A Space-time Model for Inferring A Susceptibility Map for An Infectious Disease

TL;DR

This paper introduces a space-time model that estimates disease susceptibility maps by integrating transmission dynamics and spatial susceptibility factors, validated on foot-and-mouth disease and measles outbreak data.

Contribution

It develops a Gaussian process-based framework for estimating spatial susceptibility surfaces from outbreak data, incorporating both known and unknown risk factors.

Findings

Successfully identified high-risk regions in Turkey FMD data

Estimated susceptibility correlates with population size in England-Wales measles data

Improved computational efficiency through dimension reduction

Abstract

Motivated by foot-and-mouth disease (FMD) outbreak data from Turkey, we develop a model to estimate disease risk based on a space-time record of outbreaks. The spread of infectious disease in geographical units depends on both transmission between neighbouring units and the intrinsic susceptibility of each unit to an outbreak. Spatially correlated susceptibility may arise from known factors, such as population density, or unknown (or unmeasured) factors such as commuter flows, environmental conditions, or health disparities. Our framework accounts for both space-time transmission and susceptibility. We model the unknown spatially correlated susceptibility as a Gaussian process. We show that the susceptibility surface can be estimated from observed, geo-located time series of infection events and use a projection-based dimension reduction approach which improves computational efficiency. In addition to identifying high risk regions from the Turkey FMD data, we also study how our approach works on the well known England-Wales measles outbreaks data; our latter study results in an estimated susceptibility surface that is strongly correlated with population size, consistent with prior analyses.