Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance.
We present a machine learning-based methodology capable of providing real-time ("nowcast") and forecast estimates of influenza activity in the US by leveraging data from multiple data sources including: Google searches, Twitter microblogs, nearly real-time hospital visit records, and data...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2015-10-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC4626021?pdf=render |
| _version_ | 1828364226739044352 |
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| author | Mauricio Santillana André T Nguyen Mark Dredze Michael J Paul Elaine O Nsoesie John S Brownstein |
| author_facet | Mauricio Santillana André T Nguyen Mark Dredze Michael J Paul Elaine O Nsoesie John S Brownstein |
| author_sort | Mauricio Santillana |
| collection | DOAJ |
| description | We present a machine learning-based methodology capable of providing real-time ("nowcast") and forecast estimates of influenza activity in the US by leveraging data from multiple data sources including: Google searches, Twitter microblogs, nearly real-time hospital visit records, and data from a participatory surveillance system. Our main contribution consists of combining multiple influenza-like illnesses (ILI) activity estimates, generated independently with each data source, into a single prediction of ILI utilizing machine learning ensemble approaches. Our methodology exploits the information in each data source and produces accurate weekly ILI predictions for up to four weeks ahead of the release of CDC's ILI reports. We evaluate the predictive ability of our ensemble approach during the 2013-2014 (retrospective) and 2014-2015 (live) flu seasons for each of the four weekly time horizons. Our ensemble approach demonstrates several advantages: (1) our ensemble method's predictions outperform every prediction using each data source independently, (2) our methodology can produce predictions one week ahead of GFT's real-time estimates with comparable accuracy, and (3) our two and three week forecast estimates have comparable accuracy to real-time predictions using an autoregressive model. Moreover, our results show that considerable insight is gained from incorporating disparate data streams, in the form of social media and crowd sourced data, into influenza predictions in all time horizons. |
| first_indexed | 2024-04-14T05:13:07Z |
| format | Article |
| id | doaj.art-e2ccd6c5d9934de5b88030b9293e97c5 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-14T05:13:07Z |
| publishDate | 2015-10-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-e2ccd6c5d9934de5b88030b9293e97c52022-12-22T02:10:29ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582015-10-011110e100451310.1371/journal.pcbi.1004513Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance.Mauricio SantillanaAndré T NguyenMark DredzeMichael J PaulElaine O NsoesieJohn S BrownsteinWe present a machine learning-based methodology capable of providing real-time ("nowcast") and forecast estimates of influenza activity in the US by leveraging data from multiple data sources including: Google searches, Twitter microblogs, nearly real-time hospital visit records, and data from a participatory surveillance system. Our main contribution consists of combining multiple influenza-like illnesses (ILI) activity estimates, generated independently with each data source, into a single prediction of ILI utilizing machine learning ensemble approaches. Our methodology exploits the information in each data source and produces accurate weekly ILI predictions for up to four weeks ahead of the release of CDC's ILI reports. We evaluate the predictive ability of our ensemble approach during the 2013-2014 (retrospective) and 2014-2015 (live) flu seasons for each of the four weekly time horizons. Our ensemble approach demonstrates several advantages: (1) our ensemble method's predictions outperform every prediction using each data source independently, (2) our methodology can produce predictions one week ahead of GFT's real-time estimates with comparable accuracy, and (3) our two and three week forecast estimates have comparable accuracy to real-time predictions using an autoregressive model. Moreover, our results show that considerable insight is gained from incorporating disparate data streams, in the form of social media and crowd sourced data, into influenza predictions in all time horizons.http://europepmc.org/articles/PMC4626021?pdf=render |
| spellingShingle | Mauricio Santillana André T Nguyen Mark Dredze Michael J Paul Elaine O Nsoesie John S Brownstein Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance. PLoS Computational Biology |
| title | Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance. |
| title_full | Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance. |
| title_fullStr | Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance. |
| title_full_unstemmed | Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance. |
| title_short | Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance. |
| title_sort | combining search social media and traditional data sources to improve influenza surveillance |
| url | http://europepmc.org/articles/PMC4626021?pdf=render |
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