Using electronic health records and Internet search information for accurate influenza forecasting

BMC Infectious Diseases

Volumn 17, Issue 1, 2017, Pages

  Yang, Shihao ^a   Santillana, Mauricio ^b,c   Brownstein, John S ^b,c   Gray, Josh ^d   Richardson, Stewart ^d   Kou, S C ^a  

^a HARVARD UNIVERSITY   (United States)

^b BOSTON CHILDREN S HOSPITAL   (United States)

^c HARVARD MEDICAL SCHOOL   (United States)

^d athenahealth   (United States)

Author keywords

Autoregression; Digital disease detection; Dynamic error reduction; Influenza like illnesses reports; Validation test

Indexed keywords

ACCESS TO INFORMATION; ACCURACY; ARTICLE; DECISION MAKING; DISEASE ACTIVITY; ELECTRONIC HEALTH RECORD; FORECASTING; GEOGRAPHIC DISTRIBUTION; HUMAN; INTERNET; ONLINE SYSTEM; PREDICTION; RELIABILITY; SEASONAL INFLUENZA; TREND STUDY; HEALTH SURVEY; INFLUENZA, HUMAN; PROCEDURES; PUBLIC HEALTH SERVICE; SEASON; UNITED STATES;

CENTERS FOR DISEASE CONTROL AND PREVENTION (U.S.); ELECTRONIC HEALTH RECORDS; FORECASTING; HUMANS; INFLUENZA, HUMAN; INTERNET; POPULATION SURVEILLANCE; SEASONS; UNITED STATES;

EID: 85018433836     PISSN: None     EISSN: 14712334     Source Type: Journal    
DOI: 10.1186/s12879-017-2424-7     Document Type: Article

References (46)

