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Acta Biomed. 2021 Feb 8;92(1):e2021056. doi: 10.23750/abm.v92i1.9790.
This study aimed to assess if the frequency of the Italian general public searches for influenza, using the Wikipedia web-page, are aligned with Istituto Superiore di Sanità (ISS) influenza cases.
The reported cases of flu were selected from October 2015 to May 2019. Wikipedia Trends was used to assess how many times a specific page was read by users; data were extracted as daily data and aggregated on a weekly basis. The following data were extracted: number of weekly views by users from the October 2015 to May 2019 of the pages: Influenza, Febbre and Tosse (Flu, Fever and Cough, in English). Cross-correlation results are obtained as product-moment correlations between the two times series.
Regarding the database with weekly data, temporal correlation was observed between the bulletin of ISS and Wikipedia search trends. The strongest correlation was at a lag of 0 for number of cases and Flu (r=0.7571), Fever and Cough (r=0.7501). The strongest correlation was at a lag of -1 for Fever and Cough (r=0.7501). The strongest correlation was at a lag of 1 for number of cases and Flu (r=0.7559), Fever and Cough (r=0.7501).
A possible future application for programming and management interventions of Public Health is proposed.
本研究旨在评估意大利普通民众使用维基百科网页搜索流感的频率是否与意大利高等卫生研究院 (ISS) 的流感病例一致。
从 2015 年 10 月至 2019 年 5 月，选择报道的流感病例。使用 Wikipedia Trends 评估有多少用户阅读了特定页面；数据以每日数据提取，并按周汇总。提取以下数据：2015 年 10 月至 2019 年 5 月期间，用户每周对以下页面的浏览次数：流感、发烧和咳嗽（英文为“Flu, Fever and Cough”）。交叉相关结果是通过两个时间序列之间的乘积矩相关获得的。
关于每周数据数据库，观察到 ISS 公告和 Wikipedia 搜索趋势之间存在时间相关性。最强的相关性出现在病例数和流感（r=0.7571）、发烧和咳嗽（r=0.7501）的滞后 0 处。发烧和咳嗽（r=0.7501）的最强相关性出现在滞后 1 处。病例数和流感（r=0.7559）、发烧和咳嗽（r=0.7501）的最强相关性出现在滞后 1 处。
提出了一个可能的未来公共卫生规划和管理干预的应用程序。
