利用互联网搜索趋势和废水监测来识别社区传染病爆发的情况
Alessandro Zulli1, Zoe Zhang1, Madelena Ruedaflores1
1Department of Chemical and Environmental Engineering, Yale University, New Haven, Connecticut 06511, United States.
Environmental science & technology
|February 12, 2025
概括
这项研究表明,谷歌趋势数据可以有效地预测流感和诺罗病毒等疾病的废水病毒水平. 这为早期发现疫情和公共卫生监测提供了快速,负担得起的工具.
科学领域:
- 环境微生物学环境微生物学
- 流行病学 流行病学
- 数字流行病学数字流行病学
背景情况:
- 废水监测是监测传染病的一个关键工具.
- 传统的临床数据可能会有报告延迟,限制及时的公共卫生干预.
- 数字数据源为快速,低成本的公共卫生洞察提供了潜力.
研究的目的:
- 调查谷歌趋势搜索数据对建模和预测废水病毒度的实用性.
- 评估谷歌趋势,废水数据和各种病毒病原体的临床病例之间的相关性.
- 使用谷歌趋势开发和验证病毒爆发的预测模型.
主要方法:
- 谷歌趋势搜索术语与废水病毒度之间的相关性分析.
- 开发了三种不同的建模方法:简单的线性,逐步选择和主要组件分析.
- 模型的验证,使用诺罗病毒疫情的案例研究.
主要成果:
- 在谷歌趋势和废水度之间观察到强烈的相关性,用于A型流感 (R2=0.76) 和呼吸道同胞病毒 (R2=0.66).
- 对于诺罗病毒和mopox发现了显著的相关性,即使临床数据有限.
- 预测模型对诺罗病毒 (高达R2=0.66) 和mopox (高达R2=0.60) 显示出强的性能.
结论:
- 谷歌趋势数据为基于废水的传染病监测提供了有价值的补充流.
- 与临床数据相比,这种方法可以提供较早的疫情检测,特别是当临床基础设施有限时.
- 该方法显示出快速,经济有效的公共卫生监测的前景,尽管需要考虑潜在的混因素.
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