Individual-level models of disease transmission incorporating piecewise spatial risk functions

TL;DR

This paper introduces flexible non-parametric spatial models for disease transmission at the individual level, using Bayesian MCMC, and demonstrates their effectiveness with simulated and real foot-and-mouth disease data.

Contribution

It develops non-parametric spatial risk functions within a Bayesian framework, allowing more adaptable modeling of disease spread compared to traditional parametric kernels.

Findings

Piecewise constant and linear kernels are effective in modeling transmission.

Models perform well even under misspecification.

Application to UK foot-and-mouth data demonstrates practical utility.

Abstract

Modelling epidemics is crucial for understanding the emergence, transmission, impact and control of diseases. Spatial individual-level models (ILMs) that account for population heterogeneity are a useful tool, accounting for factors such as location, vaccination status and genetic information. Parametric forms for spatial risk functions, or kernels, are often used, but rely on strong assumptions about underlying transmission mechanisms. Here, we propose a class of non-parametric spatial disease transmission model, fitted within a Bayesian Markov chain Monte Carlo (MCMC) framework, allowing for more flexible assumptions when estimating the effect on spatial distance and infection risk. We focus upon two specific forms of non-parametric spatial infection kernel: piecewise constant and piecewise linear. Although these are relatively simple forms, we find them effective. The performance of these models is examined using simulated data, including under circumstances of model misspecification, and then applied to data from the UK 2001 foot-and-mouth disease.