Accounting for bias in prevalence estimation: The case of a globally emerging pathogen

Accurate quantification of infection parameters is necessary to ensure effective surveillance, investigation and mitigation of infectious diseases. However, hosts and pathogens are often imperfectly observed and key epidemiological parameters, such as infection prevalence, can be biased if this observational uncertainty is not properly accounted for. Here, we evaluated the combined effects of imperfect pathogen detection and host pseudoreplication on the estimation of infection prevalence of the pathogen Batrachochytrium dendrobatidis (Bd) in the southern Darwin's frog (Rhinoderma darwinii). This pathogen causes amphibian chytridiomycosis, a panzootic disease responsible for the greatest documented loss of biodiversity due to an infectious disease. From November 2018 to March 2019, we made 1085 captures of 641 R. darwinii individuals in two areas of Southern Chile. Captured frogs were individually identified to eliminate host pseudoreplication, skin-swabbed twice in sequence, and each swab was analysed in duplicate using a real-time polymerase chain reaction (qPCR) assay to detect Bd. To provide a robust estimate of period prevalence, we used a Bayesian multiscale occupancy model that considers pathogen imperfect detection arising from both sampling and diagnostic testing processes. Finally, using a deterministic matrix population model, we illustrated how the method chosen to estimate prevalence influenced our conclusions regarding the impact of Bd infection on host population trajectories. Our results showed that Bd prevalence could be underestimated by 55% if false negatives and host pseudoreplication were not accounted for. Host pseudoreplication had a greater impact on prevalence underestimation than pathogen imperfect detection in our study. This underestimation in prevalence changed our interpretation of the impacts of Bd infections on our model species, from a nearly stable population using the naïve period prevalence to a declining one using our robust estimate. Synthesis and applications. These results highlight the importance of using robust inferences to inform disease risk assessments and to efficiently allocate limited resources during mitigation strategies of infectious diseases. The methods used here can be applied to a wide range of host–pathogen systems, and will be of interest to both researchers and practitioners aiming to investigate and mitigate the impacts of infectious diseases on free-ranging populations.