At-border interventions are a critical step along the biosecurity continuum, to measure and control the risks associated with the cross-border movement of people and goods. Air passengers are a high-volume pathway for a range of biosecurity risk materials, against which various interventions may be used (e.g., manual searches, detector dogs, x-rays, etc.). Tasmania is an island state, and its environment and industries have benefited from a low level of invasive pests due to their geographic isolation. Therefore, relatively strict at-border interventions are used to prevent the entry of new pests, including some serious invasive pests already present on mainland Australia (e.g., Queensland (Bactrocera tryoni) and Mediterranean (Ceratitis capitata) fruit fly). Using a large interception database for domestic air passengers entering the southern Australian state of Tasmania from mainland Australia, this study applies common statistical modelling tools to assess the efficacy of interventions (namely, dog detectors, and bag searches), and to identify pathway risk factors (e.g., flight origin/route). This analysis considered the effects of interventions on both voluntary declarations by passengers and also detections of undeclared risk material on passengers. The analysis also focused on biosecurity risk items generally (e.g., fruits and vegetables, meat products, cut flowers), and items that are specifically considered to be fruit-fly hosts. The results highlight that active at-border interventions and the presence of biosecurity inspectors capture a significant volume of biosecurity risk items at the border, and detector dogs have particularly strong positive effects on the rate of interceptions, particularly for items detected on passengers. Conducting bag searches also appears to increase interceptions, both by increasing the rate of items being detected and by encouraging voluntary declarations. Sensitivity analyses then test the robustness of results to modelling implementation methods and distributional assumptions. This study demonstrates how statistical modelling can provide robust insights into biosecurity interventions and risk factors along pathways, and further highlights the value of high-quality interception data resources for informing and improving biosecurity systems.
