Abstract：
The Internet is an important gateway for accessing health-related information, and data generated through web queries have been increasingly used as a complementary source for monitoring and forecasting of infectious diseases and they may partially address the issue of underreporting. In this study, we assessed whether tick-borne encephalitis (TBE)-related Internet search volume may be useful as a complementary tool for TBE surveillance in Italy. Monthly Google Trends (GT) data for TBE-related information were extracted for the period between January 2017 and September 2022, corresponding to the available time series of TBE notifications in Italy. Time series modeling was performed by applying seasonal autoregressive integrated moving average (SARIMA) models with or without GT data. The search terms relative to tick bites reflected best the observed temporal distribution of TBE cases, showing a correlation coefficient of 0.81 (95 % CI: 0.71-0.88). Particularly, both the reported number of TBE cases and GT searches occurred mainly during the summer. The peak of disease notifications coincided with that of Google searches in 4 of 6 years. Once calibrated, SARIMA models with or without GT data were applied to a validation set. Retrospective forecast made by the model with GT data was associated with a lower prediction error and accurately predicted the peak timing. By contrast, the traditional SARIMA model underestimated the actual number of TBE notifications by 65 %. Timeliness, easy availability, low cost and transparency make monitoring of the TBE-related Internet search queries a promising addition to the traditional methods of TBE surveillance in Italy.
摘要：
互联网是获取健康相关信息的重要门户,通过网络查询产生的数据越来越多地被用作监测和预测传染病的补充来源，它们可能部分解决漏报问题。在这项研究中,我们评估了与蜱传脑炎(TBE)相关的互联网搜索量是否可作为意大利TBE监测的补充工具.TBE相关信息的月度Google趋势（GT）数据是在2017年1月至2022年9月期间提取的，对应于意大利可用的TBE通知时间序列。通过应用具有或不具有GT数据的季节性自回归综合移动平均(SARIMA)模型来进行时间序列建模。相对于tick位的搜索词最好地反映了观察到的TBE病例的时间分布，相关系数为0.81(95%CI：0.71-0.88)。特别是,报告的TBE病例数和GT搜索均主要发生在夏季.6年中的4年中，疾病通知的高峰与Google搜索的高峰相吻合。一旦校准,将具有或不具有GT数据的SARIMA模型应用于验证集。通过使用GT数据进行的模型进行的回顾性预测与较低的预测误差相关，并且准确地预测了峰值时间。相比之下,传统的SARIMA模型将TBE通知的实际数量低估了65%。及时性,容易获得,低成本和透明度使与TBE相关的互联网搜索查询的监控成为意大利传统TBE监控方法的一个有希望的补充。
