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

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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.

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Authors and Affiliations

  1. Division of Statistics, Northern Illinois University, DeKalb, IL, 60115, USA

    Duchwan Ryu & Nader Ebrahimi

  2. Department of Mathematics and Statistics, University of North Florida, Jacksonville, FL, 32224, USA

    Devrim Bilgili

  3. Public Health Departments, School of Medicine, Koç University, Istanbul, Turkey

    Önder Ergönül

  4. Department of Biostatistics, University of Florida, Gainesville, FL, 32611, USA

    Faming Liang

Authors
  1. Duchwan Ryu
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  2. Devrim Bilgili
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  3. Önder Ergönül
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  4. Faming Liang
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  5. Nader Ebrahimi
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Correspondence to Duchwan Ryu.

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Ryu, D., Bilgili, D., Ergönül, Ö. et al. A Bayesian Generalized Linear Model for Crimean–Congo Hemorrhagic Fever Incidents. JABES 23, 153–170 (2018). https://doi.org/10.1007/s13253-017-0310-9

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  • DOI: https://doi.org/10.1007/s13253-017-0310-9

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