一种新的双向LSTM深度学习方法用于COVID-19预测
Nway Nway Aung1, Junxiong Pang2,3, Matthew Chin Heng Chua4
1Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, Singapore, 119615, Singapore. nwaynwayaung.lily@gmail.com.
Scientific reports
|October 20, 2023
概括
一个深度学习模型准确地使用历史数据提前14天预测每日COVID-19病例. 这种双向长期短期记忆 (Bi-LSTM) 方法显示出对流行病预测的希望,即使变量较少.
科学领域:
- 流行病学 流行病学
- 数据科学数据科学数据科学
- 计算生物学 计算生物学
背景情况:
- COVID-19导致了全球显著的发病率和死亡率.
- 准确预测COVID-19病例对于公共卫生反应至关重要.
- 流行病的早期阶段对预测建模提出了独特的挑战.
研究的目的:
- 开发和验证用于预测每日COVID-19病例的深度学习模型.
- 评估该模型在190个国家/地区的流行病早期的表现.
- 将深度学习模型的准确性与经典ARIMA模型进行比较.
主要方法:
- 使用双向长期短期记忆 (Bi-LSTM) 深度学习架构.
- 从2020年1月到2021年1月,对每日确诊病例,复制数,政策措施,流动性和飞行数据进行培训的模型.
- 预计190个国家的每日新增COVID-19病例提前14天.
主要成果:
- 两种LSTM模型的准确性相似,在总绝对百分比误差方面表现优于ARIMA模型.
- 在两种Bi-LSTM模型中,平均绝对误差 (MAE) 的中位数分别为157和150.
- 病例数量较高和感染波较多的国家通常有更准确的预测.
结论:
- 使用Bi-LSTM架构的深度学习方法有效地预测了COVID-19病例在早期的流行病.
- 可以利用开源数据进行可靠的流行病学预测.
- 模型的准确性可以通过减少一组输入变量来保持.
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