在COVID-19大流行期间,利用从互补的社交媒体数据中提取信息来改进医疗灾难决策
Domenic Kellner1, Maximilian Lowin1, Oliver Hinz1
1Goethe University Frankfurt, Theodor-W.-Adorno-Platz 4, D-60629 Frankfurt am Main, Germany.
概括
社交媒体数据可以比官方报告早14天预测COVID-19病例和住院病例. 结合来自Twitter和Reddit的数据,可以提高公共卫生紧急情况的预测准确度.
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:
- 医疗保健灾难的有效管理需要及时准确的信息.
- 由于报告延迟,COVID-19大流行凸显了官方数据来源的局限性.
- 在健康危机期间的决策往往受到低于最佳信息质量的阻碍.
研究的目的:
- 使用在线社交网络数据开发可适应的信息提取解决方案.
- 创建用于预测COVID-19病例数和住院率的预测指数.
- 通过及时的数据,加强在极端健康事件期间的决策支持.
主要方法:
- 利用在线社交网络 (Twitter,Reddit) 的数据来提取信息.
- 开发了一种解决方案,用于创建预测疾病传播的指数.
- 采用异质数据源来利用互补性来改善预测.
主要成果:
- 综合的社交媒体数据来源比单个来源更能提供更好的预测.
- 预测COVID-19病例和住院病例的预测比官方数据提前14天.
- 根据不断变化的信息和数据变化,需要对模型进行调整.
结论:
- 在线社交网络为公共卫生监测提供了有价值,及时的数据来源.
- 整合多种数据流可以提高流行病预测的准确性和交付时间.
- 随着疾病动态和数据的演变,适应性建模对于保持预测准确性至关重要.
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