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

Omar Enzo Santangelo; Sandro  Provenzano; Dimple Grigis; Domiziana Giordano; Francesco Armetta; Alberto Firenze

doi:10.23750/abm.v91i4.8888

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

Authors

Omar Enzo Santangelo Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties "G. D'Alessandro", University of Palermo, Palermo, Italy
Sandro Provenzano Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties "G. D'Alessandro", University of Palermo, Palermo, Italy
Dimple Grigis University of Bergamo, Bergamo, Italy
Domiziana Giordano Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties "G. D'Alessandro", University of Palermo, Palermo, Italy
Francesco Armetta Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties "G. D'Alessandro", University of Palermo, Palermo, Italy
Alberto Firenze Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties "G. D'Alessandro", University of Palermo, Palermo, Italy

DOI: https://doi.org/10.23750/abm.v91i4.8888

Keywords:

Vaccine-preventable diseases, Italy, Germany, France, Romania, Measles Vaccine, Big Data, Internet, Measles, Medical Informatics Computing, Medical Informatics.

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.