ADSP: An adaptive sample pooling strategy for diagnostic testing

Xuekui Zhang; Xiaolin Huang; Li Xing

doi:10.1016/j.jbi.2023.104501

ADSP: An adaptive sample pooling strategy for diagnostic testing

J Biomed Inform. 2023 Oct:146:104501. doi: 10.1016/j.jbi.2023.104501. Epub 2023 Sep 22.

Authors

Xuekui Zhang¹, Xiaolin Huang², Li Xing³

Affiliations

¹ Department of Mathematics and Statistics, University of Victoria, Victoria, BC, Canada. Electronic address: xuekui@uvic.ca.
² Department of Mathematics and Statistics, University of Victoria, Victoria, BC, Canada.
³ Department of Mathematics and Statistics, University of Saskatchewan, Saskatoon, SK, Canada.

PMID: 37742781
DOI: 10.1016/j.jbi.2023.104501

Abstract

Background: We often must conduct diagnostic tests on a massive volume of samples within a limited time during outbreaks of infectious diseases (e.g., COVID-19,screening) or repeat many times routinely (e.g., regular and massive screening for plant virus infections in farms). These tests aim to obtain the diagnostic result of all samples within a limited time. In such scenarios, the limitation of testing resources and human labor drives the need to pool individual samples and test them together to improve testing efficiency. When a pool is positive, further testing is required to identify the affected individuals; whereas when a pool is negative, we conclude all individuals in the pool are negative. How one splits the samples into pools is a critical factor affecting testing efficiency.

Objective: We aim to find the optimal strategy that adaptively guides users on optimally splitting the sample cohort into test-pools.

Methods: We developed an algorithm that minimizes the expected number of tests needed to obtain the diagnostic results of all samples. Our algorithm dynamically updates the critical information according to the result of the most recent test and calculates the optimal pool size for the next test. We implemented our novel adaptive sample pooling strategy into a web-based application, ADSP (https://ADSP.uvic.ca). ADSP interactively guides users on how many samples to be pooled for the current test, asks users to report the test result back and uses it to update the best strategy on how many samples to be pooled for the next test.

Results: We compared ADSP with other popular pooling methods in simulation studies, and found that ADSP requires fewer tests to diagnose a cohort and is more robust to the inaccurate initial estimate of the test cohort's disease prevalence.

Conclusion: Our web-based application can help researchers decide how to pool their samples for grouped diagnostic tests. It improves test efficiency when grouped tests are conducted.

Keywords: Adaptive strategy; COVID-19; Diagnostic test; Grouped test; Sample pooling.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

COVID-19 Testing
COVID-19* / diagnosis
COVID-19* / epidemiology
Diagnostic Techniques and Procedures*
Humans
Sensitivity and Specificity