Incorporating Contact Network Uncertainty in Individual Level Models of Infectious Disease using Approximate Bayesian Computation.
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.
['Almutiry|Waleed|W|', 'Deardon|Rob|R|']
D000818:Animals D001499:Bayes Theorem D003141:Communicable Diseases D004812:Epidemiologic Methods D005536:Foot-and-Mouth Disease D006801:Humans D008390:Markov Chains D015233:Models, Statistical D009010:Monte Carlo Method D035501:Uncertainty
contact networks approximate Bayesian computation Markov chain Monte Carlo population Monte Carlo epidemic models