Study on Univariate Modeling and Prediction Methods Using Monthly HIV Incidence and Mortality Cases in China.

IF 1.5 Q4 INFECTIOUS DISEASES

HIV AIDS-Research and Palliative Care Pub Date : 2024-10-24 eCollection Date: 2024-01-01 DOI:10.2147/HIV.S476371

Yuxiao Yang, Xingyuan Gao, Hongmei Liang, Qiuying Yang

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引用次数: 0

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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中国艾滋病月发病率和死亡率的单变量建模和预测方法研究

目的：艾滋病对全球公共卫生造成严重危害。本文使用了五种单一模型：ARIMA、SARIMA、Prophet、BP 神经网络和 LSTM 方法对中国每月艾滋病发病数和死亡数进行建模和预测。我们还提出了 LSTM-SARIMA 组合模型，以提高预测的准确性。这项研究为艾滋病的防治提供了有力的数据支持：方法：我们收集了 2010 年 1 月至 2024 年 2 月中国每月的艾滋病发病和死亡病例数据。其中，2010 年 1 月至 2021 年 2 月的数据用于建模，其余数据用于验证。在建模过程中，我们根据数据集的特征对其进行了处理。研究中的所有模型均使用 Python 3.11.6 进行。同时，我们使用构建的模型预测了 2024 年 3 月至 2024 年 7 月的每月发病率和死亡率。然后，我们使用 RMSE、MAE、MAPE 和 SMAPE 对预测结果进行了评估：结果：LSTM 和 BPNN 深度学习方法在预测死亡病例数方面优于 ARIMA、SARIMA 和 Prophet。在预测艾滋病发病例数时，两类方法差异不大，LSTM 方法的表现略好于其他方法。同时，预测艾滋病死亡病例的平均误差明显低于预测艾滋病发病病例的平均误差。在预测艾滋病发病率和死亡率方面，LSTM-SARIMA 方法优于其他方法：结论：由于艾滋病发病和死亡病例序列的不同特点，不同方法的性能也略有不同。艾滋病死亡率序列比发病率序列更平滑。LSTM-SARIMA组合模型的预测效果优于传统方法和单独的LSTM方法，这对于优化艾滋病的预测结果具有重要的现实意义。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

HIV AIDS-Research and Palliative Care INFECTIOUS DISEASES-

CiteScore

3.00

自引率

6.70%

发文量

审稿时长

16 weeks

期刊介绍： About Dove Medical Press Dove Medical Press Ltd is part of Taylor & Francis Group, the Academic Publishing Division of Informa PLC. We specialize in the publication of Open Access peer-reviewed journals across the broad spectrum of science, technology and especially medicine. Dove Medical Press was founded in 2003 with the objective of combining the highest editorial standards with the ''best of breed'' new publishing technologies. We have offices in Manchester and London in the United Kingdom, representatives in Princeton, New Jersey in the United States, and our editorial offices are in Auckland, New Zealand. Dr Scott Fraser is our Medical Director based in the UK. He has been in full time clinical practice for over 20 years as well as having an active research interest.