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Modeling Competing Infectious Pathogens From a Bayesian Perspective: Application to Influenza Studies With Incomplete Laboratory Results

Author

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  • Yang, Yang
  • Halloran, M. Elizabeth
  • Daniels, Michael J.
  • Longini, Ira M.
  • Burke, Donald S.
  • Cummings, Derek A. T.

Abstract

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Suggested Citation

  • 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.
    • Handle: RePEc:bes:jnlasa:v:105:i:492:y:2010:p:1310-1322
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    Cited by:

    1. Yang Yang & Ira M. Longini Jr. & M. Elizabeth Halloran & Valerie Obenchain, 2012. "A Hybrid EM and Monte Carlo EM Algorithm and Its Application to Analysis of Transmission of Infectious Diseases," Biometrics, The International Biometric Society, vol. 68(4), pages 1238-1249, December.
    2. Yang Yang & M. Elizabeth Halloran & Yanjun Chen & Eben Kenah, 2014. "A pathway EM-algorithm for estimating vaccine efficacy with a non-monotone validation set," Biometrics, The International Biometric Society, vol. 70(3), pages 568-578, September.
    3. 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.

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