一个基于时间序列的机器学习策略,用于基于废水的预测和COVID-19动态的现在预测
Mallory Lai1, Yongtao Cao2, Shaun S Wulff1
1Department of Mathematics and Statistics, University of Wyoming, Laramie, USA.
The Science of the total environment
|July 1, 2023
概括
基于废水的流行病学 (WBE) 与机器学习 (ML) 结合,为预测COVID-19病例提供了一种经济有效和高效的方法. 这种方法增强了疾病监测和流行病准备,当直接测试具有挑战性时.
科学领域:
- 环境科学环境科学
- 流行病学 流行病学
- 机器学习是机器学习.
背景情况:
- 通过测试直接监测COVID-19面临诸多挑战,包括成本,延迟和个人选择.
- 基于废水的流行病学 (WBE) 提供了一种补充方法来跟踪疾病的流行和动态.
研究的目的:
- 将WBE数据与机器学习集成,用于现在预测和预测COVID-19病例.
- 评估WBE在预测新的每周COVID-19病例数量的有效性和可解释性.
- 确定短期和长期预测模型的最佳特征.
主要方法:
- 使用时间序列机器学习 (TSML) 策略来分析时间WBE数据.
- 纳入了相关的时间变量,包括环境和水温.
- 用特征工程来提高模型性能和可解释性.
主要成果:
- 机器学习显著提高了WBE在COVID-19监测中的性能和可解释性.
- 特定特征被确定为不同现在预测和预测时间的最佳特征.
- 该TSML方法证明了与传统预测方法相匹配或优越的性能.
结论:
- 基于ML的WBE为COVID-19病例预测提供了强大的替代方案,特别是当直接测试有限时.
- 这种方法有助于研究人员,政策制定者和公共卫生从业人员在疫情防控方面的准备.
- 该研究强调了ML集成WBE在未来的流行病监测中的潜力.
更多相关视频
09:26Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples
Published on: June 30, 2023
1.2K
06:54Author Spotlight: Understanding and Detecting Environmental Antimicrobial Resistance by Combining Culture-Based Techniques and Genomics
Published on: July 19, 2024
1.1K
相关概念视频
01:18Steps in Outbreak Investigation
154
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:
154
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
01:25Statistical Methods for Analyzing Epidemiological Data
425
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
425
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
01:24Rapidly Varying Flow
98
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
98
01:17Design Example: Analyzing Capacity Contours for Flood Risk Assessment
72
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
72