关于使用中国每月艾滋病毒发病率和死亡率的单变模型和预测方法的研究
Yuxiao Yang1,2, Xingyuan Gao3, Hongmei Liang4
1School of Biomedical Engineering, Capital Medical University, Beijing, People's Republic of China.
HIV/AIDS (Auckland, N.Z.)
|October 29, 2024
概括
一个新的LSTM-SARIMA模型准确地预测了中国的艾滋病发病率和死亡率. 这种先进的预测方法为公共卫生干预和艾滋病预防策略提供了至关重要的数据支持.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 艾滋病是一个重大的全球公共卫生挑战.
- 准确预测艾滋病发病率和死亡率对于有效的预防和治疗策略至关重要.
研究的目的:
- 为了模拟和预测中国每月的艾滋病发病率和死亡率.
- 评估单个模型 (ARIMA,SARIMA,Prophet,BPNN,LSTM) 和一个结合LSTM-SARIMA模型的性能.
- 通过先进的建模技术,提高艾滋病病例的预测准确度.
主要方法:
- 从2010年1月到2024年2月,利用了中国的每月艾滋病数据.
- 使用单个模型:ARIMA,SARIMA,Prophet,BP神经网络 (BPNN) 和长短期记忆 (LSTM).
- 开发并验证了一种混合LSTM-SARIMA模型,预测2024年3月至7月的病例,并使用RMSE,MAE,MAPE和SMAPE进行评估.
主要成果:
- 深度学习模型 (LSTM,BPNN) 在死亡率预测方面表现优于传统模型.
- 与其他发生率预测方法相比,LSTM显示出轻微的优势.
- 与单个模型相比,LSTM-SARIMA组合模型在预测艾滋病发病率和死亡率方面表现优越.
结论:
- 由于发病率和死亡率数据系列的不同特征,模型性能有所不同.
- LSTM-SARIMA模型为优化艾滋病预测和支持公共卫生决策提供了实际意义.
- 这项研究为中国的艾滋病预防和治疗工作提供了强有力的数据支持.
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