Using electronic health records and Internet search information for accurate influenza forecasting
View/ Open
Author
Gray, Josh
Richardson, Stewart
Kou, S. C.
Published Version
https://doi.org/10.1186/s12879-017-2424-7Metadata
Show full item recordCitation
Yang, Shihao, Mauricio Santillana, John S. Brownstein, Josh Gray, Stewart Richardson, and S. C. Kou. 2017. “Using electronic health records and Internet search information for accurate influenza forecasting.” BMC Infectious Diseases 17 (1): 332. doi:10.1186/s12879-017-2424-7. http://dx.doi.org/10.1186/s12879-017-2424-7.Abstract
Background: 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. Methods: 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). Results: 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. Conclusions: 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. Electronic supplementary material The online version of this article (doi:10.1186/s12879-017-2424-7) contains supplementary material, which is available to authorized users.Other Sources
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5423019/pdf/Terms of Use
This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAACitable link to this page
http://nrs.harvard.edu/urn-3:HUL.InstRepos:33029933
Collections
- FAS Scholarly Articles [18292]
- HMS Scholarly Articles [17922]
Contact administrator regarding this item (to report mistakes or request changes)