A Bayesian spatio-temporal Poisson auto-regressive model for the disease infection rate: application to COVID-19 cases in England

Alaimo Di Loro, Pierfrancesco, Böhning, Dankmar and Sahu, Sujit K. (2024) A Bayesian spatio-temporal Poisson auto-regressive model for the disease infection rate: application to COVID-19 cases in England. Journal of the Royal Statistical Society, Series C (Applied Statistics). (doi:10.1093/jrsssc/qlae067).

Record type: Article

Abstract

The COVID-19 pandemic provided new modelling challenges to investigate epidemic processes. This paperextends Poisson Auto-Regression to incorporate spatio-temporal dependence and characterise the local dynamics by borrowing information from adjacent areas. Adopted in a fully Bayesian framework and implemented through a novel sparse-matrix representation in Stan, the model has been validated through a simulation study. We use it to analyse the weekly COVID-19 cases in the English local authority districts and verify some of the epidemicdriving
factors. The model detects substantial spatio-temporal heterogeneity and enables the formalisation of novel model-based investigation methods for assessing additional aspects of disease epidemiology.

Text

STPoiAR_CovidEng_JRSSC (1) - Accepted Manuscript

Restricted to Repository staff only until 26 November 2025.

Available under License University of Southampton Accepted Manuscript Licence.

Request a copy