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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Almutiry Waleed & Deardon Rob, 2020. "Incorporating Contact Network Uncertainty in Individual Level Models of Infectious Disease using Approximate Bayesian Computation," The International Journal of Biostatistics, De Gruyter, vol. 16(1), pages 1-17, May.
    • Handle: RePEc:bpj:ijbist:v:16:y:2020:i:1:p:17:n:9
    DOI: 10.1515/ijb-2017-0092
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