Raw Giardia detection dataset.

BackgroundGut-parasite transmission often involves faecal shedding, and detecting parasites in stool samples remains the cornerstone of diagnosis. However, not all samples drawn from infected hosts contain parasites (because of intermittent shedding), and no test can detect the target parasites in 100% of parasite-bearing samples (because of imperfect sensitivity). Disentangling the effects of intermittent shedding and imperfect sensitivity on pathogen detection would help us better understand transmission dynamics, disease epidemiology, and diagnostic-test performance. Using paediatric Giardia infections as a case-study, here we illustrate a hierarchical-modelling approach to separately estimating the probabilities of host-level infection (); stool-sample–level shedding, given infection (); and test-level detection, given infection and shedding ().Methods/FindingsWe collected 1–3 stool samples, in consecutive weeks, from 276 children. Samples (413 overall) were independently examined, via standard sedimentation/optical microscopy, by a senior parasitologist and a junior, trained student (826 tests overall). Using replicate test results and multilevel hierarchical models, we estimated per-sample Giardia shedding probability at and observer-specific test sensitivities at and . Gender-specific infection-frequency estimates were and . Had we used a (hypothetical) Perfect Test with 100% narrow-sense sensitivity (), the average probability of detecting Giardia in a sample drawn from an infected child () would have been . Because no test can be >100% sensitive, (which measures clinical sensitivity) can only be brought above ~ 0.44 by tinkering with the availability of Giardia in stool samples (i.e., ); for example, drawing-and-pooling 3 replicate samples would yield .ConclusionsBy allowing separate estimation (and modelling) of pathogen-shedding probabilities, the approach we illustrate provides a means to study pathogen transmission cycles and dynamics in unprecedented detail. Separate estimation (and modelling) of true test sensitivity, moreover, may cast new light on the performance of diagnostic tests and procedures, whether novel or routine-practice.