Evaluating the Viability of Saliva-based Sampling in COVID-19 Diagnostic Tests Compared to Nasopharyngeal (NP) and Oropharyngeal Swabs (OP)

Authors

  • JASMINE LAOUDI Aspiring Scientists' Summer Internship Program Intern
  • Dr. Fatah Kashanchi Aspiring Scientists' Summer Internship Program Mentor
  • Mahla Dehbandi Aspiring Scientists' Summer Internship Program Co-mentor

DOI:

https://doi.org/10.13021/jssr2021.3266

Abstract

School of Systems Biology, College of Science, George Mason University When treating COVID-19, detecting the virus early on and isolating the infected patients play an important role in controlling the outbreak as it has infected almost 200 million people with over 4 million deaths worldwide. In addition, limiting viral exposure to healthcare workers is a major priority. Currently, nasopharyngeal (NP) and oropharyngeal (OP) swabs are utilized the most for diagnostic tests. However, both swabs require the use of an RT-qPCR test, which needs advanced laboratory equipment, limiting the accessibility of tests. The purpose of this study is to determine whether saliva is a viable sample for diagnosing COVID-19 as it allows for quick, accessible self-performed tests. This was done by comparing NP + OP swab samples/other common sampling methods with saliva samples through Point of Care, RT-PCR, and Serological COVID-19 diagnostic tests for detection rate, sensitivity and other parameters. NP swabs and saliva contained similar viral loads as they were positively correlated (r = 0.8029), but saliva samples maintained their viral load for 7 days while the NP swabs did not. Saliva samples also had higher detection rates (92.3% vs. 73.8%) and sensitivity (95.0% vs. 72.2%) than NP + OP swab samples through the RT-qPCR diagnostic test. Further study and research is needed to solidify the accuracy of the use of saliva for testing, but it is still an alternative to the NP+OP swab detection due to its greater sensitivity and higher detection rate.

Published

2022-12-13

Issue

Section

College of Science: School of Systems Biology

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