谷歌趋势和维基百科能否帮助传统监测？麻疹的初步研究。

Can Google Trends and Wikipedia help traditional surveillance? A pilot study on measles.

机构信息

Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties "G. D'Alessandro", University of Palermo, Palermo, Italy.

University of Bergamo, Bergamo, Italy.

出版信息

Acta Biomed. 2020 Nov 12;91(4):e2020190. doi: 10.23750/abm.v91i4.8888.

DOI:10.23750/abm.v91i4.8888

PMID:33525291

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7927545/

Abstract

INTRODUCTION

Cases of measles in some European countries are increasing. The aim of this study is to find the correlation between Google Trends and Wikipedia searches and the real number of cases notified.

MATERIALS AND METHODS

The data on Internet searches have been obtained from Google Trends and Wikipedia. The reported cases of measles were selected from January 2013 until December 2018 for Google Trends and July 2015 until December 2018 from for Wikipedia. We have selected data from four European Countries: Italy, France, Germany and Romania. The data extracted from Wikipedia and Google Trends have been moved over time (Lag), one month in the future and one month in the past. Cross-correlation results are obtained as product-moment correlations between the two time series. The statistical analyses have been performed by using the Spearman's rank correlation coefficient or Pearson correlation coefficient.

RESULTS

A temporal correlation was observed between the bulletin of ECDC and Wikipedia search trends. For Wikipedia the strongest correlation is at a lag of +1 for rougeole (r=0.9006) and masern (r=0.7023) and at lag 0 for morbillo (r=0.8892) and rujeola (r=0.5462); for Google Trends the strongest correlation at a lag 0 for rougeole (rho=0.7398), symptômes rougeole (rho=0.3399), masern (rho=0.6484), sintomi morbillo (rho=0.6029), rujeola (rho=0.7209), simptome rujeola (rho=0.5297) and at lag -1 for masern symptom (rho=0.4536) and morbillo (rho=0.5804).

CONCLUSIONS

Google and Wikipedia could play an important role in surveillance, although these tools need to be combined with traditional surveillance systems.

摘要

简介

一些欧洲国家的麻疹病例正在增加。本研究旨在寻找 Google Trends 和 Wikipedia 搜索与实际报告病例数之间的相关性。

材料和方法

从 Google Trends 和 Wikipedia 获得有关互联网搜索的数据。从 2013 年 1 月至 2018 年 12 月选择 Google Trends 的麻疹报告病例，从 2015 年 7 月至 2018 年 12 月选择 Wikipedia 的麻疹报告病例。我们选择了来自四个欧洲国家的数据：意大利、法国、德国和罗马尼亚。从 Wikipedia 和 Google Trends 提取的数据已随着时间（滞后）移动，未来一个月和过去一个月。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。统计分析是通过使用 Spearman 等级相关系数或 Pearson 相关系数来进行的。

结果

在 ECDC 公报和 Wikipedia 搜索趋势之间观察到时间相关性。对于 Wikipedia，最强的相关性滞后 1 个月为麻疹（r=0.9006）和风疹（r=0.7023），滞后 0 个月为腮腺炎（r=0.8892）和麻疹（r=0.5462）；对于 Google Trends，最强的相关性滞后 0 个月为麻疹（rho=0.7398）、麻疹症状（rho=0.3399）、风疹（rho=0.6484）、腮腺炎症状（rho=0.6029）、风疹（rho=0.7209）、麻疹症状（rho=0.5297）和滞后 1 个月的风疹症状（rho=0.4536）和腮腺炎（rho=0.5804）。