Disease dynamics across complex agricultural networks

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Outbreaks in economically valuable animal populations have important social, environmental and economic costs. Horses are considered livestock by both provincial and federal agriculture agencies in Canada and the equine industry contributes over 19 billion dollars annually to the Canadian economy. Emergency management and preparedness in the face of outbreaks in agricultural animal populations (including horses) is of significant importance. Yet, the equine industry has lagged behind other agricultural commodity groups in this area. The movement of horses creates opportunities for the introduction and spread of infectious diseases and the equine industry has adopted a number of biosecurity recommendations. However, these recommendations are based on limited empirical data. This critical information gap hinders our ability to perform thorough risk assessments of the epidemic potential that exists, and inform evidence-based biosecurity policies. History has demonstrated that events that bring horses together from different locations have the potential to propagate disease spread events. The systematic application of network science and epidemiological modeling as tools for informing decision-making under uncertainty is a useful approach for the management of infectious disease risks in the equine population. The over-arching goal of this program of research is to examine the hypothesis that equine network structure and human behaviour interact to influence the success of equine biosecurity. Modeling disease transmission across a network presents a number of theoretical and applied challenges that warrant further study. Specifically we will, 1) collect empirical network data, 2) incorporate network data into epidemic models, and 3) consider disease-behaviour interactions. This proposal represents an important step forward for understanding disease dynamics as they evolve across complex networks. The properties of network epidemic models are often counterintuitive and small changes can have a dramatic impact on the model outcomes. In these cases, intuition can be a very poor guide for decision-making. Collecting data to better support our understanding of how network structure and human behaviour interact to influence opportunities for pathogens to be spread at equine co-mingling sites represents an important next step for veterinary research in Canada. The proposed research represents an important step to build upon the current biosecurity standards documents for the Equine Sector, develop evidence-based recommendations that will support the Canadian equine industry in preparing emergency preparedness plans, and allow more timely emergency response within the Canadian agricultural sector.