The Internet and the Anti-Vaccine Movement: Tracking the 2017 EU Measles Outbreak

Citation

Mavragani A & Ochoa G (2018) The Internet and the Anti-Vaccine Movement: Tracking the 2017 EU Measles Outbreak. Big Data and Cognitive Computing, 2 (1), Art. No.: 2. https://doi.org/10.3390/bdcc2010002

Abstract
In the Internet Era of information overload, how does the individual filter and process available knowledge? In addressing this question, this paper examines the behavioral changes in the online interest in terms related to Measles and the Anti-Vaccine Movement from 2004 to 2017, in order to identify any relationships between the decrease in immunization percentages, the Anti-Vaccine Movement, and the increased reported Measles cases. The results show that statistically significant positive correlations exist between monthly Measles cases and Google queries in the respective translated terms in most EU28 countries from January 2011 to August 2017. Furthermore, a strong negative correlation (p< 0.01) exists between the online interest in the term ‘Anti Vaccine’ and the Worldwide immunization percentages from 2004 to 2016. The latter could be supportive of previous work suggesting that conspiracist ideation is related to the rejection of scientific propositions. As Measles require the highest immunization percentage out of the vaccine preventable diseases, the 2017 EU outbreak could be the first of several other diseases’ outbreaks or epidemics in the near future should the immunization percentages continue to decrease. Big Data Analytics in general and the analysis of Google queries in specific have been shown to be valuable in addressing health related topics up to this point. Therefore, analyzing the variations and patterns of available online information could assist health officials with the assessment of reported cases, as well as taking the required preventive actions.

Keywords
anti-vaccine; anti-vaccine movement; Google Trends; Internet; measles; MMR; online behavior; vaccination

Journal
Big Data and Cognitive Computing: Volume 2, Issue 1

• BDCC-02-00002.pdf