IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v68y2012i3p755-765.html
   My bibliography  Save this article

A Network-based Analysis of the 1861 Hagelloch Measles Data

Author

Listed:
  • Chris Groendyke
  • David Welch
  • David R. Hunter

Abstract

No abstract is available for this item.

Suggested Citation

  • Chris Groendyke & David Welch & David R. Hunter, 2012. "A Network-based Analysis of the 1861 Hagelloch Measles Data," Biometrics, The International Biometric Society, vol. 68(3), pages 755-765, September.
    • Handle: RePEc:bla:biomet:v:68:y:2012:i:3:p:755-765
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2012.01748.x
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Carol Y. Lin, 2008. "Modeling Infectious Diseases in Humans and Animals by KEELING, M. J. and ROHANI, P," Biometrics, The International Biometric Society, vol. 64(3), pages 993-993, September.
    2. Chris Groendyke & David Welch & David R. Hunter, 2011. "Bayesian Inference for Contact Networks Given Epidemic Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 38(3), pages 600-616, September.
    3. Tom Britton & Theodore Kypraios & Philip D. O'Neill, 2011. "Inference for Epidemics with Three Levels of Mixing: Methodology and Application to a Measles Outbreak," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 38(3), pages 578-599, September.
    4. Stanley Wasserman & Philippa Pattison, 1996. "Logit models and logistic regressions for social networks: I. An introduction to Markov graphs andp," Psychometrika, Springer;The Psychometric Society, vol. 61(3), pages 401-425, September.
    5. Tom Britton & Philip D. O'Neill, 2002. "Bayesian Inference for Stochastic Epidemics in Populations with Random Social Structure," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(3), pages 375-390, September.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Xing Ju Lee & Christopher C. Drovandi & Anthony N. Pettitt, 2015. "Model choice problems using approximate Bayesian computation with applications to pathogen transmission data sets," Biometrics, The International Biometric Society, vol. 71(1), pages 198-207, March.
    2. Cornelius Fritz & Co-Pierre Georg & Angelo Mele & Michael Schweinberger, 2024. "A Strategic Model of Software Dependency Networks," Papers 2402.13375, arXiv.org.
    3. Razvan G. Romanescu & Rob Deardon, 2017. "Fast Inference for Network Models of Infectious Disease Spread," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(3), pages 666-683, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kimberly M. Thompson, 2016. "Evolution and Use of Dynamic Transmission Models for Measles and Rubella Risk and Policy Analysis," Risk Analysis, John Wiley & Sons, vol. 36(7), pages 1383-1403, July.
    2. Razvan G. Romanescu & Rob Deardon, 2017. "Fast Inference for Network Models of Infectious Disease Spread," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(3), pages 666-683, September.
    3. Artalejo, J.R. & Economou, A. & Lopez-Herrero, M.J., 2015. "The stochastic SEIR model before extinction: Computational approaches," Applied Mathematics and Computation, Elsevier, vol. 265(C), pages 1026-1043.
    4. Xiang, Fei & Neal, Peter, 2014. "Efficient MCMC for temporal epidemics via parameter reduction," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 240-250.
    5. Tyagi, Swati & Martha, Subash C. & Abbas, Syed & Debbouche, Amar, 2021. "Mathematical modeling and analysis for controlling the spread of infectious diseases," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    6. Manuel E. Sosa & Steven D. Eppinger & Craig M. Rowles, 2004. "The Misalignment of Product Architecture and Organizational Structure in Complex Product Development," Management Science, INFORMS, vol. 50(12), pages 1674-1689, December.
    7. Cilem Selin Hazir & Corinne Autant-Bernard, 2012. "Using Affiliation Networks to Study the Determinants of Multilateral Research Cooperation Some empirical evidence from EU Framework Programs in biotechnology," Working Papers 1212, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    8. Wei Zhong, 2017. "Simulating influenza pandemic dynamics with public risk communication and individual responsive behavior," Computational and Mathematical Organization Theory, Springer, vol. 23(4), pages 475-495, December.
    9. De Martino, Giuseppe & Spina, Serena, 2015. "Exploiting the time-dynamics of news diffusion on the Internet through a generalized Susceptible–Infected model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 634-644.
    10. Samrachana Adhikari & Beau Dabbs, 2018. "Social Network Analysis in R: A Software Review," Journal of Educational and Behavioral Statistics, , vol. 43(2), pages 225-253, April.
    11. John M Drake & Tobias S Brett & Shiyang Chen & Bogdan I Epureanu & Matthew J Ferrari & Éric Marty & Paige B Miller & Eamon B O’Dea & Suzanne M O’Regan & Andrew W Park & Pejman Rohani, 2019. "The statistics of epidemic transitions," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-14, May.
    12. Xing Ju Lee & Christopher C. Drovandi & Anthony N. Pettitt, 2015. "Model choice problems using approximate Bayesian computation with applications to pathogen transmission data sets," Biometrics, The International Biometric Society, vol. 71(1), pages 198-207, March.
    13. Christel Kamp & Mathieu Moslonka-Lefebvre & Samuel Alizon, 2013. "Epidemic Spread on Weighted Networks," PLOS Computational Biology, Public Library of Science, vol. 9(12), pages 1-10, December.
    14. Guido M. Kuersteiner & Ingmar R. Prucha, 2020. "Dynamic Spatial Panel Models: Networks, Common Shocks, and Sequential Exogeneity," Econometrica, Econometric Society, vol. 88(5), pages 2109-2146, September.
    15. David Levinson & Arthur Huang, 2012. "A Positive Theory of Network Connectivity," Environment and Planning B, , vol. 39(2), pages 308-325, April.
    16. Moritz Kersting & Andreas Bossert & Leif Sörensen & Benjamin Wacker & Jan Chr. Schlüter, 2021. "Predicting effectiveness of countermeasures during the COVID-19 outbreak in South Africa using agent-based simulation," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-15, December.
    17. Lomi, Alessandro & Fonti, Fabio, 2012. "Networks in markets and the propensity of companies to collaborate: An empirical test of three mechanisms," Economics Letters, Elsevier, vol. 114(2), pages 216-220.
    18. Ofosuhene O Apenteng & Noor Azina Ismail, 2014. "The Impact of the Wavelet Propagation Distribution on SEIRS Modeling with Delay," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-9, June.
    19. Miguel Navascués & Costantino Budroni & Yelena Guryanova, 2021. "Disease control as an optimization problem," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-32, September.
    20. Frank Daumann & Florian Follert & Werner Gleißner & Endre Kamarás & Chantal Naumann, 2021. "Political Decision Making in the COVID-19 Pandemic: The Case of Germany from the Perspective of Risk Management," IJERPH, MDPI, vol. 19(1), pages 1-23, December.

    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:biomet:v:68:y:2012:i:3:p:755-765. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.