Accurate Estimation of Influenza Epidemics Using Google Search Data Via ARGO

Overview

Journal Proc Natl Acad Sci U S A

Specialty Science

Date 2015 Nov 11

PMID 26553980

Citations 140

Authors

Shihao Yang

Mauricio Santillana

S C Kou

Affiliations

Soon will be listed here.

Abstract

Accurate real-time tracking of influenza outbreaks helps public health officials make timely and meaningful decisions that could save lives. We propose an influenza tracking model, ARGO (AutoRegression with GOogle search data), that uses publicly available online search data. In addition to having a rigorous statistical foundation, ARGO outperforms all previously available Google-search-based tracking models, including the latest version of Google Flu Trends, even though it uses only low-quality search data as input from publicly available Google Trends and Google Correlate websites. ARGO not only incorporates the seasonality in influenza epidemics but also captures changes in people's online search behavior over time. ARGO is also flexible, self-correcting, robust, and scalable, making it a potentially powerful tool that can be used for real-time tracking of other social events at multiple temporal and spatial resolutions.

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References

Santillana M, Nguyen A, Dredze M, Paul M, Nsoesie E, Brownstein J . Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance. PLoS Comput Biol. 2015; 11(10):e1004513. PMC: 4626021. DOI: 10.1371/journal.pcbi.1004513. View

Yang A, Tsai S, Huang N, Peng C . Association of Internet search trends with suicide death in Taipei City, Taiwan, 2004-2009. J Affect Disord. 2011; 132(1-2):179-84. DOI: 10.1016/j.jad.2011.01.019. View

Santillana M, Nsoesie E, Mekaru S, Scales D, Brownstein J . Using clinicians' search query data to monitor influenza epidemics. Clin Infect Dis. 2014; 59(10):1446-50. PMC: 4296132. DOI: 10.1093/cid/ciu647. View

Ocampo A, Chunara R, Brownstein J . Using search queries for malaria surveillance, Thailand. Malar J. 2013; 12:390. PMC: 4228243. DOI: 10.1186/1475-2875-12-390. View

Nsoesie E, Mararthe M, Brownstein J . Forecasting peaks of seasonal influenza epidemics. PLoS Curr. 2013; 5. PMC: 3712489. DOI: 10.1371/currents.outbreaks.bb1e879a23137022ea79a8c508b030bc. View

Shaman J, Karspeck A . Forecasting seasonal outbreaks of influenza. Proc Natl Acad Sci U S A. 2012; 109(50):20425-30. PMC: 3528592. DOI: 10.1073/pnas.1208772109. View

Chretien J, George D, Shaman J, Chitale R, McKenzie F . Influenza forecasting in human populations: a scoping review. PLoS One. 2014; 9(4):e94130. PMC: 3979760. DOI: 10.1371/journal.pone.0094130. View

Lampos V, Miller A, Crossan S, Stefansen C . Advances in nowcasting influenza-like illness rates using search query logs. Sci Rep. 2015; 5:12760. PMC: 4522652. DOI: 10.1038/srep12760. View

Althouse B, Ng Y, Cummings D . Prediction of dengue incidence using search query surveillance. PLoS Negl Trop Dis. 2011; 5(8):e1258. PMC: 3149016. DOI: 10.1371/journal.pntd.0001258. View

10.

Cook S, Conrad C, Fowlkes A, Mohebbi M . Assessing Google flu trends performance in the United States during the 2009 influenza virus A (H1N1) pandemic. PLoS One. 2011; 6(8):e23610. PMC: 3158788. DOI: 10.1371/journal.pone.0023610. View

11.

Santillana M, Zhang D, Althouse B, Ayers J . What can digital disease detection learn from (an external revision to) Google Flu Trends?. Am J Prev Med. 2014; 47(3):341-7. DOI: 10.1016/j.amepre.2014.05.020. View

12.

Ginsberg J, Mohebbi M, Patel R, Brammer L, Smolinski M, Brilliant L . Detecting influenza epidemics using search engine query data. Nature. 2008; 457(7232):1012-4. DOI: 10.1038/nature07634. View

13.

Cavazos-Rehg P, Krauss M, Spitznagel E, Lowery A, Grucza R, Chaloupka F . Monitoring of non-cigarette tobacco use using Google Trends. Tob Control. 2014; 24(3):249-55. PMC: 4122644. DOI: 10.1136/tobaccocontrol-2013-051276. View

14.

Lipsitch M, Finelli L, Heffernan R, Leung G, Redd S . Improving the evidence base for decision making during a pandemic: the example of 2009 influenza A/H1N1. Biosecur Bioterror. 2011; 9(2):89-115. PMC: 3102310. DOI: 10.1089/bsp.2011.0007. View

15.

Nsoesie E, Brownstein J, Ramakrishnan N, Marathe M . A systematic review of studies on forecasting the dynamics of influenza outbreaks. Influenza Other Respir Viruses. 2013; 8(3):309-16. PMC: 4181479. DOI: 10.1111/irv.12226. View

16.

Polgreen P, Chen Y, Pennock D, Nelson F . Using internet searches for influenza surveillance. Clin Infect Dis. 2008; 47(11):1443-8. DOI: 10.1086/593098. View

17.

Smolinski M, Crawley A, Baltrusaitis K, Chunara R, Olsen J, Wojcik O . Flu Near You: Crowdsourced Symptom Reporting Spanning 2 Influenza Seasons. Am J Public Health. 2015; 105(10):2124-30. PMC: 4566540. DOI: 10.2105/AJPH.2015.302696. View

18.

Burkom H, Murphy S, Shmueli G . Automated time series forecasting for biosurveillance. Stat Med. 2007; 26(22):4202-18. DOI: 10.1002/sim.2835. View

19.

Shaman J, Karspeck A, Yang W, Tamerius J, Lipsitch M . Real-time influenza forecasts during the 2012-2013 season. Nat Commun. 2013; 4:2837. PMC: 3873365. DOI: 10.1038/ncomms3837. View

20.

Paolotti D, Carnahan A, Colizza V, Eames K, Edmunds J, Gomes G . Web-based participatory surveillance of infectious diseases: the Influenzanet participatory surveillance experience. Clin Microbiol Infect. 2013; 20(1):17-21. PMC: 7128292. DOI: 10.1111/1469-0691.12477. View