使用LSTM预测COVID-19患者数量
Muhammad Iqbal1, Feras Al-Obeidat2, Fahad Maqbool1
1Department of Computer Science and Information TechnologyUniversity of Sargodha Sargodha 40100 Pakistan.
IEEE transactions on computational social systems
|November 20, 2023
概括
这项研究使用长短期记忆 (LSTM) 模型来预测巴基斯坦的COVID-19患者数量. 该LSTM模型准确估计了患者人数,有助于资源分配和公共卫生规划.
科学领域:
- 流行病学 流行病学
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- 由于COVID-19大流行,需要准确预测患者数量,以便有效地应对公共卫生问题.
- COVID-19的指数式传播模式需要计算建模来分配资源和医院负载管理.
- 准确的患者体积估计对于流行病期间的政府决策至关重要.
研究的目的:
- 用长短期记忆 (LSTM) 神经网络预测巴基斯坦COVID-19患者的数量.
- 评估LSTM模型在预测COVID-19患者数量的有效性.
- 将LSTM模型的预测与现有模型进行准确性比较.
主要方法:
- 使用长短期记忆 (LSTM) 循环神经网络 (RNN) 进行时间序列预测.
- 在2020年3月至5月的巴基斯坦COVID-19数据上训练了RNN模型.
- 预测了2020年6月COVID-19阳性患者的百分比,并计算了平均绝对百分比误差 (MAPE).
主要成果:
- 在巴基斯坦,LSTM模型在预测COVID-19患者数量方面表现出有效性.
- 在各种LSTM单元,批量大小和时代中分析了模型性能.
- 预测的患者数量与实际患者数据密切匹配,优于比较预测模型.
结论:
- LSTM模型为预测COVID-19等传染病爆发提供了一种可行的方法.
- 准确的患者数量预测支持知情的公共卫生战略和资源管理.
- 开发的LSTM模型为估计巴基斯坦未来的COVID-19患者负载提供了可靠的工具.
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