Accurate estimation of influenza epidemics using Google search data via ARGO

Proceedings of the National Academy of Sciences of the United States of America

Volumn 112, Issue 47, 2015, Pages 14473-14478

  Yang, Shihao ^a   Santillana, Mauricio ^b,c   Kou, S C ^a  

^a HARVARD UNIVERSITY   (United States)

^b HARVARD UNIVERSITY   (United States)

^c BOSTON CHILDREN S HOSPITAL   (United States)

Author keywords

Autoregressive exogenous model; Big data; Digital disease detection; Influenza like illnesses activity real time estimation; Seasonal influenza

Indexed keywords

ACCESS TO INFORMATION; ACCURACY; CONFERENCE PAPER; DISEASE ACTIVITY; EPIDEMIC; INFLUENZA A (H1N1); MEDICAL INFORMATION; PREDICTION; PRIORITY JOURNAL; RETROSPECTIVE STUDY; SEARCH ENGINE; SEASONAL INFLUENZA; SEASONAL VARIATION; TIME SERIES ANALYSIS; HUMAN; INFLUENZA, HUMAN; INTERNET;

EPIDEMICS; HUMANS; INFLUENZA, HUMAN; INTERNET; RETROSPECTIVE STUDIES; SEARCH ENGINE;

EID: 84947998511     PISSN: 00278424     EISSN: 10916490     Source Type: Journal    
DOI: 10.1073/pnas.1515373112     Document Type: Article

References (44)

