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

Duchwan Ryu (), Devrim Bilgili (), Önder Ergönül (), Faming Liang () and Nader Ebrahimi ()
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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

Journal of Agricultural, Biological and Environmental Statistics, 2018, vol. 23, issue 1, No 9, 153-170

Abstract: 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.

Keywords: Bayesian generalized linear model; Data augmentation; Dynamically weighted importance sampling; Radial basis function networks; Spatiotemporal model (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s13253-017-0310-9

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