Table of Contents
Journal of Allergy
Volume 2014 (2014), Article ID 381983, 9 pages
Research Article

A Model for the Determination of Pollen Count Using Google Search Queries for Patients Suffering from Allergic Rhinitis

Institute of Medical Statistics, Informatics and Epidemiology, University of Cologne, 50924 Cologne, Germany

Received 27 February 2014; Revised 22 May 2014; Accepted 26 May 2014; Published 19 June 2014

Academic Editor: Carlos E. Baena-Cagnani

Copyright © 2014 Volker König and Ralph Mösges. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Background. The transregional increase in pollen-associated allergies and their diversity have been scientifically proven. However, patchy pollen count measurement in many regions is a worldwide problem with few exceptions. Methods. This paper used data gathered from pollen count stations in Germany, Google queries using relevant allergological/biological keywords, and patient data from three German study centres collected in a prospective, double-blind, randomised, placebo-controlled, multicentre immunotherapy study to analyse a possible correlation between these data pools. Results. Overall, correlations between the patient-based, combined symptom medication score and Google data were stronger than those with the regionally measured pollen count data. The correlation of the Google data was especially strong in the groups of severe allergy sufferers. The results of the three-centre analyses show moderate to strong correlations with the Google keywords (up to >0.8 cross-correlation coefficient, ) in 10 out of 11 groups (three averaged patient cohorts and eight subgroups of severe allergy sufferers: high IgE class, high combined symptom medication score, and asthma). Conclusion. For countries with a good Internet infrastructure but no dense network of pollen traps, this could represent an alternative for determining pollen levels and, forecasting the pollen count for the next day.