The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance
Open accessAbstract
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.
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Cites
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
Support Vector Regression Machines
Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A. J., & Vapnik, V. N. (1996) · Cited by 4,658
Detecting influenza epidemics using search engine query data
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009) · Nature · doi:10.1038/nature07634 · Cited by 4,295
The Parable of Google Flu: Traps in Big Data Analysis
Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014) · Science · doi:10.1126/science.1248506 · Cited by 2,523
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00117832 · Cited by 1,912
Signorini, A., Segre, A. M., & Polgreen, P. M. (2011) · PLoS ONE · doi:10.1371/journal.pone.0019467 · Cited by 1,235
Using Internet Searches for Influenza Surveillance
Polgreen, P. M., Chen, Y., Pennock, D. M., & Nelson, F. D. (2008) · Clinical Infectious Diseases · doi:10.1086/593098 · Cited by 566
Butler, D. (2013) · Nature · doi:10.1038/494155a · Cited by 514
Cited by
Tracking COVID-19 using online search
Lampos, V., Majumder, M. S., Yom-Tov, E., Edelstein, M., Moura, S., Hamada, Y., Rangaka, M. X., McKendry, R. A., & Cox, I. J. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00384-w · Cited by 164
Srinivas, S., & Ravindran, A. R. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.02.022 · Cited by 147
Johansson, M. A., Reich, N. G., Hota, A., Brownstein, J. S., & Santillana, M. (2016) · Scientific Reports · doi:10.1038/srep33707 · Cited by 120
Self-citationVolkova, S., Ayton, E., Porterfield, K., & Corley, C. D. (2017) · PLOS ONE · doi:10.1371/journal.pone.0188941 · Cited by 100
Lu, F. S., Nguyen, A. T., Link, N. B., Davis, J. T., Chinazzi, M., Xiong, X., Vespignani, A., Lipsitch, M., & Santillana, M. (2020) · doi:10.1101/2020.04.18.20070821 · Cited by 87
“Incorporating estimates from influenza and COVID-19 forecasting and participatory surveillance systems may prove useful in future studies as well [34, 35, 36, 37, 38, 39].”
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Metadata
- Authors
- Santillana, M., Nguyen, A. T., Dredze, M., Paul, M. J., Nsoesie, E. O., & Brownstein, J. S.
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-10-29
- Volume
- 11
- Issue
- 10
- Pages
- e1004513
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- OpenAlex
- W2148330046
- Cited by
- 420
- References
- 41
- FWCI
- 26.15
- Type
- journal article
- Language
- en
- Open access
- gold
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- Santillana, M., Nguyen, A. T., Dredze, M., Paul, M. J., Nsoesie, E. O., & Brownstein, J. S. (2015). Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance. PLOS Computational Biology. https://doi.org/10.1371/journal.pcbi.1004513
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