Wildlife diseases have important implications for wildlife and human health, the preservation of biodiversity and the resilience of
ecosystems. However, understanding disease dynamics and the impacts of pathogens in wild populations is challenging because
these complex systems can rarely, if ever, be observed without error. Uncertainty in disease ecology studies is commonly defined
in terms of either heterogeneity in detectability (due to variation in the probability of encountering, capturing, or detecting
individuals in their natural habitat) or uncertainty in disease state assignment (due to misclassification errors or incomplete
information). In reality, however, uncertainty in disease ecology studies extends beyond these components of observation error
and can arise from multiple varied processes, each of which can lead to bias and a lack of precision in parameter estimates. Here,
we present an inventory of the sources of potential uncertainty in studies that attempt to quantify disease-relevant parameters
from wild populations (e.g. prevalence, incidence, transmission rates, force of infection, risk of infection, persistence times, and
disease-induced impacts). We show that uncertainty can arise via processes pertaining to aspects of the disease system, the study
design, the methods used to study the system, and the state of knowledge of the system, and that uncertainties generated via one
process can propagate through to others because of interactions between the numerous biological, methodological and
environmental factors at play. We show that many of these sources of uncertainty may not be immediately apparent to
researchers (for example, unidentified crypticity among vectors, hosts or pathogens, a mismatch between the temporal scale of
sampling and disease dynamics, demographic or social misclassification), and thus have received comparatively little consideration
in the literature to date. Finally, we discuss the type of bias or imprecision introduced by these varied sources of uncertainty and
briefly present appropriate sampling and analytical methods to account for, or minimise, their influence on estimates of disease-
relevant parameters. This review should assist researchers and practitioners to navigate the pitfalls of uncertainty in wildlife
disease ecology studies.