Available online 27 September 2022, 100634

Uncertainty in estimated prevalence should account for uncertainty in diagnostic test performance.

Improvements in prevalence inferences can result from more lab validation efforts.

Optimal test allocation between the field and lab can improve study design during a disease outbreak.

Emergence of a novel pathogen drives the urgent need for diagnostic tests that can aid in defining disease prevalence. The limitations associated with rapid development and deployment of these tests result in a dilemma: In efforts to optimize prevalence estimates, would tests be better used in the lab to reduce uncertainty in test characteristics or to increase sample size in field studies? Here, we provide a framework to address this question through a joint Bayesian model that simultaneously analyzes lab validation and field survey data, and we define the impact of test allocation on inferences of sensitivity, specificity, and prevalence. In many scenarios, prevalence estimates can be most improved by apportioning additional effort towards validation rather than to the field. The joint model provides superior estimation of prevalence, sensitivity, and specificity, compared with typical analyses that model lab and field data separately, and it can be used to inform sample allocation when testing is limited.

Bayesian inference

Prevalence

Sample size calculation

Sensitivity

Specificity

© 2022 The Author(s). Published by Elsevier B.V.