IDEAS home Printed from https://ideas.repec.org/a/spr/jagbes/v23y2018i1d10.1007_s13253-017-0310-9.html
   My bibliography  Save this article

A Bayesian Generalized Linear Model for Crimean–Congo Hemorrhagic Fever Incidents

Author

Listed:
  • Duchwan Ryu

    (Northern Illinois University)

  • Devrim Bilgili

    (University of North Florida)

  • Önder Ergönül

    (Koç University)

  • Faming Liang

    (University of Florida)

  • Nader Ebrahimi

    (Northern Illinois University)

Abstract

Global spread of the Crimean–Congo hemorrhagic fever (CCHF) is a fatal viral infection disease found in parts of Africa, Asia, Eastern Europe and Middle East, with a fatality rate of up to 30%. A timely prediction of the prevalence of CCHF incidents is highly desirable, while CCHF incidents often exhibit nonlinearity in both temporal and spatial features. However, the modeling of discrete incidents is not trivial. Moreover, the CCHF incidents are monthly observed in a long period and take a nonlinear pattern over a region at each time point. Hence, the estimation and the data assimilation for incidents require extensive computations. In this paper, using the data augmentation with latent variables, we propose to utilize a dynamically weighted particle filter to take advantage of its population controlling feature in data assimilation. We apply our approach in an analysis of monthly CCHF incidents data collected in Turkey between 2004 and 2012. The results indicate that CCHF incidents are higher at Northern Central Turkey during summer and that some beforehand interventions to stop the propagation are recommendable. Supplementary materials accompanying this paper appear on-line.

Suggested Citation

  • Duchwan Ryu & Devrim Bilgili & Önder Ergönül & Faming Liang & Nader Ebrahimi, 2018. "A Bayesian Generalized Linear Model for Crimean–Congo Hemorrhagic Fever Incidents," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(1), pages 153-170, March.
    • Handle: RePEc:spr:jagbes:v:23:y:2018:i:1:d:10.1007_s13253-017-0310-9
    DOI: 10.1007/s13253-017-0310-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13253-017-0310-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13253-017-0310-9?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
    ---><---

    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. Liang F., 2002. "Dynamically Weighted Importance Sampling in Monte Carlo Computation," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 807-821, September.
    2. Sadanori Konishi, 2004. "Bayesian information criteria and smoothing parameter selection in radial basis function networks," Biometrika, Biometrika Trust, vol. 91(1), pages 27-43, March.
    3. Bochao Jia & Suwa Xu & Guanghua Xiao & Vishal Lamba & Faming Liang, 2017. "Learning gene regulatory networks from next generation sequencing data," Biometrics, The International Biometric Society, vol. 73(4), pages 1221-1230, December.
    4. Holmes C.C. & Mallick B.K., 2003. "Generalized Nonlinear Modeling With Multivariate Free-Knot Regression Splines," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 352-368, January.
    5. Duchwan Ryu & Faming Liang & Bani K. Mallick, 2013. "Sea Surface Temperature Modeling using Radial Basis Function Networks With a Dynamically Weighted Particle Filter," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 111-123, March.
    Full references (including those not matched with items on IDEAS)

    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. Shuichi Kawano, 2014. "Selection of tuning parameters in bridge regression models via Bayesian information criterion," Statistical Papers, Springer, vol. 55(4), pages 1207-1223, November.
    2. Hidetoshi Matsui & Takamitsu Araki & Sadanori Konishi, 2011. "Multiclass Functional Discriminant Analysis and Its Application to Gesture Recognition," Journal of Classification, Springer;The Classification Society, vol. 28(2), pages 227-243, July.
    3. Ando, Tomohiro, 2009. "Bayesian inference for the hazard term structure with functional predictors using Bayesian predictive information criteria," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 1925-1939, April.
    4. Fujii, Toru & Konishi, Sadanori, 2006. "Nonlinear regression modeling via regularized wavelets and smoothing parameter selection," Journal of Multivariate Analysis, Elsevier, vol. 97(9), pages 2023-2033, October.
    5. Ando, Tomohiro, 2009. "Bayesian factor analysis with fat-tailed factors and its exact marginal likelihood," Journal of Multivariate Analysis, Elsevier, vol. 100(8), pages 1717-1726, September.
    6. Wai-Yin Poon & Hai-Bin Wang, 2014. "Multivariate partially linear single-index models: Bayesian analysis," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(4), pages 755-768, December.
    7. Pena, Daniel & Redondas, Dolores, 2006. "Bayesian curve estimation by model averaging," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 688-709, February.
    8. Ando, Tomohiro & Tsay, Ruey, 2010. "Predictive likelihood for Bayesian model selection and averaging," International Journal of Forecasting, Elsevier, vol. 26(4), pages 744-763, October.
    9. Drew Creal, 2012. "A Survey of Sequential Monte Carlo Methods for Economics and Finance," Econometric Reviews, Taylor & Francis Journals, vol. 31(3), pages 245-296.
    10. DongHyuk Lee & Raymond J. Carroll & Samiran Sinha, 2017. "Frequentist standard errors of Bayes estimators," Computational Statistics, Springer, vol. 32(3), pages 867-888, September.
    11. M. Rincón & M. Ruiz-Medina, 2012. "Wavelet-RKHS-based functional statistical classification," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(3), pages 201-217, October.
    12. Suvo Chatterjee & Shrabanti Chowdhury & Duchwan Ryu & Sanjib Basu, 2023. "Bayesian functional data analysis over dependent regions and its application for identification of differentially methylated regions," Biometrics, The International Biometric Society, vol. 79(4), pages 3294-3306, December.
    13. Matsui, Hidetoshi, 2020. "Quadratic regression for functional response models," Econometrics and Statistics, Elsevier, vol. 13(C), pages 125-136.
    14. Rong Zhang & Zhao Ren & Wei Chen, 2018. "SILGGM: An extensive R package for efficient statistical inference in large-scale gene networks," PLOS Computational Biology, Public Library of Science, vol. 14(8), pages 1-14, August.
    15. Matsui, Hidetoshi & Konishi, Sadanori, 2011. "Variable selection for functional regression models via the L1 regularization," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3304-3310, December.
    16. Matsui, Hidetoshi, 2014. "Variable and boundary selection for functional data via multiclass logistic regression modeling," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 176-185.
    17. Masao Ueki & Kaoru Fueda, 2010. "Optimal tuning parameter estimation in maximum penalized likelihood method," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(3), pages 413-438, June.
    18. Mary Meyer & Amber Hackstadt & Jennifer Hoeting, 2011. "Bayesian estimation and inference for generalised partial linear models using shape-restricted splines," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(4), pages 867-884.
    19. Tomohiro Ando & Sadanori Konishi, 2009. "Nonlinear logistic discrimination via regularized radial basis functions for classifying high-dimensional data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(2), pages 331-353, June.
    20. Liang, Faming & Zhang, Jian, 2009. "Learning Bayesian networks for discrete data," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 865-876, February.

    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:spr:jagbes:v:23:y:2018:i:1:d:10.1007_s13253-017-0310-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.