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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Yang, Shihao Santillana, Mauricio Brownstein, John S. Gray, Josh Richardson, Stewart Kou, S. C. |
| 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. |
| Related Links | https://bmcinfectdis.biomedcentral.com/counter/pdf/10.1186/s12879-017-2424-7.pdf |
| Ending Page | 9 |
| Page Count | 9 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 14712334 |
| DOI | 10.1186/s12879-017-2424-7 |
| Journal | BMC Infectious Diseases |
| Issue Number | 1 |
| Volume Number | 17 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2017-05-08 |
| Access Restriction | Open |
| Subject Keyword | Infectious Diseases Parasitology Medical Microbiology Tropical Medicine Internal Medicine Influenza-like illnesses reports Digital disease detection Dynamic error reduction Validation test Autoregression |
| Content Type | Text |
| Resource Type | Article |
| Subject | Infectious Diseases |
| Journal Impact Factor | 3.4/2023 |
| 5-Year Journal Impact Factor | 3.3/2023 |
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