通过在线社交网络推断人类的行为可能为预测树虫病毒爆发提供准确的行为估计
Frédéric Jourdain1,2, Debapriyo Chakraborty2, Beatrice Gaillard2
1Santé Publique France, Saint-Maurice, France.
PLOS global public health
|July 24, 2025
概括
使用社交媒体 (X) 的数字流行病学可以有效地追踪疫情期间的人类行为. 尽管存在潜在的偏见,但X数据经过调整后,提供了与传统调查相似的疫情预测.
科学领域:
- 流行病学 流行病学
- 数字流行病学数字流行病学
- 公共卫生 公共卫生
背景情况:
- 人类的行为显著影响传染病的传播,特别是在疫情期间.
- 数字流行病学,分析社交媒体,提供了一种有前途的方法来跟踪行为,但可能面临代表性偏见.
- 与传统方法相比,评估社交媒体数据的可靠性对于准确的流行病学建模至关重要.
研究的目的:
- 系统地比较来自社交媒体的行为估计与大规模民意调查的行为估计.
- 评估社交媒体数据 (X) 对模拟树病毒传播动态的有用性.
- 为了确定是否调整的社交媒体数据可以产生与传统调查数据相比较的疫情预测.
主要方法:
- 从16万条X邮件中比较了与人与蚊子接触相关的情绪状态,与大都会法国15,000人的民意调查数据进行了比较.
- 利用这两种数据源来参数化蚊子传播病毒传播的数学模型.
- 将人口调整和变化纳入传输模式.
主要成果:
- 在X数据和不同年龄组的民意调查之间的情绪状态估计中观察到显著差异.
- 尽管存在这些差异,但将人口调整和变异性整合到模型中,在某些条件下,导致了可比的整体疫情动态预测.
- 这项研究首次证明,数字社交网络数据可以推断出流行病学相关的行为,类似于大规模的民意调查.
结论:
- 社交媒体数据 (X) 可以成为推断人类行为与传染病流行病学相关的有价值工具.
- 调整后的社交媒体数据可以支持疫情预测和实时评估公共卫生行为.
- 这些发现为使用数字流行病学开发有效的疾病控制策略开辟了新的途径.
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