1. Ginsberg J, et al. (2009) Detecting influenza epidemics using search engine query data. Nature 457(7232):1012-1014.
2. Polgreen PM, Chen Y, Pennock DM, Nelson FD, Weinstein RA (2008) Using Internet searches for influenza surveillance. Clin Infect Dis 47(11):1443-1448.
3. Yuan Q, et al. (2013) Monitoring influenza epidemics in china with search query from baidu. PLoS One 8(5):e64323.
4. Paul MJ, Dredze M, Broniatowski D (2014) Twitter improves influenza forecasting. PLOS Curr Outbreaks 10.1371/currents.outbreaks.90b9ed0f59bae4ccaa683a39865d9117.
5. McIver DJ, Brownstein JS (2014) Wikipedia usage estimates prevalence of influenzalike illness in the United States in near real-time. PLOS Comput Biol 10(4):e1003581.
6. Santillana M, Nsoesie EO, Mekaru SR, Scales D, Brownstein JS (2014) Using clinicians' search query data to monitor influenza epidemics. Clin Infect Dis 59(10):1446-1450.
7. Wesolowski A, et al. (2014) Commentary: Containing the Ebola outbreak - the potential and challenge of mobile network data. PLOS Curr Outbreaks 10.1371/currents. outbreaks.0177e7fcf52217b8b634376e2f3efc5e.
8. Chan EH, Sahai V, Conrad C, Brownstein JS (2011) Using web search query data to monitor dengue epidemics: a new model for neglected tropical disease surveillance. PLoS Negl Trop Dis 5(5):e1206.
9. Preis T, Moat HS, Stanley HE (2013) Quantifying trading behavior in financial markets using Google trends. Sci Rep 3:1684.
10. Bollen J, Mao H, Zeng X (2011) Twitter mood predicts the stock market. J Comput Sci 2(1):1-8.
11. Wu L, Brynjolfsson E (2015) The future of prediction: How Google searches foreshadow housing prices and sales. Economic Analysis of the Digital Economy, eds Goldfarb A, Greenstein SM, Tucker CE (Univ Chicago Press, Chicago), pp 89-118.
12. Helft M (November 11, 2008) Google uses searches to track flu's spread. NY Times. Available at www.nytimes.com/2008/11/12/technology/internet/12flu.html?-r=0#. Accessed July 11, 2015.
13. Butler D (2013) When Google got flu wrong. Nature 494(7436):155-156.
14. Cook S, Conrad C, Fowlkes AL, Mohebbi MH (2011) Assessing Google Flu Trends performance in the United States during the 2009 influenza virus A (H1N1) pandemic. PLoS One 6(8):e23610.
15. Lazer D, Kennedy R, King G, Vespignani A (2014) Big data. The parable of Google Flu: Traps in big data analysis. Science 343(6176):1203-1205.
16. Santillana M, Zhang DW, Althouse BM, Ayers JW (2014) What can digital disease detection learn from (an external revision to) Google Flu Trends? Am J Prev Med 47(3):341-347.
17. World Health Organization (2014) Influenza (seasonal) (World Health Org, Geneva), Fact Sheet 211.
18. Shaman J, Karspeck A (2012) Forecasting seasonal outbreaks of influenza. Proc Natl Acad Sci USA 109(50):20425-20430.
19. Lipsitch M, Finelli L, Heffernan RT, Leung GM, Redd SC; 2009 H1n1 Surveillance Group (2011) Improving the evidence base for decision making during a pandemic: The example of 2009 influenza A/H1N1. Biosecur Bioterror 9(2):89-115.
20. Nsoesie EO, Brownstein JS, Ramakrishnan N, Marathe MV (2014) A systematic review of studies on forecasting the dynamics of influenza outbreaks. Influenza Other Respi Viruses 8(3):309-316.
21. Chretien JP, George D, Shaman J, Chitale RA, McKenzie FE (2014) Influenza forecasting in human populations: A scoping review. PLoS One 9(4):e94130.
22. Nsoesie E, Mararthe M, Brownstein J (2013) Forecasting peaks of seasonal influenza epidemics. PLoS Curr 5:5.
23. Soebiyanto RP, Adimi F, Kiang RK (2010) Modeling and predicting seasonal influenza transmission in warm regions using climatological parameters. PLoS One 5(3):e9450.
24. Shaman J, Karspeck A, Yang W, Tamerius J, Lipsitch M (2013) Real-time influenza forecasts during the 2012-2013 season. Nat Commun 4(2837):2837.
25. Yang W, Lipsitch M, Shaman J (2015) Inference of seasonal and pandemic influenza transmission dynamics. Proc Natl Acad Sci USA 112(9):2723-2728.
26. Paolotti D, et al. (2014) Web-based participatory surveillance of infectious diseases: The Influenzanet participatory surveillance experience. Clin Microbiol Infect 20(1):17-21.
27. Dalton C, et al. (2009) Flutracking: A weekly australian community online survey of influenza-like illness in 2006, 2007 and 2008. Commun Dis Intell Q Rep 33(3):316-322.
28. Smolinski MS, et al. (2015) Flu near you: Crowdsourced symptom reporting spanning two influenza seasons. Am J Public Health 105(10):2124-2130.
29. Althouse BM, Ng YY, Cummings DA (2011) Prediction of dengue incidence using search query surveillance. PLoS Negl Trop Dis 5(8):e1258.
30. Ocampo AJ, Chunara R, Brownstein JS (2013) Using search queries for malaria surveillance, Thailand. Malar J 12(1):390.
31. Scarpino SV, Dimitrov NB, Meyers LA (2012) Optimizing provider recruitment for influenza surveillance networks. PLOS Comput Biol 8(4):e1002472.
32. Davidson MW, Haim DA, Radin JM (2015) Using networks to combine "big data" and traditional surveillance to improve influenza predictions. Sci Rep 5:8154.
33. Burkom HS, Murphy SP, Shmueli G (2007) Automated time series forecasting for biosurveillance. Stat Med 26(22):4202-4218.
34. Everitt BS, Skrondal A (2002) The Cambridge Dictionary of Statistics (Cambridge Univ Press, Cambridge, UK).
35. Politis DN, Romano JP (1994) The stationary bootstrap. J Am Stat Assoc 89(428): 1303-1313.
36. Tsukayama H (October 13, 2014) Google is testing live-video medical advice. Washington Post. Available at https://www.washingtonpost.com/news/the-switch/wp/2014/ 10/13/google-is-testing-live-video-medical-advice/. Accessed April 20, 2015.
37. Gianatasio D (November 10, 2014) How this agency cleverly stopped people from googling their medical symptoms: The right ads at the right time. Adweek. Available at www.adweek.com/adfreak/how-agency-cleverly-stopped-people-googling-theirmedical-symptoms-161331. Accessed April 20, 2015.
38. Yang AC, Tsai SJ, Huang NE, Peng CK (2011) Association of Internet search trends with suicide death in Taipei City, Taiwan, 2004-2009. J Affect Disord 132(1-2):179-184.
39. Cavazos-Rehg PA, et al. (2015) Monitoring of non-cigarette tobacco use using Google Trends. Tob Control 24(3):249-255.
40. Santillana M, et al. (2015) Combining search, social media, and traditional data sources to improve influenza surveillance. PLoS Comput Biol 11(10):e1004513.
41. Lampos V, Miller AC, Crossan S, Stefansen C (2015) Advances in nowcasting influenzalike illness rates using search query logs. Sci Rep 5:12760.
42. Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc, B 58(1):267-288.
43. Hoerl AE, Kennard RW (1970) Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12(1):55-67.
44. Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc Series B Stat Methodol 67(2):301-320.