推断诺罗病毒在单个中的分布低于通过聚合采样量化的量化限值
Jianxin Wang1, Chen Zhou1, Yeru Wang1,2
1School of Information, Beijing Forestry University, Beijing, China.
Risk analysis : an official publication of the Society for Risk Analysis
|September 30, 2023
概括
一种新方法推断了诺罗病毒 (NoV) 在中的分布,即使在检测极限以下. 这通过准确量化病毒载荷来解决食品安全风险,以便更好地评估公共卫生.
科学领域:
- 食品安全 食品安全
- 病毒学 病毒学
- 统计建模 统计建模
背景情况:
- 中的诺罗病毒 (NoV) 构成了严重的食品安全问题.
- 准确的风险评估需要了解每只的NoV副本的完整分布.
- 实验室方法难以量化NoV低于量化的极限,阻碍了风险评估.
研究的目的:
- 开发一种系统的方法来推断诺罗病毒 (NoV) 在中无法观察到的分布.
- 克服实验室检测方法的局限性,以量化低病毒载荷.
- 为了能够全面评估因食用而导致的NoV暴露的风险.
主要方法:
- 提出了使用聚合样本 (n=2) 的聚合样本 (DIPS) 的分布推断方法.
- 采用卷积积分和一个实编码的遗传算法来推断分布.
- 该方法不需要关于分布类型,历史数据或可观察和不可观察部分之间的关系的假设.
主要成果:
- 在各种模拟数据集 (正常,均,马,lognormal,零膨胀的波桑等) 上进行实验. 显示了很高的准确性.
- 推断分布与原始分布非常接近,通过了科尔莫戈罗夫-斯米尔诺夫测试.
- 为了实践应用DIPS方法,开发了一个Web系统.
结论:
- 在DIPS方法有效地推断诺罗病毒 (NoV) 在的分布,即使在有限的检测.
- 这种方法提高了食品传播疾病风险评估的准确性.
- 开发的网络系统为研究NoV和其他污染物的研究人员提供了宝贵的工具.
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