通过多因素驱动的长短期记忆 (LSTM) 模型预测COVID-19病例
Yanwen Shao1, Tsz Kin Wan2, Kei Hang Katie Chan3,4,5
1Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China.
Scientific reports
|February 10, 2025
概括
这项研究分析了影响COVID-19在15个国家传播的因素,使用机器学习来预测新病例. 长短期记忆 (LSTM) 模型显示出最好的预测准确度,为有效的公共卫生政策提供了洞察力.
科学领域:
- 流行病学和公共卫生.
- 数据科学和机器学习
背景情况:
- 自2019年12月以来,COVID-19大流行已经造成了全球显著的死亡率和经济破坏.
- 了解影响因素和预测未来趋势对于有效的流行病管理至关重要.
研究的目的:
- 为了确定影响COVID-19新病例在不同国家增加的关键因素.
- 使用历史数据开发和评估未来COVID-19趋势的预测模型.
主要方法:
- 从15个国家收集了大约900天的数据,包括流行病信息,国家特征,气候和预防政策 (44个特征).
- 采用特征选择来识别具有影响力的变量.
- 训练并比较长期短期记忆 (LSTM),支持向量回归器 (SVR) 和时间卷积网络 (TCN) 模型用于病例预测.
主要成果:
- 确定了影响不同国家群体内新增COVID-19病例的重要因素.
- 与SVR和TCN相比,LSTM模型表现出优越的性能和概括能力.
- 德国 (0.864),意大利 (0.860) 和美国 (0.766) 实现了最高的预测准确度 (解释变异得分).
结论:
- 该研究为预测COVID-19新病例提供了有价值的见解,适用于更多的国家和地区.
- 调查结果可以为政府的政策决策提供信息,以便制定更有效的COVID-19预防策略.
相关概念视频
01:18Steps in Outbreak Investigation
102
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
102
01:35Long-term Potentiation
54.7K
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
54.7K
02:39lncRNA - Long Non-coding RNAs
8.5K
In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
8.5K