Infectious disease transmission between individuals in a heterogeneous population is often best modelled through a contact network. This contact network can be spatial in nature, with connections between individuals closer in space being more likely. However, contact network data are often unobserved. Here, we consider the fit of an individual level model containing a spatially-based contact network that is either entirely, or partially, unobserved within a Bayesian framework, using data augmented Markov chain Monte Carlo (MCMC). We also incorporate the uncertainty about event history in the disease data. We also examine the performance of the data augmented MCMC analysis in the presence or absence of contact network observational models based upon either knowledge about the degree distribution or the total number of connections in the network. We find that the latter tend to provide better estimates of the model parameters and the underlying contact network.
Funding source: Ontario Ministry of Agriculture, Food and Rural Affairs
Funding source: Canada Foundation for Innovation
Funding source: Qassim University
Funding source: Natural Sciences and Engineering Research Council of Canada
This work was funded by the Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA), the Natural Sciences and Engineering Research Council of Canada (NSERC), Qassim University through the Saudi Arabian Cultural Bureau in Canada, and was carried out on equipment funded by the Canada Foundation for Innovation – Leading Edge Fund project “Centre for Public Health and Zoonoses” at the University of Guelph.
Research funding: None declared.
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: Authors state no conflict of interest.
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Full description and results of the extra analysis of the two larger epidemics, and tables of the posterior means and 95% credible intervals of the model parameters for the known, partial unknown, and complete unknown contact network analyses of both intense and sparse network data sets of population sizes = 25 and 50 individuals.
The online version of this article offers supplementary material (https://doi.org/10.1515/scid-2019-0012).
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