On the Effect of Ignoring Within-Unit Infectious Disease Dynamics When Modelling Spatial Transmission
Date
2019-09-18
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Abstract
Individual-level models (ILMs) are a class of models that can be used to analyze infectious epidemic data to assist in the understanding of the spatio-temporal dynamics of infectious diseases in discrete time (Deardon et al., 2010). ILMs are generally fitted to epidemic data through Markov chain Monte Carlo (MCMC) methods in a Bayesian statistical framework. Here, we test the effect of ignoring within-unit (e.g., city) infectious disease dynamics when we model spatial transmission. We do this by generating our epidemic data sets from a true model which considers within unit dynamics. It is often hard to get individual-level data in reality. Also, the R package EpiILM used in this thesis for model fitting does not allow for within unit dynamics. For these reasons, we cannot easily fit our generating model to data. We fitted two ILM models (one model with a covariate representing city size, and the other model without covariates), in which within unit dynamics are not explicitly accounted for. We have found from our analysis that the model with the covariate may be a slightly better model to describe the spatio-temporal dynamics of the epidemic. However, although the model with the covariate is better in describing the epidemic process, the dynamics are still not perfectly captured by this model. Our results show the dangers inherent in ignoring within unit dynamics when modelling spatial disease transmission.
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Ferdous, T. (2019). On the Effect of Ignoring Within-Unit Infectious Disease Dynamics When Modelling Spatial Transmission (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.