1. Reliable assessments of infection status and population prevalence are critical for epidemiological modelling and disease management, but can be greatly biased when disease state is determined from imperfect diagnostic tests. Available statistical methods to adjust test-based prevalence estimates by correcting for test accuracy demand that many stringent requirements and assumptions be met (knowledge about underlying population prevalence or multiple diagnostic methods), limiting their utility for wildlife disease surveys.
2. In this paper, we present site-occupancy modelling as a flexible approach to derive estimates of population prevalence and test sensitivity under imperfect pathogen detection without a need for restrictive requirements or assumptions. We extend the utility of the standard site-occupancy framework for pathogen detection data by novel application of abundance-induced heterogeneity (AIH) models (Royle & Nichols 2003) that allow test sensitivity to vary with host pathogen load or infection intensity.
3. We demonstrate the utility of this approach for wildlife disease studies by applying site-occupancy models to a data set consisting of replicate quantitative (q)PCR diagnoses of malaria parasites (Plasmodium spp.) in blood samples from wild blue tits (Cyanistes caeruleus).
4. Model selection revealed that Plasmodium detection rates by qPCR were strongly dependent on host parasite load. Estimates of parasite detection rates revealed the qPCR assay to be highly sensitive, with accordingly, a very low probability of false negative diagnosis for the majority of infected hosts in our population and little bias in naive estimates of population prevalence, although this will be a system-specific result.
5. Our results demonstrate the utility of a site-occupancy approach for deriving estimates of population prevalence under imperfect pathogen detection and reveal that accounting for host variation in pathogen load allows a more accurate assessment of diagnostic test sensitivity.
6. By identifying factors that influence pathogen detection rates, and revealing optimal protocols for obtaining unbiased prevalence estimates, while minimising the probability of false negative diagnoses, we also show that this approach can enhance both diagnostic accuracy and cost-efficiency in wildlife disease surveys.