从网络搜索日志测量疫苗接种覆盖率和疫苗持有者的担忧
Serina Chang1, Adam Fourney2, Eric Horvitz3
1Department of Computer Science, Stanford University, Stanford, CA, USA.
Nature communications
|August 1, 2024
概括
搜索引擎数据和机器学习可以比传统方法更快,更细致地跟踪COVID-19疫苗的意图和犹. 这种方法揭示了对疫苗拒绝的洞察力.
科学领域:
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
- 流行病学 流行病学
背景情况:
- 有效的疫苗政策需要及时和详细的关于疫苗接种状况和犹的数据.
- 目前的美国数据来源 (例如,CDC报告,调查) 有局限性,包括延迟,缺乏细节性和自我报告偏见.
- 搜索引擎查询数据为实时了解公共卫生行为提供了潜在的替代方案.
研究的目的:
- 评估匿名搜索引擎数据和机器学习对监测COVID-19疫苗意图和犹的实用性.
- 开发和验证一种机器学习分类器,用于从搜索查询中检测寻找疫苗的行为.
- 分析疫苗犹的模式,并确定与疫苗接种相关的因素.
主要方法:
- 利用了2021年2月至8月的匿名的Bing搜索数据.
- 开发了一个机器学习分类器,以识别寻求COVID-19疫苗的用户.
- 将分类器衍生的疫苗接种率与CDC数据在邮政编码层面进行比较.
- 分析了疫苗早期采用者的搜索行为和内容参与模式.
主要成果:
- 疫苗意图分类器准确预测了寻找疫苗的行为,与CDC数据保持一致,但比它早1-2周.
- 该分类器提供了颗粒式的,ZIP级的疫苗接种率,突出了当地变化.
- 拒绝接种疫苗的人更有可能与不可信的新闻来源接触,并对疫苗要求,发展和神话表示更大的担忧.
- 根据具体的担忧和疫苗开放性,在疫苗持有者中确定了不同的集群.
结论:
- 匿名搜索引擎数据与机器学习相结合,为了解疫苗接种和犹提供了一个强大的实时工具.
- 这种方法可以补充传统的监测方法,为公共卫生政策提供更及时,更细致的见解.
- 了解疫苗犹群体的具体问题对于开发有针对性的干预措施和提高疫苗信心至关重要.
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