|
![]()
| Testing Interest1 #701021 Effective diagnostic testing serves at least four purposes: (i) diagnosis, (ii) surveillance, (iii) outbreak mitigation/control and (iv) screening (e.g., for access to long-term care (LTC) homes, etc.). | - COVID-19 has shown that it is critical to maximize testing capacity and to ensure that resources are optimally allocated across these four objectives. These have been challenges throughout the COVID-19 pandemic. Laying out the framework for testing capacity and allocation will build preparedness for emerging infectious diseases and potential pandemics, and may inform testing allocation for endemic infections in Canada.
|
+Citavimą (1) - CitavimąPridėti citatąList by: CiterankMapLink[1] ADSP: An adaptive sample pooling strategy for diagnostic testing
Cituoja: Xuekui Zhang, Xiaolin Huang, Li Xing
Publication date: 23 September 2023
Publication info: Journal of Biomedical Informatics, Volume 146, 2023, 104501, ISSN 1532-0464
Cituojamas: David Price 4:08 PM 11 December 2023 GMT
Citerank: (3) 685355Xuekui ZhangDr. Xuekui Zhang (PhD) is an Assistant Professor at University of Victoria, a Canada Research Chair (Tier II) in Bioinformatics and Biostatistics (2017-2027), and a Michael Smith Health Research BC Scholar (2022-2027).10019D3ABAB, 701020CANMOD – PublicationsPublications by CANMOD Members144B5ACA0, 715831Diagnostic testing859FDEF6
URL:
DOI: https://doi.org/10.1016/j.jbi.2023.104501
| | Ištrauka - [Journal of Biomedical Informatics, 28 September 2023]
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. |
|
|