Journal of Vaccines & Vaccination

Abstract

The Coviral Portal: Multi-Cohort Viral Loads and Antigen-Test Virtual Trials for COVID-19

Alexandra Morgan, Elisa Contreras, Michie Yasuda, Sanjucta Dutta, Donald Hamel, Tarini Shankar, Diane Balallo, Stefan Riedel, James E. Kirby, Phyllis J Kanki and Ramy Arnaout*

Objective: Regulatory approval of new over-the-counter tests for infectious agents such as SARS-CoV-2 has historically required that clinical trials include diverse groups of specific patient populations, making the approval process slow and expensive. Showing that populations do not differ in their viral loads the key factor determining test performance could expedite the evaluation of new tests.

Materials and methods: We annotated 46,726 RT-qPCR-positive SARS-CoV-2 viral loads with demographics and health status, evaluated the performance of two commercially available antigen tests over a wide range of viral loads, and created an open-access web portal allowing comparisons of viral-load distributions across patient groups and application of antigen-test performance characteristics.

Results: In several cases distributions were surprisingly similar where a difference was expected (e.g. smokers vs. non- smokers); in other cases there was a difference opposite from expectations (e.g. higher in patients who identified as White vs. Black). Predicted sensitivity and specificity of antigen tests for detecting contagiousness were similar across most groups.

Discussion: Rich clinical annotations reveal patient-subgroup-specific similarities and differences that are fertile ground for future research. Making viral loads freely and easily available for patient groups required significant attention to avoid potential loopholes that might risk patient privacy via identifiability. Two-parameter flexibility enables customized prediction of antigen-test results.

Conclusion: In silico analyses of large-scale, real-world clinical data repositories can serve as a timely evidence-based proxy for dedicated trials of antigen tests for specific populations. Free availability of richly annotated data facilitates large-scale hypothesis generation and testing.

Published Date: 2024-02-15; Received Date: 2024-01-15