ArticleHIV/AIDS (Auckland, N.Z.)2024
Study on Univariate Modeling and Prediction Methods Using Monthly HIV Incidence and Mortality Cases in China.
Article in HIV/AIDS (Auckland, N.Z.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Comparison of growth, time series, and hybrid models for HIV infectious disease incidence in adults in Saudi Arabia.BMC infectious diseases · 2026Article
- Forecasting monthly AIDS incidence in China via LSTM-CNN parallel fusion: a comparative study of 10 predictive models.Frontiers in public health · 2026Article
- Study on the spatial-temporal evolution and influencing factors of AIDS incidence in China from 2006 to 2022.BMC infectious diseases · 2025Article
- Multiple change point analysis and time series forecast of brucellosis reports in Xinjiang, China from 2010 to 2024.BMC public health · 2025Article
- HIV drug resistance and its associated factors among patients during interruption of antiretroviral therapy in China.Frontiers in microbiology · 2025Article
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4 authors.
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Abstract
Purpose: AIDS presents serious harms to public health worldwide. In this paper, we used five single models: ARIMA, SARIMA, Prophet, BP neural network, and LSTM method to model and predict the number of monthly AIDS incidence cases and mortality cases in China. We have also proposed the LSTM-SARIMA combination model to enhance the accuracy of the prediction. This study provides strong data support for the prevention and treatment of AIDS. Methods: We collected data on monthly AIDS incidence cases and mortality cases in China from January 2010 to February 2024. Among them, for modeling, we used data from January 2010 to February 2021 and the rest for validation. Treatments were applied to the dataset based on its characteristics during modeling. All models in our study were performed using Python 3.11.6. Meanwhile, we used the constructed model to predict monthly incidence and mortality cases from March 2024 to July 2024. We then evaluated our prediction results using RMSE, MAE, MAPE, and SMAPE. Results: The deep learning methods of LSTM and BPNN outperform ARIMA, SARIMA, and Prophet in predicting the number of mortality cases. When predicting the number of AIDS incidence cases, there is little difference between the two types of methods, and the LSTM method performs slightly better than the rest of the methods. Meanwhile, the average error in predicting AIDS mortality cases is significantly lower than in predicting AIDS incidence cases. The LSTM-SARIMA method outperforms other methods in predicting AIDS incidence and mortality. Conclusion: Due to the different characteristics of the AIDS incidence and mortality cases series, the performance of distinct methods is slightly different. The AIDS mortality series is smoother than the incidence series. The combined LSTM-SARIMA model outperforms the traditional method in prediction and the LSTM method alone, which is of practical significance for optimizing the prediction results of AIDS.
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