通过网络分析增强谷歌趋势数据的预测能力:COVID-19的传染病学研究
Amanda My Chu1, Andy C Y Chong2, Nick H T Lai3
1Department of Social Sciences and Policy Studies, The Education University of Hong Kong, Hong Kong, Hong Kong.
JMIR public health and surveillance
|September 7, 2023
概括
这项研究引入了一个合并的算法,以增强谷歌趋势数据分辨率,用于准确的每日流行病监测. 改进的数据与网络分析相结合,有效预测COVID-19趋势和风险.
科学领域:
- 公共卫生监督 公共卫生监督
- 计算流行病学计算流行病学
- 数据科学数据科学数据科学
背景情况:
- 随着COVID-19的爆发,人们越来越需要实时的公共卫生监测.
- 谷歌趋势 (GT) 提供可访问的搜索量数据,对预测健康问题有价值.
- 在GT数据解析和检索方面的局限性限制了其对疾病爆发的预测能力.
研究的目的:
- 开发一个合并的算法,在长时间内恢复GT搜索量数据的分辨率和准确性.
- 为了证明应用的合并搜索卷 (MSVs) 与网络分析跟踪COVID-19流行风险.
主要方法:
- 从谷歌趋势 (GT) 收集了相对搜索量.
- 使用新的算法将GT数据转化为合并的搜索量 (MSV).
- 通过计算MSV之间的相关性和分析网络统计数据来构建动态网络.
主要成果:
- 拟议的算法成功地从GT的每周数据点中恢复了每日搜索量数据,用于长期分析.
- 动态时间扭曲图显示,动态网络准确预测了COVID-19大流行趋势.
- 这些网络有效地预测了确诊病例的数量和严重程度风险得分.
结论:
- 处理GT数据的创新方法增强了其用于大流行风险预测的实用性.
- 应用MSV和网络分析扩大了GT数据在公共卫生中的潜力.
- 未来的研究可以探索GT动态网络的非传染性疾病,健康行为和错误信息.
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