SEGS Research: Biosecurity


Food security and economic security are vulnerable to the consequences of new, emerging, or trans-boundary animal diseases reaching the United States and spreading rapidly through food animal populations, triggering export market closures and raising concerns about food safety. Preparing for such a disaster is a complex challenge. Because of human resistance to preparing for something that is not part of one’s individual experience or the collective experience of one’s social network, developing and maintaining protocols and policies effective at preventing the spread of disease is difficult. The USDA NIFA has awarded a Coordinated Agricultural Project grant to a multi-institutional multi-disciplinary team willing to take on this challenge by focusing on its human behavioral dimensions. We use experimental computer games to learn about human decision-making in a simulated farm environment, and then integrate these results into Agent-Based Models of these complex production systems to understand the critical control points and the effects of biosecurity practice adoption. The title of the project is “A human behavioral approach to reducing the impact of livestock pest or disease incursions of socio-economic importance.” We use both “biosecurity” and “animal health protection” to describe our work.

Project Objectives

* Objective 1: Characterize determinants of behavior of stakeholders at critical control points where appli- cation of practices or protocols can prevent (or reduce the impact of) incursions of pests and diseases of cattle, pigs and small ruminants.
* Objective 2. Determine economic attractiveness of solutions that enhance biosecurity.
* Objective 3. Determine most effective communication strategies (including message wording, messenger and media selection).
* Objective 4. Integrate disease characteristics, human risk perception and socio-economic influences on behavior in a simulated “game” environment.
* Objective 5. Develop educational and outreach materials and methods that lead to measurable changes in attitude and behaviors at critical control points in cattle, swine and small ruminant production systems.


  • Willingness to Comply With Biosecurity in Livestock Facilities: Evidence From Experimental Simulations.

    Scott C. Merrill, Susan Moegenburg, Christopher J. Koliba, Asim Zia, Luke Trinity, Eric Clark, Gabriela Bucini, Serge Wiltshire, Timothy Sellnow, Deanna Sellnow and Julia M. Smith

    Frontiers in Veterinary Science., 04 June 2019

    Open PDF

    Disease in U.S. animal livestock industries annually costs over a billion dollars. Adoption and compliance with biosecurity practices is necessary to successfully reduce the risk of disease introduction or spread. Yet, a variety of human behaviors, such as the urge to minimize time costs, may induce non-compliance with biosecurity practices. Utilizing a “serious gaming” approach, we examine how information about infection risk impacts compliance with biosecurity practices. We sought to understand how simulated environments affected compliance behavior with treatments that varied using three factors: (1) the risk of acquiring an infection, (2) the delivery method of the infection risk message (numerical, linguistic and graphical), and (3) the certainty of the infection risk information. Here we show that compliance is influenced by message delivery methodology, with numeric, linguistic, and graphical messages showing increasing efficacy, respectively. Moreover, increased situational uncertainty and increased risk were correlated with increases in compliance behavior. These results provide insight toward developing messages that are more effective and provide tools that will allow managers of livestock facilities and policy makers to nudge behavior toward more disease resilient systems via greater compliance with biosecurity practices.

  • Using an agent-based model to evaluate the effect of producer specialization on the epidemiological resilience of livestock production networks

    Serge Wiltshire

    PLOS ONE., 09 March 2018

    Open PDF

    An agent-based computer model that builds representative regional U.S. hog production networks was developed and employed to assess the potential impact of the ongoing trend towards increased producer specialization upon network-level resilience to catastrophic disease outbreaks. Empirical analyses suggest that the spatial distribution and connectivity patterns of contact networks often predict epidemic spreading dynamics. Our model heuristically generates realistic systems composed of hog producer, feed mill, and slaughter plant agents. Network edges are added during each run as agents exchange livestock and feed. The heuristics governing agents’ contact patterns account for factors including their industry roles, physical proximities, and the age of their livestock. In each run, an infection is introduced, and may spread according to probabilities associated with the various modes of contact. For each of three treatments—defined by one-phase, two-phase, and three-phase production systems—a parameter variation experiment examines the impact of the spatial density of producer agents in the system upon the length and size of disease outbreaks. Resulting data show phase transitions whereby, above some density threshold, systemic outbreaks become possible, echoing findings from percolation theory. Data analysis reveals that multi-phase production systems are vulnerable to catastrophic outbreaks at lower spatial densities, have more abrupt percolation transitions, and are characterized by less-predictable outbreak scales and durations. Key differences in network-level metrics shed light on these results, suggesting that the absence of potentially-bridging producer–producer edges may be largely responsible for the superior disease resilience of single-phase “farrow to finish” production systems..

