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

2026-01-08

AI Summary

Accurately estimating population-level prevalence and transmission dynamics of wildlife pathogens is extremely challenging due to sparse, detection-driven, and unevenly sequenced surveillance data. To address this, this work proposes zooNet, a novel framework that unifies ecological transmission mechanisms and inferred genomic connectivity within a heterogeneous graph structure. By integrating mechanistic transmission simulations, metadata-informed genomic distance imputation, and spatiotemporal graph neural networks, zooNet reconstructs outbreak dynamics from incomplete observations. Applied to U.S. wild bird avian influenza surveillance data from 2022, the method successfully identified sustained transmission chains across multiple migratory flyways and provided early warnings—weeks to months in advance—for outbreak-prone regions not yet confirmed by surveillance, substantially enhancing risk inference under data sparsity.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

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.

Problem

• ecological connectivity
• outbreak risk
• sparse spatiotemporal data
• wildlife surveillance
• epidemiological reasoning

Innovation

• heterogeneous graph network
• epidemiological reasoning
• sparse spatiotemporal data
• genetic distance imputation
• wildlife surveillance