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- Title
Bayesian epidemic models for spatially aggregated count data.
- Authors
Malesios, Chrisovalantis; Demiris, Nikolaos; Kalogeropoulos, Konstantinos; Ntzoufras, Ioannis
- Abstract
Epidemic data often possess certain characteristics, such as the presence of many zeros, the spatial nature of the disease spread mechanism, environmental noise, serial correlation and dependence on time-varying factors. This paper addresses these issues via suitable Bayesian modelling. In doing so, we utilize a general class of stochastic regression models appropriate for spatio-temporal count data with an excess number of zeros. The developed regression framework does incorporate serial correlation and time-varying covariates through an Ornstein-Uhlenbeck process formulation. In addition, we explore the effect of different priors, including default options and variations of mixtures of g-priors. The effect of different distance kernels for the epidemic model component is investigated. We proceed by developing branching process-based methods for testing scenarios for disease control, thus linking traditional epidemiological models with stochastic epidemic processes, useful in policy-focused decision making. The approach is illustrated with an application to a sheep pox dataset from the Evros region, Greece. Copyright © 2017 John Wiley & Sons, Ltd.
- Publication
Statistics in Medicine, 2017, Vol 36, Issue 20, p3216
- ISSN
0277-6715
- Publication type
journal article
- DOI
10.1002/sim.7364