将病毒风险排名工具的重点放在预测上
Katherine Budeski1,2, Marc Lipsitch1,2
1Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115.
概括
这就是 SpillOver 工具.
科学领域:
- 流行病学 流行病学
- 传染病的动态传染病的动态.
- 公共卫生准备情况 公共卫生准备情况
背景情况:
- 新出现的传染病构成重大全球威胁,需要早期风险评估工具.
- 溢出:病毒风险排名工具是为了预测从野生动物到人类的动物性传播风险而开发的.
- 该工具中的当前风险因素依赖于溢出后数据,限制了它对新型病毒的预测能力.
研究的目的:
- 在删除溢出依赖风险因素后,重新评估溢出工具的风险排名表现.
- 评估排除传播后知识对该工具对新型病毒的预测准确性的影响.
- 为未来的代确定更合适的,不依赖溢出的风险因素.
主要方法:
- 在删除31个溢出依赖风险因素中的8个之后,重新分析了溢出工具的风险排名.
- 对比了原始与调整后的风险分数的预测性表现 (接收器操作特征曲线下的区域).
- 在因子级别对人类与非人类病毒的风险评分的分析平均值和标准偏差.
主要成果:
- 将病毒归类为人类病原体的预测准确性在删除溢出依赖因素后显著下降 (AUC从0.94降至0.73).
- 排除的溢出依赖因素显示了人类和非人类病毒分类之间的截然不同的平均值.
- 不依赖溢出因素在两种分类中经常表现出类似的平均值,这表明歧视力有限.
结论:
- 最初的 SpillOver 工具依赖于溢出后数据,这阻碍了它预测新型病毒风险的能力.
- 排除溢出依赖因素显著降低了该工具的预测能力.
- 未来的版本应该优先考虑非溢出依赖的风险因素,以提高对新生动物感染威胁的预测准确度.
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