RT Journal Article
SR Electronic
T1 Increasing testing throughput and case detection with a pooled-sample Bayesian approach in the context of COVID-19
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.04.03.024216
DO 10.1101/2020.04.03.024216
A1 Noriega, Rodrigo
A1 Samore, Matthew H.
YR 2020
UL http://biorxiv.org/content/early/2020/04/05/2020.04.03.024216.abstract
AB Rapid 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.