Healthc Inform Res.  2017 Oct;23(4):343-348. 10.4258/hir.2017.23.4.343.

Methods Using Social Media and Search Queries to Predict Infectious Disease Outbreaks

• Seo DW
• Shin SY

• KMID: 2396428
• DOI: http://doi.org/10.4258/hir.2017.23.4.343

Abstract

OBJECTIVES
For earlier detection of infectious disease outbreaks, a digital syndromic surveillance system based on search queries or social media should be utilized. By using real-time data sources, a digital syndromic surveillance system can overcome the limitation of time-delay in traditional surveillance systems. Here, we introduce an approach to develop such a digital surveillance system.
METHODS
We first explain how the statistics data of infectious diseases, such as influenza and Middle East Respiratory Syndrome (MERS) in Korea, can be collected for reference data. Then we also explain how search engine queries can be retrieved from Google Trends. Finally, we describe the implementation of the prediction model using lagged correlation, which can be calculated by the statistical packages, i.e., SPSS (Statistical Package for the Social Sciences).
RESULTS
Lag correlation analyses demonstrated that search engine data/Twitter have a significant temporal relationship with influenza and MERS data. Therefore, the proposed digital surveillance system can be used to predict infectious disease outbreaks earlier.
CONCLUSIONS
This prediction method could be the core engine for implementing a (near-) real-time digital surveillance system. A digital surveillance system that uses Internet resources has enormous potential to monitor disease outbreaks in the early phase.

Keyword

Digital Syndromic Surveillance System; Disease Outbreak; Social Media; Search Engine

MeSH Terms

Communicable Diseases*
Coronavirus Infections
Disease Outbreaks*
Influenza, Human
Information Storage and Retrieval
Internet
Korea
Methods*
Search Engine
Social Media*