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*

Figure

• 

  Figure 1 ILI report example from week 28, 2017 (July 9, 2017–July 15, 2017). The ratio is the number of outpatients divided by 1,000.

• 

  Figure 2 Trends of influenza search queries, according to Google Trends, between September 9, 2007 and September 8, 2012.

Reference

1. Peiris JS, Guan Y, Yuen KY. Severe acute respiratory syndrome. Nat Med. 2004; 10:12 Suppl. S88–S97.

2. Novel Swine-Origin Influenza A (H1N1) Virus Investigation Team. Dawood FS, Jain S, Finelli L, Shaw MW, Lindstrom S, et al. Emergence of a novel swine-origin influenza A (H1N1) virus in humans. N Engl J Med. 2009; 360(25):2605–2615.

3. Shin SY, Seo DW, An J, Kwak H, Kim SH, Gwack J, et al. High correlation of Middle East respiratory syndrome spread with Google search and Twitter trends in Korea. Sci Rep. 2016; 6:32920.

4. Henning KJ. What is syndromic surveillance? MMWR Suppl. 2004; 53:5–11.

5. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature. 2009; 457(7232):1012–1014.

6. Triple S Project. Assessment of syndromic surveillance in Europe. Lancet. 2011; 378(9806):1833–1834.

7. Eysenbach G. Infodemiology: tracking flu-related searches on the web for syndromic surveillance. AMIA Annu Symp Proc. 2006; 2006:244–248.

8. Cho S, Sohn CH, Jo MW, Shin SY, Lee JH, Ryoo SM, et al. Correlation between national influenza surveillance data and google trends in South Korea. PLoS One. 2013; 8(12):e81422.

9. Seo DW, Jo MW, Sohn CH, Shin SY, Lee J, Yu M, et al. Cumulative query method for influenza surveillance using search engine data. J Med Internet Res. 2014; 16(12):e289.

10. Hulth A, Rydevik G, Linde A. Web queries as a source for syndromic surveillance. PLoS One. 2009; 4(2):e4378.

11. National health alert system [Internet]. Cheongju: National Health Insurance Service;c2017. cited at 2017 Jul 8. Available from: http://forecast.nhis.or.kr/menu.do.

12. Korea Centers for Disease Control & Prevention. KCDC ILI reports [Internet]. Cheongju: Korea Centers for Disease Control & Prevention;c2017. cited 8 Jul 2017. Available from: http://www.cdc.go.kr/CDC/info/CdcKrInfo0502.jsp?menuIds=HOME001-MNU1175-MNU0048-MNU0050.

13. Wikipedia. 2015 Middle East respiratory syndrome outbreak in South Korea [Internet]. [place unknown]: Wikipedia;c2017. cited at 2017 Jul 8. Available from: https://en.wikipedia.org/wiki/2015_Middle_East_respiratory_syndrome_outbreak_in_South_Korea.

14. Google Trends [Internet]. Mountain View (CA): Google;c2017. cited at 2017 Jul 8. Available from: https://trends.google.com/trends.

15. Naver DataLab [Internet]. Seoul: Naver;c2017. cited at 2017 Jul 8. Available from: http://datalab.naver.com/.

16. Shin SY, Kim T, Seo DW, Sohn CH, Kim SH, Ryoo SM, et al. Correlation between National Influenza Surveillance Data and Search Queries from Mobile Devices and Desktops in South Korea. PLoS One. 2016; 11(7):e0158539.

17. Signorini A, Segre AM, Polgreen PM. The use of Twitter to track levels of disease activity and public concern in the U.S. during the influenza A H1N1 pandemic. PLoS One. 2011; 6(5):e19467.

18. GNIP 2.0 [Internet]. San Francisco (CA): Twitter Inc.;c2017. cited at 2017 Jul 8. Available from: https://gnip.com.

19. Talkwalker [Internet]. New York (NY): Talkwalker Inc.;c2017. cited at 2017 Jul 8. Available from: https://www.talkwalker.com.

20. Lazer D, Kennedy R, King G, Vespignani A. The parable of Google Flu: traps in big data analysis. Science. 2014; 343(6176):1203–1205.