流行病学数据挖掘,以协助食品传播疫情调查
Dandan Tao1, Dongyu Zhang2, Ruofan Hu2
1Vanke School of Public Health, Tsinghua University, Beijing 100084, China.
Foods (Basel, Switzerland)
|October 28, 2023
概括
历史性食品传播疫情的数据挖掘揭示了将污染源与特定食品和病原体联系在一起的模式. 这种方法有助于预测食物载体和病因,增强食品安全和疫情调查.
科学领域:
- 食品安全科学 食品安全科学
- 公共卫生 公共卫生
- 数据科学和数据分析
背景情况:
- 食物传播疾病是一个主要的公共卫生问题,需要有效的方法来识别疫情期间的污染源.
- 历史爆发数据提供了有关爆发因素,食品载体和病因学的宝贵见解,这对于开发食品安全干预至关重要.
研究的目的:
- 通过数据挖掘,揭示食物传播疫情历史数据中隐藏的模式.
- 建立疫情因素,食品载体和病因之间的关系,以改善食品安全战略.
主要方法:
- 统计分析以确定疫情因素和食物来源之间的关联,选择食品载体的重要预测因素.
- 为简单的食物来源 (牛肉,乳制品,蔬菜) 开发一个多项式预测模型.
- 文本挖掘 (SVM,后勤回归,随机森林,naive bayes) 来调查食物载体与病因关系.
- 建立食品成分网络和蒙特卡洛模拟,用于预测引起疫情的成分.
主要成果:
- 一种支持向量机器模型被证明是最佳的,用于预测食品车辆的病因.
- 协会规则确定了与特定病因密切相关的特定食品载体.
- 开发的方法成功地预测了已知的食品和原料来源的历史爆发.
- 该方法表明,它能够根据食品类型预测潜在的成分污染源.
结论:
- 来自历史性食品传播疫情的数据驱动的洞察力可以显著改善早期识别污染源的情况.
- 这种方法提供了一个新的视角和战略,用于利用大数据在食品安全和疫情调查.
- 预测模型可以帮助预测食物载体和病因,从而加强对食品传播疾病的公共卫生干预措施.
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