title: Advances in nowcasting influenza-like illness rates using search query logs.
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.
authors.affiliation: 1] University College London, Department of Computer Science, London, NW1 2FD, UK [2] Google, Flu Trends Team, London, SW1W 9TQ, UK.
authors.forename: Vasileios
authors.initials: V
authors.lastname: Lampos
authors.affiliation: 1] Google, Flu Trends Team, London, SW1W 9TQ, UK [2] Harvard University, School of Engineering and Applied Sciences, Cambridge, MA 02138, US.
authors.forename: Andrew C
authors.initials: AC
authors.lastname: Miller
authors.affiliation: Google, Flu Trends Team, London, SW1W 9TQ, UK.
authors.forename: Steve
authors.initials: S
authors.lastname: Crossan
authors.affiliation: Google, Flu Trends Team, London, SW1W 9TQ, UK.
authors.forename: Christian
authors.initials: C
authors.lastname: Stefansen
mesh_terms: D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology*; D020407:Internet / Q000706:statistics & numerical data; D008962:Models, Theoretical; D011159:Population Surveillance / Q000379:methods; D012044:Regression Analysis; D012141:Respiratory Tract Infections / Q000453:epidemiology; D057386:Search Engine / Q000379:methods / Q000706:statistics & numerical data*; D012621:Seasons; D014481:United States / Q000453:epidemiology