Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model

• Published in: Journal of the American Statistical Association Vol. 107; no. 500; pp. 1410 - 1426
• Main Authors: Dukic, Vanja, Lopes, Hedibert F, Polson, Nicholas G
• Format: Journal Article
• Language: English
• Published: Alexandria Taylor & Francis Group 01.12.2012; Taylor & Francis Ltd
• Subjects: algorithms; Applications and Case Studies; Disease models; Disease transmission; Emergency preparedness; Epidemics; Epidemiology; Flu; Google correlate; Google insights; Google searches; Google trends; H1N1; health services; Infectious Diseases; Influenza; Information services; IP surveillance; Mathematical models; monitoring; Nowcasting; Online surveillance; pandemic; Pandemics; Particle filtering; Search engines; Sequential analysis; Sequential learning; Statistical discrepancies; Statistics; Surveillance; Trends; U.S.A; United States; United States > US; USA

In this article, we use Google Flu Trends data together with a sequential surveillance model based on state-space methodology to track the evolution of an epidemic process over time. We embed a classical mathematical epidemiology model [a susceptible-exposed-infected-recovered (SEIR) model] within t...