Predictive Modeling and Categorizing Likelihoods of Quarantine Pest Introduction of Imported Propagative Commodities from Different Countries

Contaminated consignment simulation to support risk-based inspection design

Invasive nonnative plant pests can cause extensive environmental and economic damage and are very difficult to eradicate once established. Phytosanitary inspections that aim to prevent biological invasions by limiting movement of nonnative plant pests across borders are a critical component of the biosecurity continuum. Inspections can also provide valuable information about when and where plant pests are crossing national boundaries. However, only a limited portion of the massive volume of goods imported daily can be inspected, necessitating a highly targeted, risk-based strategy. Furthermore, since inspections must prioritize detection and efficiency, their outcomes generally cannot be used to make inferences about risk for cargo pathways as a whole. Phytosanitary agencies need better tools for quantifying pests going undetected and designing risk-based inspection strategies appropriate for changing operational conditions. In this research, we present PoPS (Pest or Pathogen Spread) Border, an open-source consignment inspection simulator for measuring inspection outcomes under various cargo contamination scenarios to support recommendations for inspection protocols and estimate pest slippage rates. We used the tool to estimate contamination rates of historical interception data, quantify tradeoffs in effectiveness and workload for inspection strategies, and identify vulnerabilities in sampling protocols as changes in cargo configurations and contamination occur. These use cases demonstrate how this simulation approach permits testing inspection strategies and measuring quantities that would otherwise be impossible in a field-based setting. This work represents the first steps toward a decision support tool for creating dynamic inspection protocols that respond to changes in available resources, workload, and commerce trends.

Early warning systems in biosecurity; translating risk into action in predictive systems for invasive alien species

Invasive alien species (IAS) are one of the most severe threats to biodiversity and are the subject of varying degrees of surveillance activity. Predictive early warning systems (EWS), incorporating automated surveillance of relevant dataflows, warning generation and dissemination to decision makers are a key target for developing effective management around IAS, alongside more conventional early detection and horizon scanning technologies. Sophisticated modelling frameworks including the definition of the ‘risky’ species pool, and pathway analysis at the macro and micro-scale are increasingly available to support decision making and to help prioritise risks from different regions and/or taxa. The main challenges in constructing such frameworks, to be applied to border inspections, are (i) the lack of standardisation and integration of the associated complex digital data environments and (ii) effective integration into the decision making process, ensuring that risk information is disseminated in an actionable way to frontline surveillance staff and other decision makers. To truly achieve early warning in biosecurity requires close collaboration between developers and end-users to ensure that generated warnings are duly considered by decision makers, reflect best practice, scientific understanding and the working environment facing frontline actors. Progress towards this goal will rely on openness and mutual understanding of the role of EWS in IAS risk management, as much as on developments in the underlying technologies for surveillance and modelling procedures.