📝 SummarXiv

Abstract

Individual-level models, also known as ILMs, are commonly used in epidemics modelling, as they can flexibly incorporate individual-level covariates that influence susceptibility and transmissibility upon infection. However, inference for ILMs is computationally intensive, especially as the total population size increases and additional covariates are incorporated. We propose a composite method, the composite ILM (C-ILM), that clusters the population into minimally-interfered subpopulations, with between-cluster infections enabled through a ``spark function.'' This approach allows for parallel computation of subsets before aggregation. Focusing on C-ILM, we consider four ``spark functions'', and introduce a Dirichlet process mixture modelling (DPMM) algorithm for clustering. Simulation results indicate that, in addition to faster computation, C-ILM performs well in parameter estimation and posterior predictions. Furthermore, within C-ILM framework, DPMM algorithm demonstrates superior performance compared to the conventional $K$-means algorithm. We apply the methods to data from the 2001 UK foot-and-mouth disease outbreak. The results provide evidence that C-ILM is not only computationally efficient but also achieves a better model fit compared to the basic spatial ILM.