Conditional logistic individual-level models of spatial infectious disease dynamics

Abstract

Here, we introduce a novel framework for modelling the spatiotemporal dynamics of disease spread known as conditional logistic individual-level models (CL-ILM's). This framework alleviates much of the computational burden associated with traditional spatiotemporal individual-level models for epidemics, and facilitates the use of standard software for fitting logistic models when analysing spatiotemporal disease patterns. The models can be fitted in either a frequentist or Bayesian framework. Here, we apply the new spatial CL-ILM to simulated data, semi-real data from the UK 2001 foot-and-mouth disease epidemic, and real data from a greenhouse experiment on the spread of tomato spotted wilt virus.