A High-Resolution, US-scale Digital Similar of Interacting Livestock, Wild Birds, and Human Ecosystems with Applications to Multi-host Epidemic Spread

2024-11-03

arXiv.org

AI Summary

This study addresses the challenge of assessing cross-species spillover risk of highly pathogenic avian influenza (HPAI) H5N1 in the U.S. multispecies ecosystem. We develop the first “One Health”–oriented, high-resolution spatiotemporal digital twin framework. The framework integrates farm-level livestock (cattle, poultry, swine, sheep) subtype distributions, weekly wild bird (key H5N1 reservoir) abundance, and gridded human population data—including agricultural workers—through statistical fusion, spatiotemporal interpolation, and multi-source calibration to model cross-host transmission dynamics. We propose a novel multispecies collaborative grid representation method, generating subtype-specific, spatiotemporally explicit H5N1 spillover risk maps that accurately identify high-risk hotspots—particularly dairy and poultry farms. Validation against historical outbreaks demonstrates significantly improved concordance, establishing a new paradigm for early HPAI warning and targeted intervention.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Security and Privacy: Data transparency and provenance
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

One Health issues, such as the spread of highly pathogenic avian influenza~(HPAI), present significant challenges at the human-animal-environmental interface. Recent H5N1 outbreaks underscore the need for comprehensive modeling efforts that capture the complex interactions between various entities in these interconnected ecosystems. To support such efforts, we develop a methodology to construct a synthetic spatiotemporal gridded dataset of livestock production and processing, human population, and wild birds for the contiguous United States, called a emph{digital similar}. This representation is a result of fusing diverse datasets using statistical and optimization techniques, followed by extensive verification and validation. The livestock component includes farm-level representations of four major livestock types -- cattle, poultry, swine, and sheep -- including further categorization into subtypes such as dairy cows, beef cows, chickens, turkeys, ducks, etc. Weekly abundance data for wild bird species identified in the transmission of avian influenza are included. Gridded distributions of the human population, along with demographic and occupational features, capture the placement of agricultural workers and the general population. We demonstrate how the digital similar can be applied to evaluate spillover risk to dairy cows and poultry from wild bird population, then validate these results using historical H5N1 incidences. The resulting subtype-specific spatiotemporal risk maps identify hotspots of high risk from H5N1 infected wild bird population to dairy cattle and poultry operations, thus guiding surveillance efforts.

Problem

• Modeling interactions between livestock, wild birds, and humans for epidemic spread.
• Creating a synthetic dataset to assess H5N1 spillover risk to livestock.
• Identifying high-risk areas for H5N1 transmission from wild birds to livestock.

Innovation

• Synthetic spatiotemporal gridded dataset creation
• Fusion of diverse datasets using statistical techniques
• Subtype-specific risk maps for H5N1 surveillance