一个基于食物传播疾病风险的分解-整合的混合预测模型
Ke Qin1, Jingxiang Zhang2, Xiaoting Dai1,3
1School of Business, Jiangnan University, Wuxi, PR China.
Foodborne pathogens and disease
|April 10, 2025
概括
准确预测食物传播疾病 (FBD) 趋势对于公共卫生至关重要. 这项研究开发了一种先进的风险预测模型,大大提高了FBD预测的准确性.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 数据科学数据科学数据科学
背景情况:
- 食物传播疾病 (FBD) 给全球经济和健康带来了重大挑战.
- 准确预测FBD风险趋势是一个关键的公共卫生目标.
- 现有的预测方法需要改进,以提高准确性和可靠性.
研究的目的:
- 开发和验证一种新的食品传播疾病风险预测模型.
- 提高FBD风险预测的准确性和可靠性.
- 为食品安全管理和政策提供数据驱动的支持.
主要方法:
- 在FBD监测数据 (2019-2023,无锡) 上使用了分解整合技术.
- 使用完整集体实证模式分解与自适应噪声将FBD风险数据分解为内在模式函数 (IMF).
- 使用样本和分析时间依赖性与时间卷积网络长期短期记忆 (TCN-LSTM) 模型重建了IMF.
主要成果:
- 拟议的TCN-LSTM模型证明了FBD风险的卓越预测准确性.
- 实现了5.349的平均根平均平方误差和3.819的平均绝对误差.
- 与独立的LSTM模型相比,预测准确度至少提高了40%.
结论:
- 开发的分解整合TCN-LSTM模型在FBD风险预测方面取得了重大进展.
- 该模型为有效的食品安全管理和政策制定提供了有价值的数据支持.
- 增强的FBD风险预测能力使得公共卫生早期预警更加准确和及时.
相关概念视频
01:18Steps in Outbreak Investigation
96
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:
96
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
19
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
19