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

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.

• The paper introduces a high-resolution, US-scale digital similar of livestock, wild birds, and human ecosystems for modeling multi-host epidemic spread, such as HPAI.
• This digital similar integrates detailed datasets on livestock distribution, human population, wild bird abundance and movement, and processing center locations using statistical methods.
• Validation against external datasets confirms the model's accuracy in representing populations and distributions, providing a foundational tool for complex epidemiological and ecological studies.

High-Resolution Digital Modeling of Multi-Host Ecosystem Interactions for Epidemic Spread Analysis

This paper introduces a sophisticated digital similar, focusing on a spatiotemporal model of livestock, wild birds, and human populations in the contiguous United States. The digital similar is designed to facilitate multi-host epidemic spread analysis, such as highly pathogenic avian influenza (HPAI). This synthetic dataset, unlike digital twins, offers a statistically similar representation of real data to aid researchers and policymakers in understanding complex interactions at the wild-domestic-human interface.

Methodology and Structure of the Digital Similar

The construction of the digital similar involves an intricate fusion of multiple datasets using advanced statistical and optimization techniques. It integrates four primary components:

1. Livestock Distribution: The model incorporates farm-level data for cattle, poultry, hogs, and sheep, offering further categorizations such as beef and milk cows, chickens, and other poultry. This component models farm sizes and populations at a fine spatial resolution, validated against the AgCensus and GLW datasets.
2. Human Population Data: Gridded human population data includes demographics and occupation-type distributions, crucial for understanding the interaction of agricultural workers and general population dynamics in disease spread.
3. Wild Bird Abundance and Movement: This section utilizes eBird Status and Trends data to model the relative abundance and movement patterns of bird species relevant to HPAI spread. It examines migration patterns to capture potential disease vectors.
4. Processing Centers: Location-specific data for livestock-product processing centers are included, providing insights into potential transmission hotspots and interactions between domestic and wildlife populations.

Verification, Validation, and Data Quality

The authors conducted rigorous validation and verification methods, including:

• Alignment of livestock data with established datasets such as the AgCensus, ensuring minimal discrepancies between modeled and known population totals.
• The distribution of model-generated farm size categories was validated against known CAFO locations, confirming the model's accuracy in representing large, industrial operations.
• Validation of wild bird species abundance was conducted using H5N1 incidence data, demonstrating high correlation with reported cases and bird species distributions across key states.

Implications and Future Directions

This research lays a foundational tool for advanced modeling of multi-host epidemics. By setting up a high-resolution, spatiotemporal framework, it provides an unprecedented platform for simulating disease spread and evaluating risk across interconnected ecosystems. While the dataset includes critical components relevant to avian influenza, its extensibility to other zoonotic diseases or ecological studies underscores its broad applicability.

Future enhancements could include coordinating more precise inter-farm animal movements, finer grain alignment with other environmental data, and integrating machine learning models for prediction and risk assessment. This would propel its utility in One Health initiatives, enabling more comprehensive surveillance and control of emerging infectious diseases.

This study, by presenting a robust and detailed multi-faceted model, significantly contributes to our understanding of complex epidemiological interactions across species and environments, setting the stage for more targeted and effective health interventions.