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Project Overview

The wildlife trade industry involves millions of live animals, valued >$300B USD, moving among >180 nations per year. This represents a key pathway for the evolution, emergence, and spread of novel pathogens.  Zoonotic and wildlife pathogens (e.g., SARS-CoV-2 and chytrid fungi, respectively) have cost global economies trillions of dollars, led to substantial human life and biodiversity loss, and been linked to wildlife trade. Managing disease in live animal trade networks presents distinctive challenges. Various socioeconomic factors can influence the decisions businesses make about the species they trade and the biosecurity practices they use, which in turn can influence the prevalence, persistence, and spread of pathogens. Therefore, animal trade networks represent a bidirectionally coupled system between pathogen-host
ecology and decisions made by business, consumer, and
government stakeholders (Fig. 1).

The overarching goal of this project is to identify how socioeconomic decisions made by stakeholders drive pathogen dynamics in a wildlife trade network and use this information to identify disease mitigation strategies that are economically viable and minimize spillover risk (i.e., pathogen transmission from captive to wild populations). This project is partnering with the U.S. wildlife trade industry and government stakeholders, and will facilitate discussions among them to identify strategies that promote clean trade, while considering socioeconomic impacts on the industry. The project uses a combination of socioeconomic surveys, facilitated discussions, pathogen surveillance, and controlled experiments to build a series of predictive models that can be used to guide policy decisions in wildlife trade and prevent the next global pandemic

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Our specific objectives are to:

 

  1. Model Pathogen Dynamics: Construct a fully coupled system of game theory, network, and node-level models to identify and explore bidirectional feedbacks between socioeconomic factors and pathogen dynamics in a wildlife trade network, and determine sets of stakeholder strategies that dampen pathogen amplification and minimize spillover risk.

  2. Characterize the Network: Characterize the composition and topology of the U.S. amphibian pet trade network by estimating trade volume and associations among nodes, and identifying socioeconomic and ecological factors associated with pathogen prevalence and load.   

  3. Quantify Values and Strategies: Quantify values and strategies of stakeholders in the U.S. amphibian pet trade network that drive socioeconomic feedbacks and pathogen dynamics.

  4. Estimate Magnification Potential: Quantify how ecological conditions in network nodes and shipments contribute to pathogen transmission and spread.

 

See more about our EEID project on the NSF award site: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2207922&HistoricalAwards=false

 

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