Bio-economic optimisation of surveillance to confirm broadscale eradications of invasive pests and diseases

Abstract

Although pest eradications from islands have been successful and impart biodiversity benefits, eradications at regional/national scales are more challenging. Such broadscale eradications incur high repeated costs (e.g. control and surveillance effort) because the entire area cannot be treated at one time, and a progressive ‘treat-evaluate-move on’ approach must be employed. We describe a two-stage model to analyse surveillance data for assessing progress and declaring success of broadscale eradications, and to identify optimal cost-efficient surveillance strategies. Stage I modelling coincides or follows population control within a subset area or management zone (MZ). Surveillance data are analysed to quantify the probability of freedom for a treated MZ (i.e. local eradication), which is used to inform an operational decision to reallocate resources to other MZs, and progress across the region. Importantly, freedom declared individually in all MZs is not necessarily equivalent to a high probability of eradication over the broadscale area, because each MZ will have a probability of being erroneously declared free. After a MZ has been operationally declared free, Stage II surveillance commences to detect MZ-level failures, and to estimate the broadscale surveillance sensitivity and a corresponding probability of eradication. We developed a computer algorithm to identify cost-optimal Stage I and II surveillance strategies for a hypothetical large area. We assessed the following: (1) the balance between local surveillance intensity and spatial coverage; (2) the number of years to declare success in Stages I and II; (3) the stopping probability of freedom (Stage I); and (4) the optimal strategy given variation in the starting-over cost, should a MZ be erroneously declared free. This two-stage approach provides an objective basis for decision-making in wildlife pest/disease eradication, and guidance for implementing optimal bio-economic surveillance strategies.