1. WHO. Influenza (Seasonal) [Internet]. Fact Sheet Number 211. 2015 [cited 2016 May 10]. Available from:http://www. who.int/mediacentre/factsheets/fs211/en/index.html.
2. Lipsitch M, Finelli L, Heffernan RT, Leung GM, Redd SC. 2009 H1n1 Surveillance Group. Improving the evidence base for decision making during a pandemic: the example of 2009 influenza A/H1N1. Biosecur Bioterror. 2011;9:89-115.
3. Overview of Influenza Surveillance in the United States | Seasonal Influenza (Flu) | CDC [Internet]. [cited 2016 May 31] Available from: http://www.cdc.gov/flu/weekly/overview.htm
4. Baker L, Wagner TH, Singer S, Bundorf MK. Use of the Internet and e-mail for health care information: results from a national survey. JAMA. 2003;289:2400-6.
5. Brownstein JS, Freifeld CC, Madoff LC. Digital disease detection--harnessing the Web for public health surveillance. N Engl J Med. 2009;360:2153-5. 2157
6. Eysenbach G, Köhler C. Health-related searches on the Internet. JAMA. 2004;291:2946.
7. Eysenbach G. Infodemiology: tracking flu-related searches on the web for syndromic surveillance. AMIA Annu Symp Proc. 2006:244-8.
8. Lampos V, Zou B, Cox IJ. Enhancing Feature Selection Using Word Embeddings: The Case of Flu Surveillance. Proceedings of the 26th International Conference on World Wide Web. In: International World Wide Web Conferences Steering Committee; 2017. p. 695-704.
9. Santillana M, Zhang DW, Althouse BM, Ayers JW. What can digital disease detection learn from (an external revision to) Google Flu Trends? Am J Prev Med. 2014;47:341-7.
10. Yang S, Santillana M, Kou SC. Accurate estimation of influenza epidemics using Google search data via ARGO. Proc Natl Acad Sci U S A. 2015;112:14473-8.
11. Santillana M, Nguyen AT, Dredze M, Paul MJ, Nsoesie EO, Brownstein JS. Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance. PLoS Comput Biol. 2015;11:e1004513.
12. Santillana M, Nguyen AT, Louie T, Zink A, Gray J, Sung I, et al. Cloud-based Electronic Health Records for Real-time. Region-specific Influenza Surveillance Sci Rep. 2016;6
13. Lazarus R, Kleinman KP, Dashevsky I, DeMaria A, Platt R. Using automated medical records for rapid identification of illness syndromes (syndromic surveillance): the example of lower respiratory infection. BMC Public Health. 2001;1:9.
14. Hripcsak G, Soulakis ND, Li L, Morrison FP, Lai AM, Friedman C, et al. Syndromic surveillance using ambulatory electronic health records. J Am Med Inform Assoc. 2009;16:354-61.
15. Viboud C, Charu V, Olson D, Ballesteros S, Gog J, Khan F, et al. Demonstrating the use of high-volume electronic medical claims data to monitor local and regional influenza activity in the US. PLoS One. 2014;9:e102429.
16. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature. 2009;457:1012-4.
17. Nsoesie EO, Brownstein JS, Ramakrishnan N, Marathe MV. A systematic review of studies on forecasting the dynamics of influenza outbreaks. Influenza Other Respir Viruses. 2014;8:309-16.
18. Lampos V, Miller AC, Crossan S, Stefansen C. Advances in nowcasting influenza-like illness rates using search query logs. Sci Rep. 2015;5:12760.
19. Biggerstaff M, Alper D, Dredze M, Fox S, Fung IC-H, Hickmann KS, et al. Results from the centers for disease control and prevention's predict the 2013--2014 Influenza Season Challenge. BMC Infect Dis BioMed Central. 2016;16:357.
20. New Flu Activity Forecasts Available for 2016-17 Season; CDC Names Most Accurate Forecaster for 2015-16 | Spotlights (Flu) | CDC [Internet]. [cited 2017 Jan 5]. Available from: https://www.cdc.gov/flu/spotlights/flu-activity-forecasts-2016-2017.htm.
21. Chretien J-P, George D, Shaman J, Chitale RA, McKenzie FE. Influenza forecasting in human populations: a scoping review. PLoS One. 2014;9:e94130.
22. Pandemic Influenza Preparedness and Response: A WHO Guidance Document. World Health Organization; 2009.
23. Dalton C, Durrheim D, Fejsa J, Francis L, Carlson S, d'Espaignet ET, et al. Flutracking: a weekly Australian community online survey of influenza-like illness in 2006, 2007 and 2008. Commun Dis Intell Q Rep search.informit.com.au. 2009;33:316-22.
24. Shaman J, Karspeck A. Forecasting seasonal outbreaks of influenza. Proc Natl Acad Sci U S A. 2012;109:20425-30.
25. Brooks LC, Farrow DC, Hyun S, Tibshirani RJ, Rosenfeld R. Flexible Modeling of Epidemics with an Empirical Bayes Framework. PLoS Comput Biol. 2015;11:e1004382.
26. Shaman J, Karspeck A, Yang W, Tamerius J, Lipsitch M. Real-time influenza forecasts during the 2012-2013 season. Nat Commun. 2013;4:2837.
27. Yang W, Karspeck A, Shaman J. Comparison of filtering methods for the modeling and retrospective forecasting of influenza epidemics. PLoS Comput Biol. 2014;10:e1003583.
28. Shaman J, Kandula S. Improved Discrimination of Influenza Forecast Accuracy Using Consecutive Predictions. PLoS Curr. [Internet]. 2015;7.doi: 10.1371/currents.outbreaks.8a6a3df285af7ca973fab4b22e10911e.
29. Paul MJ, Dredze M, Broniatowski D. Twitter improves influenza forecasting. PLoS Curr. [Internet]. 2014;6. doi: 10.1371/currents.outbreaks.90b9ed0f59bae4ccaa683a39865d9117.
30. Chakraborty P, Khadivi P, Lewis B, Mahendiran A, Chen J, Butler P, et al. Forecasting a Moving Target: Ensemble Models for ILI Case Count Predictions. Proceedings of the 2014 SIAM International Conference on Data Mining. 2014. p. 262-70.
31. Hickmann KS, Fairchild G, Priedhorsky R, Generous N, Hyman JM, Deshpande A, et al. Forecasting the 2013-2014 Influenza Season Using Wikipedia. PLoS Comput Biol Public Library of Science. 2015;11:e1004239.
32. Tibshirani R. Regression Shrinkage and Selection via the Lasso. J R Stat Soc Series B Stat Methodol [R Stat Soc Wiley]. 1996;58:267-88.
33. Politis DN, Romano JP. The Stationary Bootstrap. J Am Stat Assoc. 1994;89:1303-13.
34. 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:e19467.
35. Smolinski MS, Crawley AW, Kristin B, Rumi C, Olsen JM, Oktawia W, et al. Flu Near You: Crowdsourced Symptom Reporting Spanning 2 Influenza Seasons. Am J Public Health. 2015;105:2124-30.
36. Santillana M, Nsoesie EO, Mekaru SR, Scales D, Brownstein JS. Using clinicians' search query data to monitor influenza epidemics. Clin Infect Dis. 2014;59:1446-50.
37. McIver DJ, Brownstein JS. Wikipedia usage estimates prevalence of influenza-like illness in the United States in near real-time. PLoS Comput Biol. 2014;10:e1003581.
38. Generous N, Fairchild G, Deshpande A, Del Valle SY, Priedhorsky R. Global disease monitoring and forecasting with Wikipedia. PLoS Comput Biol. 2014;10:e1003892.
39. Broeck WVD, Van den Broeck W, Gioannini C, Gonçalves B, Quaggiotto M, Colizza V, et al. The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale. BMC Infect. Dis. [Internet]. 2011;11:37. doi: 10.1186/1471-2334-11-37.
40. Polgreen PM, Yiling C, Pennock DM, Nelson FD. Using Internet Searches for Influenza Surveillance. Clin Infect Dis. 2008;47:1443-8.
41. Yuan Q, Nsoesie EO, Lv B, Peng G, Chunara R, Brownstein JS. Monitoring influenza epidemics in china with search query from baidu. PLoS One. 2013;8:e64323.
42. Butler D. When Google got flu wrong. Nature. 2013;494:155-6.
43. Cook S, Conrad C, Fowlkes AL, Mohebbi MH. Assessing Google flu trends performance in the United States during the 2009 influenza virus A (H1N1) pandemic. PLoS One. 2011;6:e23610.
44. Olson DR, Konty KJ, Paladini M, Viboud C, Simonsen L. Reassessing Google Flu Trends data for detection of seasonal and pandemic influenza: a comparative epidemiological study at three geographic scales. PLoS Comput Biol. 2013;9:e1003256.
45. Lazer D, Kennedy R, King G, Vespignani A. The Parable of Google Flu: Traps in Big Data Analysis. Science. 2014;343:1203-5.
46. Kuehn BM. Scientists mine web search data to identify epidemics and adverse events. JAMA. 2013;309:1883-4.