Answers are grounded in this paper and cite their sources.
Optimization modeling study reveals 51% to 61% cost savings in respiratory disease screening, suggesting that adaptive pooled testing significantly expands diagnostic capacity under resource limits.
Testing Smarter, Not More: An Adaptive Approach In the paper titled “A Novel Adaptive Testing Scheme for Multidisease Testing,” Douglas Bish, Ebru Bish, Merve Bodur, and Berk Görgülü develop an adaptive, cost-efficient testing scheme for lab-based multidisease testing, essential for diagnosis and screening, particularly for disease groups that share symptoms but require disease-specific treatments (e.g., respiratory, gastrointestinal, or sexually transmitted diseases). Their proposed adaptive strategy integrates multiplex assays and pooled testing in a novel way, utilizing modern automated testing technologies to build a customized follow-up test for each person, covering only the diseases flagged in that person’s relevant pooled tests. Optimizing such adaptive designs is mathematically demanding, but in a case study of 18 respiratory diseases, based on data from the Centers for Disease Control and Prevention, their adaptive designs yield substantial cost savings (51%–61%) relative to current practice under forecast error and highly seasonal prevalences. By lowering the cost per subject, adaptive designs can expand testing capacity and support earlier detection under resource constraints.
No takes yet. Share an insight, caveat, or question.
Bish et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: