ArticleEpidemiology and infection2019
Application of a long short-term memory neural network: a burgeoning method of deep learning in forecasting HIV incidence in Guangxi, China.
Article in Epidemiology and infection, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 citing papers in PubMed, 51 citations in OpenAlex.
- An ARIMAX model integrating Baidu index for improved hand, foot, and mouth disease incidence prediction.Scientific reports · 2026Article
- Multidimensional Trends and AI-Based Forecasts of HIV Incidence in Korea, 1985-2024.Journal of epidemiology and global health · 2026Article
- G20 Country Burden of High BMI-Related Liver Cancer: Trends from 1990 to 2021 and Projections to 2050.Journal of epidemiology and global health · 2026Article
- Forecasting monthly AIDS incidence in China via LSTM-CNN parallel fusion: a comparative study of 10 predictive models.Frontiers in public health · 2026Article
- Epidemiological characteristics and incidence prediction of varicella from 2014 to 2023 in Chongqing, China.Frontiers in public health · 2026Article
- Mathematical modeling and neural network based fitting of HIV/AIDS data in the workingclass population case study from Ethiopia.Scientific reports · 2025Article
- Statistical modelling and forecasting of HIV and anti-retroviral therapy cases by time-series and machine learning models.Scientific reports · 2025Article
- Study on the prediction performance of AIDS monthly incidence in Xinjiang based on time series and deep learning models.BMC public health · 2025Article
- A comparative study of time series foundation models for hand, foot, and mouth disease forecasting: TimesFM, Moirai, and traditional approaches.Frontiers in public health · 2025Article
- A comparative analysis of classical and machine learning methods for forecasting TB/HIV co-infection.Scientific reports · 2024Article
- Study on Univariate Modeling and Prediction Methods Using Monthly HIV Incidence and Mortality Cases in China.HIV/AIDS (Auckland, N.Z.) · 2024Article
- Machine Learning Prediction Model of Tuberculosis Incidence Based on Meteorological Factors and Air Pollutants.International journal of environmental research and public health · 2023Article
- Prediction of outpatients with conjunctivitis in Xinjiang based on LSTM and GRU models.PloS one · 2023Article
- Comparison of ARIMA model, DNN model and LSTM model in predicting disease burden of occupational pneumoconiosis in Tianjin, China.BMC public health · 2022Article
- Predicting HIV Status among Men Who Have Sex with Men in Bulawayo & Harare, Zimbabwe Using Bio-Behavioural Data, Recurrent Neural Networks, and Machine Learning Techniques.Tropical medicine and infectious disease · 2022Article
- A Combined Model of SARIMA and Prophet Models in Forecasting AIDS Incidence in Henan Province, China.International journal of environmental research and public health · 2022Article
- Comparison of ARIMA and LSTM for prediction of hemorrhagic fever at different time scales in China.PloS one · 2022Article
- Development and comparison of predictive models for sexually transmitted diseases-AIDS, gonorrhea, and syphilis in China, 2011-2021.Frontiers in public health · 2022Article
- Application of artificial intelligence and machine learning for HIV prevention interventions.The lancet. HIV · 2022Review
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Authors and funding
15 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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