  • Decision-making in Livestock Biosecurity Practices amidst Environmental and Social Uncertainty: Evidence from an Experimental Game.

    Merrill, S. C., Koliba, C. J., Moegenburg, S., Zia, A., Parker, J., Sellnow, T., Wiltshire, S., Bucini, G., Danehy, C. and Smith, J. M.

    Plos One. Preprint at

    Open PDF

    Livestock industries are vulnerable to disease threats, which can cost billions of dollars and have substantial negative social ramifications. Losses are mitigated through increased use of disease-related biosecurity practices, making increased biosecurity an industry goal. Currently, there is no industry-wide standard for sharing information about disease incidence or on-site biosecurity strategies, resulting in uncertainty regarding disease prevalence and biosecurity strategies employed by industry stakeholders. Using an experimental simulation game, we examined human participant's willingness to invest in biosecurity when confronted with disease outbreak scenarios. We varied the scenarios by changing the information provided about 1) disease incidence and 2) biosecurity strategy or response by production facilities to the threat of disease. Here we show that willingness to invest in biosecurity increases with increased information about disease incidence, but decreases with increased information about biosecurity practices used by nearby facilities. Thus, the type or context of the uncertainty confronting the decision maker may be a major factor influencing behavior. Our findings suggest that policies and practices that encourage greater sharing of disease incidence information should have the greatest benefit for protecting herd health.

  • Net-work Meta-Metrics: Using evolutionary computation to identify effective indicators of epidemiological vulnerability in a livestock production system model.

    Wiltshire, S., A. Zia, C. Koliba, G. Bucini, E. Clark, S. Merrill, J. Smith, and S. Moegenburg

    Journal of Artificial Societies and Social Simulation. March 2019

    Open PDF

    We developed an agent-based susceptible / infective model which simulates disease incursions in the hog pro- duction chain networks of three U.S. states. Agent parameters, contact network data, and epidemiological spread patterns are output after each model run. Key network metrics are then calculated, some of which per- tain to overall network structure, and others to each node’s positionality within the network. We run statistical tests to evaluate the extent to which each network metric predicts epidemiological vulnerability, finding signif- icant correlations in some cases, but no individual metric that serves as a reliable risk indicator. To investigate the complex interactions between network structure and node positionality, we use a genetic programming (GP) algorithm to search for mathematical equations describing combinations of individual metrics — which we call “meta-metrics” — that may better predict vulnerability. We find that the GP solutions — the best of which combine both global and node-level metrics — are far better indicators of disease risk than any individ- ual metric, with meta-metrics explaining up to 91% of the variability in agent vulnerability across all three study areas. We suggest that this methodology could be applied to aid livestock epidemiologists in the targeting of biosecurity interventions, and also that the meta-metric approach may be useful to study a wide range of com- plex network phenomena.

  • The critical role of information sharing to the value proposition of a food systems network

    Christopher Koliba, Serge Wiltshire, Steven Scheinert, Drake Turner, Asim Zia & Erica Campbell

    2016. Public Management Review. Taylor and Francis Online.

    Open PDF

    With goal-directed networks being used so extensively as a strategy to achieve ‘collective impact,’ increased attention is being paid to the investment of participating member organizations’ time, and informational, financial, and human capital in these efforts. Authors draw on the concept of ‘value proposition’ from the business and public administration literature and use extensive network data from a food systems planning network to test hypotheses focusing on the positionality of member organizations within specific operational subnetworks by correlating positionality with multiple assessments of value. Results indicate that embeddedness in the information sharing subnetwork most strongly correlates with member value proposition.