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Linearized Forms of Individual-Level Models for Large-Scale Spatial Infectious Disease Systems

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Abstract

Individual-level models (ILMs) for infectious diseases, fitted in a Bayesian MCMC framework, are an intuitive and flexible class of models that can take into account population heterogeneity via various individual-level covariates. ILMs containing a geometric distance kernel to account for geographic heterogeneity provide a natural way to model the spatial spread of many diseases. However, in even only moderately large populations, the likelihood calculations required can be prohibitively time consuming. It is possible to speed up the computation via a technique which makes use a linearized distance kernel. Here we examine some methods of carrying out this linearization and compare the performances of these methods.

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Acknowledgements

This work was funded by the Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA)/University of Guelph partnership and the Natural Sciences and Engineering Research Council (NSERC) of Canada Discovery Grants Program. This work was also made possible through the use of computing facilities funded in part by the Canada Foundation for Innovation (CFI) and NSERC. We would also like to thank the reviewers of the paper whose revisions on an earlier draft have helped substantially improve the paper.

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Authors and Affiliations

  1. Department of Population Medicine, Ontario Veterinary College, University of Guelph, Ontario, Canada

    Grace P. S. Kwong

  2. Department of Mathematics & Statistics, University of Guelph, Ontario, Canada

    Rob Deardon

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  1. Grace P. S. Kwong
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  2. Rob Deardon
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Correspondence to Grace P. S. Kwong.

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Kwong, G.P.S., Deardon, R. Linearized Forms of Individual-Level Models for Large-Scale Spatial Infectious Disease Systems. Bull Math Biol 74, 1912–1937 (2012). https://doi.org/10.1007/s11538-012-9739-8

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  • DOI: https://doi.org/10.1007/s11538-012-9739-8

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