A model‐based solution for observational errors in laboratory studies

Abstract

Molecular techniques for detecting microorganisms, macroorganisms and infectious agents are susceptible to false-negative and false-positive errors. If left unaddressed, these observational errors may yield misleading inference concerning occurrence, prevalence, sensitivity, specificity and covariate relationships. Occupancy models are widely used to account for false-negative errors and more recently have even been used to address false-positive errors, too. Current modelling options assume false-positive errors only occur in truly negative samples, an assumption that yields biased inference concerning detection because a positive sample could be classified as such not because the target agent was successfully detected, but rather due to a false-positive test result. We present an extension to the occupancy modelling framework that allows false-positive errors in both negative and positive samples, thereby providing unbiased inference concerning occurrence and detection, as well as reliable conclusions about the efficacy of sampling designs, handling protocols and diagnostic tests. We apply the model to simulated data, showing that it recovers known parameters and outperforms other approaches that are commonly used when confronted with observation errors. We then apply the model to an experimental data set on Batrachochytrium dendrobatidis, a pathogenic fungus that is implicated in the global decline or extinction of hundreds of amphibian species. The model-based approach we present is not only useful for obtaining reliable inference when data are contaminated with observational errors, but also eliminates the need for establishing arbitrary thresholds or decision rules that have hidden and unintended consequences.

Fishing for mammals: Landscape‐level monitoring of terrestrial and semi‐aquatic communities using eDNA from riverine systems

“…Lugg et al, ). For metabarcoding, there is clearly a need to carefully consider the potential for cross contamination between samples and how false positives (and negatives) could impact detection probabilities using occupancy modelling with eDNA data (Brost, Mosher, & Davenport, ; Lahoz‐Monfort, Guillera‐Arroita, & Tingley, ). Among the recommendations made by Lahoz‐Monfort et al () to account for these uncertainties, one was the simultaneous collection of data from more conventional surveying methods.…”

“…Here, we have demonstrated general congruence between surveying methods for the water vole (Table ; Figure ) and using certain species to apply a multiple detection methods model would be appropriate in further studies (Lahoz‐Monfort et al, ). Alternatively, using repeated sampling and known negative controls in occupancy models that fully incorporate false‐positive errors could be applied in the absence of other surveying data (Brost et al, ). Overall, multi‐species metabarcoding studies may trade‐off a slightly lower (but comparable) detection probability than other survey methods for individual species (Figure ) in favour of a better overall ‘snapshot’ of occupancy of the whole mammalian community (Ushio et al, ).…”

Infectious Dose of African Swine Fever Virus When Consumed Naturally in Liquid or Feed

“…We categorized ASFV infection as positive if > 1 diagnostic test indicated evidence of infection. We analyzed all binary responses simultaneously to account for imperfect test agreement ( 42 – 44 ).…”

Successful molecular detection studies require clear communication among diverse research partners

“…), and these have been used to analyze molecular detection data from pathogen samples (Brost et al . ), eDNA samples (Lahoz‐Monfort et al . ), and scat (Balestrieri et al .…”