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

ABSTRACT: 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 the state-space framework, thereby extending the SEIR dynamics to allow changes through time. The implementation of this model is based on a particle filtering algorithm, which learns about the epidemic process sequentially through time and provides updated estimated odds of a pandemic with each new surveillance data point. We show how our approach, in combination with sequential Bayes factors, can serve as an online diagnostic tool for influenza pandemic. We take a close look at the Google Flu T

SUBMITTER: Dukic V 

PROVIDER: S-EPMC10426794 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature