School of Public Health, Sun Yat-sen University, Guangzhou, China.
School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia.
Sci Rep. 2019 Jul 18;9(1):10434. doi: 10.1038/s41598-019-46898-y.
The use of Internet-based systems for infectious disease surveillance has been increasingly explored in recent years. However, few studies have used Internet search query or social media data to monitor spatial and temporal trends of avian influenza in China. This study investigated the potential of using search query and social media data in detecting and monitoring avian influenza A (H7N9) cases in humans in China. We collected weekly data on laboratory-confirmed H7N9 cases in humans, as well as H7N9-related Baidu Search Index (BSI) and Weibo Posting Index (WPI) data in China from 2013 to 2017, to explore the spatial and temporal trends of H7N9 cases and H7N9-related Internet search queries. Our findings showed a positive relationship of H7N9 cases with BSI and WPI search queries spatially and temporally. The outbreak threshold time and peak time of H7N9-related BSI and WPI searches preceded H7N9 cases in most years. Seasonal autoregressive integrated moving average (SARIMA) models with BSI (β = 0.008, p < 0.001) and WPI (β = 0.002, p = 0.036) were used to predict the number of H7N9 cases. Regression tree model analysis showed that the average H7N9 cases increased by over 2.4-fold (26.8/11) when BSI for H7N9 was > = 11524. Both BSI and WPI data could be used as indicators to develop an early warning system for H7N9 outbreaks in the future.
近年来，人们越来越多地探索使用基于互联网的系统进行传染病监测。然而，很少有研究利用互联网搜索查询或社交媒体数据来监测中国禽流感的时空趋势。本研究探讨了利用搜索查询和社交媒体数据来发现和监测中国人类感染甲型流感（H7N9）病例的潜力。我们收集了 2013 年至 2017 年期间中国每周人类确诊的 H7N9 病例以及与 H7N9 相关的百度搜索索引（BSI）和微博发帖索引（WPI）数据，以探索 H7N9 病例和与 H7N9 相关的互联网搜索查询的时空趋势。我们的研究结果表明，H7N9 病例与 BSI 和 WPI 搜索查询在空间和时间上呈正相关。在大多数年份中，H7N9 相关 BSI 和 WPI 搜索的暴发阈值时间和峰值时间都先于 H7N9 病例。BSI（β=0.008，p<0.001）和 WPI（β=0.002，p=0.036）的季节性自回归综合移动平均（SARIMA）模型被用于预测 H7N9 病例的数量。回归树模型分析表明，当 BSI 用于 H7N9 时，BSI 为≥11524，H7N9 的平均病例数增加了 2.4 倍以上（26.8/11）。BSI 和 WPI 数据都可以用作未来开发 H7N9 暴发预警系统的指标。
