A complex system approach to assess the beef cattle industry robustness against biosecurity threats

Abstract

Modern infrastructure systems have become increasingly interconnected, and this includes those in the agricultural industry. Although this interdependence enables the economic functioning of the interdependent system, it also brings vulnerabilities to the system amplifying disease spreading and natural disaster consequences. With this background, this dissertation aims to investigate behaviors of complex systems facing biosecurity challenges from different perspectives.

First, we develop an agent-based model simulating the beef cattle production and related transportation services in southwest Kansas, United States. We evaluate the robustness of the interdependent system by constructing hypothetical disruptions in both the cattle industry and the transportation industry. We observe that the system is robust to random failures but vulnerable to the targeted shutdown of cattle premises or truck premises. In addition, disruptions in the trucks serving packers have the worst impact on cattle production, as compared to other transportation disruptions. In disaster preparations, policymakers need to pay particular attention to one of its critical components, meat packers.

Second, we assess the impact of truck contamination and information sharing on the foot-and-mouth disease transmission. Scenario analyses results show that including indirect contact routes between premises via truck movements can significantly increase the amplitude of disease spread, compared with equivalent scenarios that only consider animal movement. We find that mitigation strategies informed by information sharing can effectively mitigate epidemics, highlighting the benefit of promoting information sharing in the cattle industry.

Third, we examine the impact of human behavior factors on a hypothetical foot-and-mouth disease outbreak. The simulation results indicate that heterogeneity of individuals regarding risk attitudes significantly affects the epidemic dynamics, and human-behavior factors need to be considered for improved epidemic forecasting. With the same initial biosecurity status, the number of infected producer locations and cattle losses can be more effectively reduced with an increase in the percentage of risk-averse producers selecting large producers first compared to randomly selecting producers. In addition, the reduction in epidemic size caused by the shifting of producers’ risk attitudes towards risk-aversion is heavily dependent on the distribution of the initial biosecurity level.

Fourth, we study the robustness of supply chain networks against cascading failures. The simulation results show that the system is relatively robust against load fluctuations but is more fragile to demand shocks. For the underload-driven model without the recovery process, we find the existence of a discontinuous phase transition. Compared to other systems studied under overload cascading failures, this system is more robust for power-law distributions than uniform distributions of the lower bound parameter for the studied scenarios.

Finally, it is critical to prevent the 2019 novel coronavirus disease spread, which has left significant economic consequences to the U.S. beef industry. Several slaughterhouses were forced to stop operations temporarily due to outbreaks identified among meat-processing workers, causing sharp disruptions in beef production. We use network-based modeling to examine the impact of non-pharmaceutical interventions on the coronavirus disease spreading with various scenarios. Although the method is applied to simulate the early stage of epidemic dynamics in Hubei province, China, the model developed can be easily adapted to other regions. The simulation results show that without continued control measures, the epidemic in Hubei Province could have become persistent. Only by continuing to decrease the infection rate through protective measures and social distancing can the actual epidemic trajectory that happened in Hubei Province be reconstructed in simulation.

In summary, we develop several computational models for robustness assessment and epidemic prediction and control in this dissertation. The outcomes are expected to raise awareness of vulnerabilities in the interconnected systems and benefit existing disaster preparedness.