在使用LSTM网络的Alpha和Delta变种中预测COVID-19感染和死亡的时间序列预测
Farnaz Sheikhi1, Zahra Kowsari1
1Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran.
PloS one
|October 20, 2023
概括
深度学习模型可以预测COVID-19变种的传播. 编码解码器LSTM (ED-LSTM) 模型在全球范围内优异地预测阿尔法和德尔塔变种的感染和死亡.
科学领域:
- 流行病学 流行病学
- 计算生物学 计算生物学
- 数据科学数据科学数据科学
背景情况:
- 在COVID-19大流行中,出现了许多令人担忧的变种,每个变种都有不同的传染性和严重程度.
- 在流行病期间,预测建模对于公共卫生政策和资源分配至关重要.
- 阿尔法和三角洲变种表现出显著的结构差异,需要变种特定的预测.
研究的目的:
- 评估深度学习模型来预测COVID-19Alpha和Delta变种的传播.
- 将四个基于LSTM的模型在不同地理区域的预测性能进行比较.
- 确定最有效的模型来预测这些变体的感染和死亡.
主要方法:
- 开发了四种深度学习模型:编码解码器LSTM (ED-LSTM),双向LSTM (Bi-LSTM),卷积LSTM (Conv-LSTM) 和门式循环单元 (GRU).
- 模型经过训练和测试,使用来自伊朗,亚洲和全球地区的数据,考虑到不同的人口密度和生活方式.
- 使用根平均平方误差 (RMSE),平均绝对误差 (MAE) 和平均绝对百分比误差 (MAPE) 来评估性能,并使用弗里德曼测试进行模型排名.
主要成果:
- 在所有评估的地理尺度上,ED-LSTM模型对Alpha和Delta变体都表现出卓越的预测能力.
- 这些模型能够预测多达七天的感染和死亡情况.
- 在预测两种变体的长期趋势方面,ED-LSTM显示出一致的准确性.
结论:
- 深度学习,特别是ED-LSTM,为预测COVID-19变种传播提供了强大的方法.
- 准确的预测有助于积极的公共卫生干预和资源管理.
- 该研究强调了针对不同病毒变体和地理环境量身定制模型的重要性.
相关概念视频
01:18Steps in Outbreak Investigation
135
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:
135
01:11Residuals and Least-Squares Property
7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K
01:03Prediction Intervals
2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.3K
01:07End Point Prediction: Gran Plot
345
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
For potentiometric titration, the Gran plot is created by plotting...
345
01:14Parametric Survival Analysis: Weibull and Exponential Methods
450
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
450