Improving RT-PCR Detection Accuracy for Respiratory Virus Transmission Network (RVTN) Models through Optimized RNA Extraction Protocols under CDC Biosafety Guidelines

Joshua Blessing Animasaun, Onuh Matthew Ijiga, Victoria Bukky Ayoola and Lawrence Anebi Enyejo

International Journal of Scientific Research in Science and Technology, 2025, vol. 12, issue 6, 748-768

Abstract: Accurate reconstruction of Respiratory Virus Transmission Networks (RVTNs) depends heavily on the reliability of RT-PCR results, which are directly influenced by upstream RNA extraction processes. This study evaluates three commonly used extraction methods silica column, magnetic bead–based, and rapid lysis to determine their impact on RT-PCR sensitivity, Ct variability, viral load estimation, and downstream RVTN model stability. Experimental analyses showed that magnetic bead extraction consistently produced the highest RNA yield, lowest Ct variability, and most stable amplification performance, resulting in minimal propagation error within transmission modeling. Silica column extraction demonstrated moderate reliability, while rapid lysis produced substantial Ct variability and high diagnostic uncertainty, significantly weakening network coherence and transmission link detection. By quantifying how extraction-induced errors propagate into epidemiological modeling outputs, the study demonstrates that optimized RNA extraction is critical not only for diagnostic accuracy but also for producing reliable and interpretable transmission networks. These findings underscore the need for standardized extraction practices and improved quality control frameworks to strengthen outbreak response systems and enhance public health decision-making.

Keywords: Improving RT-PCR Detection Accuracy; Respiratory Virus Transmission; Network (RVTN) Models; Optimized RNA Extraction Protocols; CDC Biosafety Guidelines (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v12:y2025:i6:id:1361

DOI: 10.32628/IJSRST25126501

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