Bayesian modeling of temporal properties of infectious disease in a college student population

Published

Journal Article

A Bayesian statistical model is developed for analysis of the time-evolving properties of infectious disease, with a particular focus on viruses. The model employs a latent semi-Markovian state process, and the state-transition statistics are driven by three terms: (i) a general time-evolving trend of the overall population, (ii) a semi-periodic term that accounts for effects caused by the days of the week, and (iii) a regression term that relates the probability of infection to covariates (here, specifically, to the Google Flu Trends data). Computations are performed using Markov Chain Monte Carlo sampling. Results are presented using a novel data set: daily self-reported symptom scores from hundreds of Duke University undergraduate students, collected over three academic years. The illnesses associated with these students are (imperfectly) labeled using real-time (RT) polymerase chain reaction (PCR) testing for several viruses, and gene-expression data were also analyzed. The statistical analysis is performed on the daily, self-reported symptom scores, and the RT PCR and gene-expression data are employed for analysis and interpretation of the model results. © 2013 The Author(s). Published by Taylor & Francis.

Full Text

Duke Authors

Cited Authors

  • Xing, Z; Nicholson, B; Jimenez, M; Veldman, T; Hudson, L; Lucas, J; Dunson, D; Zaas, AK; Woods, CW; Ginsburg, GS; Carin, L

Published Date

  • January 1, 2014

Published In

Volume / Issue

  • 41 / 6

Start / End Page

  • 1358 - 1382

Electronic International Standard Serial Number (EISSN)

  • 1360-0532

International Standard Serial Number (ISSN)

  • 0266-4763

Digital Object Identifier (DOI)

  • 10.1080/02664763.2013.870138

Citation Source

  • Scopus