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

Author

Listed:
  • Zhengming Xing
  • Bradley Nicholson
  • Monica Jimenez
  • Timothy Veldman
  • Lori Hudson
  • Joseph Lucas
  • David Dunson
  • Aimee K. Zaas
  • Christopher W. Woods
  • Geoffrey S. Ginsburg
  • Lawrence Carin

Abstract

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.

Suggested Citation

  • Zhengming Xing & Bradley Nicholson & Monica Jimenez & Timothy Veldman & Lori Hudson & Joseph Lucas & David Dunson & Aimee K. Zaas & Christopher W. Woods & Geoffrey S. Ginsburg & Lawrence Carin, 2014. "Bayesian modeling of temporal properties of infectious disease in a college student population," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(6), pages 1358-1382, June.
    • Handle: RePEc:taf:japsta:v:41:y:2014:i:6:p:1358-1382
    DOI: 10.1080/02664763.2013.870138
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    References listed on IDEAS

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    1. Carvalho, Carlos M. & Chang, Jeffrey & Lucas, Joseph E. & Nevins, Joseph R. & Wang, Quanli & West, Mike, 2008. "High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1438-1456.
    2. Yang, Yang & Halloran, M. Elizabeth & Daniels, Michael J. & Longini, Ira M. & Burke, Donald S. & Cummings, Derek A. T., 2010. "Modeling Competing Infectious Pathogens From a Bayesian Perspective: Application to Influenza Studies With Incomplete Laboratory Results," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1310-1322.
    3. Vanja Dukic & Hedibert F. Lopes & Nicholas G. Polson, 2012. "Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1410-1426, December.
    4. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
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