一种贝叶斯方法,用于在食品传播疾病爆发调查期间的暴露流行率比较
Mohammed A Khan1,2, Beau B Bruce1, Lyndsay Bottichio1
1Division of Foodborne, Waterborne, and Environmental Diseases, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
Foodborne pathogens and disease
|August 14, 2023
概括
一种新的贝叶斯方法通过更好地估计暴露患病率来改善食品传播疫情的调查. 这种方法为鉴定疾病源的传统统计测试提供了更好的替代方案.
科学领域:
- 流行病学 流行病学
- 统计 统计 统计 统计
- 食品安全 食品安全
背景情况:
- 食物传播疾病的爆发需要及时识别来源,以保护公共健康.
- 目前的调查经常使用单样双项测试来比较病例暴露与一般人口数据.
- 这种方法在考虑基准人口暴露估计中的不确定性方面存在局限性.
研究的目的:
- 引入和评估一个贝叶斯的替代品,以一个样本的二项式测试为食品传播疫情的调查.
- 评估贝叶斯方法在计算暴露流行不确定性的有用性.
- 为了比较贝叶斯方法的性能与传统的二项式测试,使用现实世界的疫情情情景.
主要方法:
- 开发了一个贝叶斯统计模型来估计暴露患病率.
- 该模型应用于2020年与绿叶蔬菜相关的Escherichia coli O157:H7疫情.
- 病例中的暴露患病率与2018-2019年食品网人口调查数据进行了比较.
- 在调查期间,在多个时间点进行了前性模拟.
主要成果:
- 贝叶斯的方法产生后期的概率增加了叶绿的消费率,因为更多的病例患者接受了采访.
- 在传统的二项式测试达到统计学意义之前,观察到特定的叶绿物品的高概率 (>0.70).
- 在调查的早期,贝叶斯方法在检测关联方面表现出更高的灵敏度.
结论:
- 贝叶斯的方法来评估暴露在食物传播疫情的流行率可能比标准的单样本二项测试更有效.
- 这种方法通过更好地处理暴露不确定性,提高了识别疫情媒介的能力.
- 这些发现表明,食品传播疫情调查的速度和准确性可能会有所改善.
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