Connecting livestock disease dynamics to human learning and biosecurity decisions

Gabriela Bucini; Eric M Clark; Scott C Merrill; Ollin Langle-Chimal; Asim Zia; Christopher Koliba; Nick Cheney; Serge Wiltshire; Luke Trinity; Julia M Smith

doi:10.3389/fvets.2022.1067364

Connecting livestock disease dynamics to human learning and biosecurity decisions

Front Vet Sci. 2023 Jan 20:9:1067364. doi: 10.3389/fvets.2022.1067364. eCollection 2022.

Authors

Gabriela Bucini^{1

2}, Eric M Clark^{1

2}, Scott C Merrill^{1

2}, Ollin Langle-Chimal³, Asim Zia^{2

3

4}, Christopher Koliba^{2

4}, Nick Cheney³, Serge Wiltshire^{2

5}, Luke Trinity^{2

6}, Julia M Smith⁷

Affiliations

¹ Department of Plant and Soil Science, University of Vermont, Burlington, VT, United States.
² Social-Ecological Gaming and Simulation Lab, University of Vermont, Burlington, VT, United States.
³ Department of Computer Science, University of Vermont, Burlington, VT, United States.
⁴ Department of Community Development and Applied Economics, University of Vermont, Burlington, VT, United States.
⁵ Food Systems Research Center, University of Vermont, Burlington, VT, United States.
⁶ Computational Biology Research and Analytics Lab, University of Victoria, Victoria, BC, Canada.
⁷ Department of Animal and Veterinary Sciences, University of Vermont, Burlington, VT, United States.

Abstract

The acceleration of animal disease spread worldwide due to increased animal, feed, and human movement has driven a growing body of epidemiological research as well as a deeper interest in human behavioral studies aimed at understanding their interconnectedness. Biosecurity measures can reduce the risk of infection, but human risk tolerance can hinder biosecurity investments and compliance. Humans may learn from hardship and become more risk averse, but sometimes they instead become more risk tolerant because they forget negative experiences happened in the past or because they come to believe they are immune. We represent the complexity of the hog production system with disease threats, human decision making, and human risk attitude using an agent-based model. Our objective is to explore the role of risk tolerant behaviors and the consequences of delayed biosecurity investments. We set up experiment with Monte Carlo simulations of scenarios designed with different risk tolerance amongst the swine producers and we derive distributions and trends of biosecurity and porcine epidemic diarrhea virus (PEDv) incidence emerging in the system. The output data allowed us to examine interactions between modes of risk tolerance and timings of biosecurity response discussing consequences for disease protection in the production system. The results show that hasty and delayed biosecurity responses or slow shifts toward a biosecure culture do not guarantee control of contamination when the disease has already spread in the system. In an effort to support effective disease prevention, our model results can inform policy making to move toward more resilient and healthy production systems. The modeled dynamics of risk attitude have also the potential to improve communication strategies for nudging and establishing risk averse behaviors thereby equipping the production system in case of foreign disease incursions.

Keywords: agent-based modeling; biosecurity; human behavior (activities); porcine epidemic diarrhea virus (PEDv); risk attitude; swine production systems.

Grants and funding

This work was supported by Agriculture and Food Research Initiative Competitive Grant (Nos. 2015-69004-23273, 2021-67015-35236, and 2022-69014-37041) from the USDA National Institute of Food and Agriculture.