Using electronic health records and Internet search information for accurate influenza forecasting
Tóm tắt
Accurate influenza activity forecasting helps public health officials prepare and allocate resources for unusual influenza activity. Traditional flu surveillance systems, such as the Centers for Disease Control and Prevention’s (CDC) influenza-like illnesses reports, lag behind real-time by one to 2 weeks, whereas information contained in cloud-based electronic health records (EHR) and in Internet users’ search activity is typically available in near real-time. We present a method that combines the information from these two data sources with historical flu activity to produce national flu forecasts for the United States up to 4 weeks ahead of the publication of CDC’s flu reports. We extend a method originally designed to track flu using Google searches, named ARGO, to combine information from EHR and Internet searches with historical flu activities. Our regularized multivariate regression model dynamically selects the most appropriate variables for flu prediction every week. The model is assessed for the flu seasons within the time period 2013–2016 using multiple metrics including root mean squared error (RMSE). Our method reduces the RMSE of the publicly available alternative (Healthmap flutrends) method by 33, 20, 17 and 21%, for the four time horizons: real-time, one, two, and 3 weeks ahead, respectively. Such accuracy improvements are statistically significant at the 5% level. Our real-time estimates correctly identified the peak timing and magnitude of the studied flu seasons. Our method significantly reduces the prediction error when compared to historical publicly available Internet-based prediction systems, demonstrating that: (1) the method to combine data sources is as important as data quality; (2) effectively extracting information from a cloud-based EHR and Internet search activity leads to accurate forecast of flu.
Tài liệu tham khảo
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.
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.
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
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.
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
Eysenbach G, Köhler C. Health-related searches on the Internet. JAMA. 2004;291:2946.
Eysenbach G. Infodemiology: tracking flu-related searches on the web for syndromic surveillance. AMIA Annu Symp Proc. 2006:244–8.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
Lampos V, Miller AC, Crossan S, Stefansen C. Advances in nowcasting influenza-like illness rates using search query logs. Sci Rep. 2015;5:12760.
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.
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.
Chretien J-P, George D, Shaman J, Chitale RA, McKenzie FE. Influenza forecasting in human populations: a scoping review. PLoS One. 2014;9:e94130.
Pandemic Influenza Preparedness and Response: A WHO Guidance Document. World Health Organization; 2009.
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.
Shaman J, Karspeck A. Forecasting seasonal outbreaks of influenza. Proc Natl Acad Sci U S A. 2012;109:20425–30.
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.
Shaman J, Karspeck A, Yang W, Tamerius J, Lipsitch M. Real-time influenza forecasts during the 2012-2013 season. Nat Commun. 2013;4:2837.
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.
Shaman J, Kandula S. Improved Discrimination of Influenza Forecast Accuracy Using Consecutive Predictions. PLoS Curr. [Internet]. 2015;7. doi:10.1371/currents.outbreaks.8a6a3df285af7ca973fab4b22e10911e.
Paul MJ, Dredze M, Broniatowski D. Twitter improves influenza forecasting. PLoS Curr. [Internet]. 2014;6. doi:10.1371/currents.outbreaks.90b9ed0f59bae4ccaa683a39865d9117.
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.
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.
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.
Politis DN, Romano JP. The Stationary Bootstrap. J Am Stat Assoc. 1994;89:1303–13.
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.
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.
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.
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.
Generous N, Fairchild G, Deshpande A, Del Valle SY, Priedhorsky R. Global disease monitoring and forecasting with Wikipedia. PLoS Comput Biol. 2014;10:e1003892.
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.
Polgreen PM, Yiling C, Pennock DM, Nelson FD. Using Internet Searches for Influenza Surveillance. Clin Infect Dis. 2008;47:1443–8.
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.
Butler D. When Google got flu wrong. Nature. 2013;494:155–6.
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.
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.
Lazer D, Kennedy R, King G, Vespignani A. The Parable of Google Flu: Traps in Big Data Analysis. Science. 2014;343:1203–5.
Kuehn BM. Scientists mine web search data to identify epidemics and adverse events. JAMA. 2013;309:1883–4.