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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/3455| Increasing testing throughput and case detection with a pooled-sample Bayesian approach in the context of COVID-19 | |
| Noriega Rodrigo. Samore Matthew H. | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| 10.1101/2020.04.03.024216 | |
| AbstractRapid and widespread implementation of infectious disease surveillance is a critical component in the response to novel health threats. Molecular assays are the preferred method to detect a broad range of pathogens with high sensitivity and specificity. The implementation of molecular assay testing in a rapidly evolving public health emergency can be hindered by resource availability or technical constraints. In the context of the COVID-19 pandemic, the applicability of a pooled-sample testing protocol to screen large populations more rapidly and with limited resources is discussed. A Bayesian inference analysis in which hierarchical testing stages can have different sensitivities is implemented and benchmarked against early COVID-19 testing data. Optimal pool size and increases in throughput and case detection are calculated as a function of disease prevalence. Even for moderate losses in test sensitivity upon pooling, substantial increases in testing throughput and detection efficiency are predicted, suggesting that sample pooling is a viable avenue to circumvent current testing bottlenecks for COVID-19. | |
| www.biorxiv.org | |
| 2020 | |
| Artículo | |
| https://www.biorxiv.org/content/biorxiv/early/2020/04/05/2020.04.03.024216.full.pdf | |
| Inglés | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Artículos científicos |
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|---|---|---|---|
| 1104262.pdf | 1.01 MB | Adobe PDF | Visualizar/Abrir |