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Cited by 143Data-Driven Disease SurveillanceInfluenza Virus Research StudiesInformation Retrieval and Search Behavior

Advances in nowcasting influenza-like illness rates using search query logs

Open access
Lampos, Vasileios, Miller, Andrew C., Crossan, Steve, Stefansen, Christian·2015·Scientific Reports
DOI 10.1038/srep12760PMC PMC4522652
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Abstract

User-generated content can assist epidemiological surveillance in the early detection and prevalence estimation of infectious diseases, such as influenza. Google Flu Trends embodies the first public platform for transforming search queries to indications about the current state of flu in various places all over the world. However, the original model significantly mispredicted influenza-like illness rates in the US during the 2012–13 flu season. In this work, we build on the previous modeling attempt, proposing substantial improvements. Firstly, we investigate the performance of a widely used linear regularized regression solver, known as the Elastic Net. Then, we expand on this model by incorporating the queries selected by the Elastic Net into a nonlinear regression framework, based on a composite Gaussian Process. Finally, we augment the query-only predictions with an autoregressive model, injecting prior knowledge about the disease. We assess predictive performance using five consecutive flu seasons spanning from 2008 to 2013 and qualitatively explain certain shortcomings of the previous approach. Our results indicate that a nonlinear query modeling approach delivers the lowest cumulative nowcasting error and also suggest that query information significantly improves autoregressive inferences, obtaining state-of-the-art performance.

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  • Models for COVID-19, building on previously established internet search algorithms for influenza, are included in Public Health England's weekly reports.

    “Models for COVID-19 (ref. 33 ), building on previously established internet search algorithms for influenza 34 , are included in Public Health England's weekly reports 35 .”

    From Digital technologies in the public-health response to COVID-19 (2020) · cites this paper as [34]

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Cited by

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Authors
Lampos, V., Miller, A. C., Crossan, S., & Stefansen, C.
Affiliations
  • Google (United Kingdom) (GB)
  • Harvard University (US)
Corresponding author
Lampos, Vasileios
ORCID
Lampos, Vasileios
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2015-08-03
Volume
5
Issue
1
Pages
12760-12760
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/srep12760
PMC
PMC4522652
OpenAlex
W1071368427
Cited by
143
References
6
Citations by year
2026: 3 · 2025: 6 · 2024: 1 · 2023: 12 · 2022: 15 · 2021: 20 · 2020: 17 · 2019: 19 · 2018: 17 · 2017: 18 · 2016: 13 · 2015: 2
FWCI
13.08
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
  • UCL Discovery (University College London) (submittedVersion)
Funders
Engineering and Physical Sciences Research Council (EP/K031953/1)
Topics
  • Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
  • Health Sciences › Medicine › Epidemiology › Influenza Virus Research Studies
  • Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
Keywords
influenza-like illness, nowcasting, Google Flu Trends, search query logs, user-generated content, disease surveillance, elastic net, Gaussian process regression, autoregressive models, influenza surveillance
MeSH terms
Humans, Influenza, Human, Models, Theoretical, Population Surveillance, Regression Analysis, Respiratory Tract Infections, Seasons, United States, Internet, Search Engine

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