez-Uria, G et al. (2012) Trends and risk factors for HIV infection among young pregnant women in rural India. International Journal of Infectious Diseases 16, 121123.
37.Bo, H and Yufang, S (2014) HIV/AIDS-related high risk behaviors among Vietnamese cross-border floating population in the frontiers of Guangxi province. Journal of Applied Preventive Medicine 20, 610.
38.Huo, JL et al. (2016) High risk behaviors of foreign HIV/AIDS patients in China-Vietnam border. Modern Preventive Medicine 43, 43784380.
39.Zhu, J et al. (2012) The potential risk factors analysis of HIV/STD infection in Vietnamese cross-border female sex workers. Journal of Kunming Medical University 10, 145148.
40.Wang, J et al. (2015) Analysis of HIV correlated factors in Chinese and Vietnamese female sex workers in Hekou, Yunnan Province, a Chinese Border Region. PLoS ONE 10, e0129430.
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