IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v162y1999i1p121-129.html
   My bibliography  Save this article

Bayesian inference for partially observed stochastic epidemics

Author

Listed:
  • P. D. O’Neill
  • G. O. Roberts

Abstract

The analysis of infectious disease data is usually complicated by the fact that real life epidemics are only partially observed. In particular, data concerning the process of infection are seldom available. Consequently, standard statistical techniques can become too complicated to implement effectively. In this paper Markov chain Monte Carlo methods are used to make inferences about the missing data as well as the unknown parameters of interest in a Bayesian framework. The methods are applied to real life data from disease outbreaks.

Suggested Citation

  • P. D. O’Neill & G. O. Roberts, 1999. "Bayesian inference for partially observed stochastic epidemics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 162(1), pages 121-129.
    • Handle: RePEc:bla:jorssa:v:162:y:1999:i:1:p:121-129
    DOI: 10.1111/1467-985X.00125
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/1467-985X.00125
    Download Restriction: no
    File URL: https://libkey.io/10.1111/1467-985X.00125?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ioannis Andrianakis & Ian R Vernon & Nicky McCreesh & Trevelyan J McKinley & Jeremy E Oakley & Rebecca N Nsubuga & Michael Goldstein & Richard G White, 2015. "Bayesian History Matching of Complex Infectious Disease Models Using Emulation: A Tutorial and a Case Study on HIV in Uganda," PLOS Computational Biology, Public Library of Science, vol. 11(1), pages 1-18, January.
    2. Jonas E. Arias & Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez & Minchul Shin, 2021. "Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs," CESifo Working Paper Series 8977, CESifo.
    3. Jonas E. Arias & Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez & Minchul Shin, 2021. "Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs," Working Papers 21-18, Federal Reserve Bank of Philadelphia.
    4. Annemarie Bouma & Ivo Claassen & Ketut Natih & Don Klinkenberg & Christl A Donnelly & Guus Koch & Michiel van Boven, 2009. "Estimation of Transmission Parameters of H5N1 Avian Influenza Virus in Chickens," PLOS Pathogens, Public Library of Science, vol. 5(1), pages 1-13, January.
    5. Ross, J.V. & Pollett, P.K., 2007. "On costs and decisions in population management," Ecological Modelling, Elsevier, vol. 201(1), pages 60-66.
    6. Xiang, Fei & Neal, Peter, 2014. "Efficient MCMC for temporal epidemics via parameter reduction," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 240-250.
    7. Timothy Kinyanjui & Jo Middleton & Stefan Güttel & Jackie Cassell & Joshua Ross & Thomas House, 2018. "Scabies in residential care homes: Modelling, inference and interventions for well-connected population sub-units," PLOS Computational Biology, Public Library of Science, vol. 14(3), pages 1-24, March.
    8. Kypraios, Theodore, 2009. "A note on maximum likelihood estimation of the initial number of susceptibles in the general stochastic epidemic model," Statistics & Probability Letters, Elsevier, vol. 79(18), pages 1972-1976, September.
    9. McKinley, Trevelyan J. & Ross, Joshua V. & Deardon, Rob & Cook, Alex R., 2014. "Simulation-based Bayesian inference for epidemic models," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 434-447.
    10. Ross, J.V. & Pagendam, D.E. & Pollett, P.K., 2009. "On parameter estimation in population models II: Multi-dimensional processes and transient dynamics," Theoretical Population Biology, Elsevier, vol. 75(2), pages 123-132.
    11. Karen M Ong & Michael S Phillips & Charles S Peskin, 2020. "A mathematical model and inference method for bacterial colonization in hospital units applied to active surveillance data for carbapenem-resistant enterobacteriaceae," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-32, November.
    12. Gyanendra Pokharel & Rob Deardon, 2022. "Emulation‐based inference for spatial infectious disease transmission models incorporating event time uncertainty," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 455-479, March.
    13. Ross, J.V., 2012. "On parameter estimation in population models III: Time-inhomogeneous processes and observation error," Theoretical Population Biology, Elsevier, vol. 82(1), pages 1-17.
    14. James N Walker & Joshua V Ross & Andrew J Black, 2017. "Inference of epidemiological parameters from household stratified data," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-21, October.
    15. Rowland G. Seymour & Theodore Kypraios & Philip D. O’Neill & Thomas J. Hagenaars, 2021. "A Bayesian nonparametric analysis of the 2003 outbreak of highly pathogenic avian influenza in the Netherlands," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1323-1343, November.
    16. Yang, Yang & Longini Jr., Ira M. & Elizabeth Halloran, M., 2007. "A data-augmentation method for infectious disease incidence data from close contact groups," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6582-6595, August.
    17. Nik J Cunniffe & Francisco F Laranjeira & Franco M Neri & R Erik DeSimone & Christopher A Gilligan, 2014. "Cost-Effective Control of Plant Disease When Epidemiological Knowledge Is Incomplete: Modelling Bahia Bark Scaling of Citrus," PLOS Computational Biology, Public Library of Science, vol. 10(8), pages 1-14, August.
    18. Jonas E. Arias & Jesús Fernández-Villaverde & Juan Rubio Ramírez & Minchul Shin, 2021. "The Causal Effects of Lockdown Policies on Health and Macroeconomic Outcomes," NBER Working Papers 28617, National Bureau of Economic Research, Inc.
    19. Golightly, Andrew & Bradley, Emma & Lowe, Tom & Gillespie, Colin S., 2019. "Correlated pseudo-marginal schemes for time-discretised stochastic kinetic models," Computational Statistics & Data Analysis, Elsevier, vol. 136(C), pages 92-107.
    20. David A Rasmussen & Oliver Ratmann & Katia Koelle, 2011. "Inference for Nonlinear Epidemiological Models Using Genealogies and Time Series," PLOS Computational Biology, Public Library of Science, vol. 7(8), pages 1-11, August.
    21. Li, Ning & Qian, Guoqi & Huggins, Richard, 2006. "A latent variable model for estimating disease transmission rate from data on household outbreaks," Computational Statistics & Data Analysis, Elsevier, vol. 50(11), pages 3354-3368, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jorssa:v:162:y:1999:i:1:p:121-129. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.