Conditional logistic individual-level models of spatial infectious disease dynamics

TL;DR

This paper introduces conditional logistic individual-level models (CL-ILMs) that efficiently analyze the spatiotemporal spread of infectious diseases, reducing computational complexity and enabling standard software use.

Contribution

The paper presents a novel CL-ILM framework that simplifies modeling of disease dynamics and can be implemented in both frequentist and Bayesian settings.

Findings

Successfully applied to simulated data

Effectively analyzed UK 2001 foot-and-mouth epidemic data

Reduced computational burden compared to traditional models

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 both simulated and semi-real data from the UK 2001 foot-and-mouth disease epidemic.