Computationally Efficient Parameter Estimation for Spatial Individual-Level Models of Infectious Disease Transmission

dc.contributor.advisorDeeth, Lorna
dc.contributor.advisorHorrocks, Julie
dc.contributor.authorWard, Madeline of Mathematics and Statisticsen_US of Guelphen_US of Scienceen_US and Statisticsen_US
dc.description.abstractIndividual-level models (ILMs) incorporate individual-specific covariate information, such as spatial location, to model infectious disease transmission. However, fitting these models with traditional Bayesian methods becomes cumbersome as model complexity or population size increases. To help mitigate this issue, a method for fitting ILMs to aggregate-level data using traditional Metropolis-Hastings Markov chain Monte Carlo and then disaggregating the results to obtain individual-level estimates for epidemic metrics is proposed. This new method is compared to two algorithms within an alternative model-fitting framework, approximate Bayesian computation (ABC). The ABC estimates were generally more accurate but were accompanied by wide credible intervals and in some cases widely varying computation times. The aggregation method was simpler to carry out and had consistent computation times, however its estimates tended to be somewhat biased. The preferred ILM-fitting method would need to be determined case-by-case, based on the priorities of a particular problem.en_US
dc.publisherUniversity of Guelphen_US
dc.rights.licenseAll items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectinfectious disease modellingen_US
dc.subjectapproximate Bayesian computationen_US
dc.subjectindividual-level modelsen_US
dc.subjectdata aggregationen_US
dc.subjectepidemic modellingen_US
dc.subjectMarkov chain Monte Carloen_US
dc.titleComputationally Efficient Parameter Estimation for Spatial Individual-Level Models of Infectious Disease Transmissionen_US


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