Incorporating Contact Network Uncertainty in Individual Level Models of Infectious Disease using Approximate Bayesian Computation

Almutiry Waleed () and Deardon Rob ()
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Almutiry Waleed: Department of Mathematics, College of Science and Arts, Qassim University,Ar Rass, Qassim, Saudi Arabia
Deardon Rob: Department of Mathematics and Statistics and Department of Production Animal Health, University of Calgary, Calgary, Alberta, Canada

The International Journal of Biostatistics, 2020, vol. 16, issue 1, 17

Abstract: Infectious disease transmission between individuals in a heterogeneous population is often best modelled through a contact network. However, such contact network data are often unobserved. Such missing data can be accounted for in a Bayesian data augmented framework using Markov chain Monte Carlo (MCMC). Unfortunately, fitting models in such a framework can be highly computationally intensive. We investigate the fitting of network-based infectious disease models with completely unknown contact networks using approximate Bayesian computation population Monte Carlo (ABC-PMC) methods. This is done in the context of both simulated data, and data from the UK 2001 foot-and-mouth disease epidemic. We show that ABC-PMC is able to obtain reasonable approximations of the underlying infectious disease model with huge savings in computation time when compared to a full Bayesian MCMC analysis.

Keywords: contact networks; epidemic models; approximate Bayesian computation; population Monte Carlo; Markov chain Monte Carlo (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1515/ijb-2017-0092

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