RT Journal Article SR Electronic T1 Approximate Bayesian Computation Algorithms for Estimating Network Model Parameters JF bioRxiv FD Cold Spring Harbor Laboratory SP 106450 DO 10.1101/106450 A1 Ye Zheng A1 Stéphane Aris-Brosou YR 2017 UL http://biorxiv.org/content/early/2017/02/07/106450.abstract AB Studies on Approximate Bayesian Computation (ABC) replacing the intractable likelihood function in evaluation of the posterior distribution have been developed for several years. However, their field of application has to date essentially been limited to inference in population genetics. Here, we propose to extend this approach to estimating the structure of transmission networks of viruses in human populations. In particular, we are interested in estimating the transmission parameters under four very general network structures: random, Watts-Strogatz, Barabasi-Albert and an extension that incorporates aging. Estimation was evaluated under three approaches, based on ABC, ABC-Markov chain Monte Carlo (ABC-MCMC) and ABC-Sequential Monte Carlo (ABC-SMC) samplers. We show that ABC-SMC samplers outperform both ABC and ABC-MCMC, achieving high accuracy and low variance in simulations. This approach paves the way to estimating parameters of real transmission networks of transmissible diseases.