The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Accurate estimation of influenza epidemics using Google search data via ARGO
Open accessAbstract
Significance: Big data generated from the Internet have great potential in tracking and predicting massive social activities. In this article, we focus on tracking influenza epidemics. We propose a model that utilizes publicly available Google search data to estimate current influenza-like illness activity level. Our model outperforms all available Google-search–based real-time tracking models for influenza epidemics at the national level of the United States, including Google Flu Trends. Our model is flexible, self-correcting, robust, and scalable, making it a potentially powerful tool that can be used for estimation and prediction at multiple temporal and spatial resolutions for other social events.
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Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance
Santillana, M., Nguyen, A. T., Dredze, M., Paul, M. J., Nsoesie, E. O., & Brownstein, J. S. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004513 · Cited by 420
Self-citationMavragani, A., Ochoa, G., & Tsagarakis, K. P. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9366 · Cited by 390
Advances and opportunities in machine learning for process data analytics
Qin, S. J., & Chiang, L. H. (2019) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2019.04.003 · Cited by 351
Mapping the landscape of Artificial Intelligence applications against COVID-19
Bullock, J., Luccioni, A., Hoffman Pham, K., Sin Nga Lam, C., & Luengo-Oroz, M. (2020) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12162 · Cited by 348
Radin, J. M., Wineinger, N. E., Topol, E. J., & Steinhubl, S. R. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30222-5 · Cited by 300
Metadata
- Authors
- Yang, S., Santillana, M., & Kou, S. C.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2015-11-09
- Volume
- 112
- Issue
- 47
- Pages
- 14473-14478
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2130094219
- Cited by
- 373
- References
- 41
- FWCI
- 16.97
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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