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An application of hidden Markov models to the French variant Creutzfeldt–Jakob disease epidemic

Author

Listed:
  • Marc Chadeau‐Hyam
  • Paul S. Clarke
  • Chantal Guihenneuc‐Jouyaux
  • Simon N. Cousens
  • Robert G. Will
  • Azra C. Ghani

Abstract

Summary. In 1996, the discovery of variant Creutzfeldt–Jakob disease in the UK raised serious concerns about a large‐scale epidemic. These concerns have been heightened by the recent discovery of people in Britain who were infected through blood transfusion. The outbreak of variant Creutzfeldt–Jakob disease in France emerged more recently with 23 cases observed to date. We use a hidden Markov model to predict the scale of the epidemic in France. As accurate data on the most important epidemiological parameters are scarce, we incorporate estimates from previous studies. Parameter estimation is performed by using Markov chain Monte Carlo methods from which credible intervals for our predictions are obtained. The sensitivity of these predictions to important assumptions regarding population exposure is assessed.

Suggested Citation

  • Marc Chadeau‐Hyam & Paul S. Clarke & Chantal Guihenneuc‐Jouyaux & Simon N. Cousens & Robert G. Will & Azra C. Ghani, 2010. "An application of hidden Markov models to the French variant Creutzfeldt–Jakob disease epidemic," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(5), pages 839-853, November.
    • Handle: RePEc:bla:jorssc:v:59:y:2010:i:5:p:839-853
    DOI: 10.1111/j.1467-9876.2010.00714.x
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    References listed on IDEAS

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    1. Glen A. Satten & Maya R. Sternberg, 1999. "Fitting Semi-Markov Models to Interval-Censored Data with Unknown Initiation Times," Biometrics, The International Biometric Society, vol. 55(2), pages 507-513, June.
    2. Catherine M. Crespi & William G. Cumberland & Sally Blower, 2005. "A Queueing Model for Chronic Recurrent Conditions under Panel Observation," Biometrics, The International Biometric Society, vol. 61(1), pages 193-198, March.
    3. Chantal Guihenneuc-Jouyaux & Sylvia Richardson & Ira M. Longini Jr., 2000. "Modeling Markers of Disease Progression by a Hidden Markov Process: Application to Characterizing CD4 Cell Decline," Biometrics, The International Biometric Society, vol. 56(3), pages 733-741, September.
    4. Pierre Joly & Daniel Commenges, 1999. "A Penalized Likelihood Approach for a Progressive Three-State Model with Censored and Truncated Data: Application to AIDS," Biometrics, The International Biometric Society, vol. 55(3), pages 887-890, September.
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    Cited by:

    1. Chris Sherlock & Tatiana Xifara & Sandra Telfer & Mike Begon, 2013. "A coupled hidden Markov model for disease interactions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(4), pages 609-627, August.

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