A novel approach to assess the probability of disease eradication from a wild-animal reservoir host

Abstract

Surveying and declaring disease freedom in wildlife is difficult because information on population size and spatial distribution is often inadequate. We describe and demonstrate a novel spatial model of wildlife disease-surveillance data for predicting the probability of freedom of bovine tuberculosis (caused by Mycobacterium bovis) in New Zealand, in which the introduced brushtail possum (Trichosurus vulpecula) is the primary wildlife reservoir. Using parameters governing home-range size, probability of capture, probability of infection and spatial relative risks of infection we employed survey data on reservoir hosts and spillover sentinels to make inference on the probability of eradication. Our analysis revealed high sensitivity of model predictions to parameter values, which demonstrated important differences in the information contained in survey data of host-reservoir and spillover-sentinel species. The modelling can increase cost efficiency by reducing the likelihood of prematurely declaring success due to insufficient control, and avoiding unnecessary costs due to excessive control and monitoring.

Fig. 1.

The estimated median SSe_t was graphed against the proportional change in parameter value (elasticity) to assess the relative sensitivities of parameters. This analysis was done on a simulated square 25-km² landscape with a grid-cell size of 1 ha, and no spatial relative risks were present.

Fig. 2.

Maps of grid-cell sensitivities (SeU_i) across the Blythe Valley study area for randomly generated locations for (a) 200 possum traps, (b) 20 ferrets, and (c) five pigs. The black dots are trap locations and the colour scale from green to off-white represents decreasing SeU_i values.

Fig. 3.

Design and results of sensitivity analysis of varying trap distribution in simulated landscapes with spatial relative risks (RR). This analysis was performed on a square 25-km² landscape with a grid-cell size of 1 ha, a baseline RR of 1, and a varying RR value in habitat X (a). In the example shown here (a), 20% of the possum traps are in habitat X, which makes up 50% of the landscape. The grid cell-level sensitivities range from >0·8 (green) to 0 (off-white). In this analysis we varied the RR values in habitat X (b), proportion of landscape covered by habitat X (c), and the number of traps deployed (d).