From Ecological Connectivity to Outbreak Risk: A Heterogeneous Graph Network for Epidemiological Reasoning under Sparse Spatiotemporal Data

TL;DR

This paper introduces zooNet, a graph-based framework that reconstructs wildlife pathogen outbreak dynamics from sparse, detection-driven data, revealing persistent regional circulation and early transmission signals.

Contribution

The novel zooNet framework integrates mechanistic transmission models, genetic distance imputation, and spatiotemporal graph learning to infer outbreak dynamics from incomplete wildlife surveillance data.

Findings

Recovered coherent outbreak structure despite sparse data

Identified early transmission signals weeks to months before detection

Detected persistent regional circulation in wild bird populations

Abstract

Estimating population-level prevalence and transmission dynamics of wildlife pathogens can be challenging, partly because surveillance data is sparse, detection-driven, and unevenly sequenced. Using highly pathogenic avian influenza A/H5 clade 2.3.4.4b as a case study, we develop zooNet, a graph-based epidemiological framework that integrates mechanistic transmission simulation, metadata-driven genetic distance imputation, and spatiotemporal graph learning to reconstruct outbreak dynamics from incomplete observations. Applied to wild bird surveillance data from the United States during 2022, zooNet recovered coherent spatiotemporal structure despite intermittent detections, revealing sustained regional circulation across multiple migratory flyways. The framework consistently identified counties with ongoing transmission weeks to months before confirmed detections, including persistent activity in northeastern regions prior to documented re-emergence. These signals were detectable even in areas with sparse sequencing and irregular reporting. These results show that explicitly representing ecological processes and inferred genomic connectivity within a unified graph structure allows persistence and spatial risk structure to be inferred from detection-driven wildlife surveillance data.