流行病学预测的深度学习框架:一项关于巴西亚马逊州帕拉州COVID-19病例和死亡的研究
Gilberto Nerino de Souza1, Alícia Graziella Balbino Mendes2, Joaquim Dos Santos Costa1
1Universidade Federal Rural da Amazônia, Paragominas Campus, Paragominas, Pará, Brazil.
PloS one
|November 17, 2023
概括
这项研究引入了使用深度学习模型在巴西进行COVID-19时间序列预测的新框架. 该框架准确预测病例和死亡,帮助公共卫生响应.
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:
- 对于COVID-19等传染病的时间序列建模是复杂的,因为环境变化,数据不确定性和可变依赖性.
- 准确的预测对于流行病期间有效的公共卫生干预至关重要.
研究的目的:
- 开发和评估巴西帕拉州COVID-19时间序列预测的框架.
- 评估各种深度学习模型 (TCN,变压器,TFT,N-BEATS,N-HiTS) 的性能,以预测COVID-19病例和死亡.
主要方法:
- 采用了一个包含深度学习模型 (TCN,变压器,TFT,N-BEATS,N-HiTS) 和ARIMA后处理的框架.
- 纳入的多变量数据包括每日病例,死亡,症状发作和疫苗接种率.
- 员工统计评估指标 (MSE,RMSE,MAPE,sMAPE,r2,CV) 用于7天移动平均线预测.
主要成果:
- 深度学习模型,特别是N-HiTS和N-BEATS,在预测COVID-19趋势方面表现强.
- 病例发布的平均误差为5.4%,病例症状为8.0%,死亡发布的平均误差为11.12%,死亡发生的平均误差为4.6%.
- 该框架提供具有多变量支持的概率预测.
结论:
- 深度学习模型为分析和预测COVID-19的传播提供了一种有价值的方法.
- 开发的框架可以增强对流行病爆发的理解和应对策略.
- 准确的预测支持公共卫生管理人员做出明智的决策.
相关概念视频
01:25Statistical Methods for Analyzing Epidemiological Data
376
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:
376
01:18Steps in Outbreak Investigation
134
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:
134
01:26Introduction to Epidemiology
743
Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...
743
01:26Principles of Disease Surveillance
104
Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
104
01:21Causality in Epidemiology
431
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
431
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